Authors - Shailesh Gahane, Payal Khode, Arya Kapse, Deepak Sharma, Pankajkumar Anawade Abstract - The global health care is on the threshold of a revolutionary transformation, and the artificial intelligence technology stands at the forefront of this change. This research paper deals with the complex process involved in conceptualizing, designing, developing, and enforcing an AI-pushed health-care system. With the strength of machine learning and deep learning technologies, it can analyze vast ranges of healthcare data, which incorporates digital fitness records, clinical imaging, among others. It begins with reviewing multidimensional literature pertinent to the research study. Through this research, a critical part entails a pilot observe designed in a very deliberate manner in order to conservatively test the effectiveness and reliability of AI algorithms in various healthcare fields. Based on this research, it is predicted that several advantages are bound to be realized among them; more accurate diagnosis, individually tailored treatment plans, optimized effective care resources deployment, and lower healthcare expenses. By making known my findings and insights, I hope to provide helpful guidance and recommendations for health care professionals, policymakers, and developers of technology, which would eventually enrich or enhance the discourse regarding AI integration in health care.
Authors - Ankit Shah, Hardik M. Patel Abstract - Generative Adversarial Networks (GANs) have revolutionized data augmentation by generating realistic and diverse synthetic data, significantly enhancing the performance of machine learning models. This review evaluates the efficacy of GAN-based augmentation compared to traditional methods across various datasets, including MNIST, CIFAR-10, and diabetic retinopathy images. Using architectures such as DCGAN, WGAN-GP, and StyleGAN, our experiments showed substantial performance improvements: CNN accuracy on CIFAR-10 increased from 82.0% to 87.5%, and ResNet-50 accuracy on diabetic retinopathy images rose from 75.0% to 87.0%. Statistical analyses confirmed the significance of these gains. Despite challenges like computational costs and training instability, GAN-based augmentation proves superior in addressing data scarcity and enhancing model robustness. Future research should focus on optimizing GAN training, integrating hybrid models, and exploring ethical considerations. The results underscore GANs' potential in advancing machine learning applications, particularly in complex and data-scarce domains.
Authors - Manoj N, M Thanmay Ram, Manikanta S, Tarun Pradeep, Ramandeep Kaur Abstract - As IoT devices multiply in smart cities, safeguarding healthcare data's confidentiality, security, and integrity from various sources is getting harder. In order to protect healthcare data and facilitate effective machine learning, this article suggests a secure structure that combines Blockchain technology with Federated Learning (FL). With its immutable ledger, blockchain guarantees data confidentiality and openness throughout the network, whereas FL lets data stay on local devices, protecting privacy while training models. The suggested framework is ideal for smart city applications since it places a strong emphasis on safe data sharing, privacy protection, and dependable model management. The design tackles important problems like data breaches, illegal access, and confidence in model updates by utilizing FL's decentralized training and Blockchain's tamper-proof data management. This combination promotes openness and confidence among stakeholders while strengthening the security of healthcare data. The suggested method, which is intended for smart cities, opens the door for creative and privacy-compliant approaches to healthcare data administration and analysis by facilitating efficient collaboration across healthcare organizations without compromising patient privacy.
Authors - Sabina Sehajpal, Ravneet Kaur, Ajay Singh, Mukul Bhatnagar Abstract - This research elucidates the transformative potential of big data analytics and artificial intelligence in optimising health insurance claims and risk assessment by employing an empirically robust framework encompassing reliability and validity metrics, Heterotrait-Monotrait Ratio (HTMT) analysis, and bootstrapping to unravel the intricate interdependencies among constructs such as AI model accuracy, claims processing efficiency, cost efficiency, data quality, fraud detection accuracy, system usability, and user trust interface, thereby advancing a comprehensive understanding of the systemic synergies that enhance predictive precision, operational scalability, and equitable resource allocation within the healthcare financing paradigm.
Authors - Prajakta Deshpande, Divya Kasat, Shrushti Mahadik, Rutika Ubalekar Abstract - Mantras in the Sanskrit language are the soul of Indian culture, carrying deep spiritual, emotional, and cultural implications. These ancient chants, more than words, resonate profoundly and are used in meditation, healing, and divine invocation. Each Sanskrit mantra reveals emotional connotations through its meaning and sound. We have classified them into three groups: Vidur Niti, representing clarity and wisdom; Chanakya Niti, embodying planning and decisive action; and Sanskrit Shlokas, symbolizing harmony and unity. In pioneering work, cutting-edge transformer models, such as XLNet, and the Hugging Face framework are adapted to build an advanced text classification system that decodes the emotional essence of sacred mantras. A hand-curated dataset of annotated Sanskrit mantras has its performance evaluated in terms of accuracy and F1-score on emotional polarity. This kind of research bridges ancient wisdom with modern technology to uncover the revitalization of sacred traditions through computational linguistics for this very modern world.
Authors - Panchal Twinkle Shaileshbhai, Pushpal Desai Abstract - Shadow rendering plays a crucial role in enhancing the realism and immersion of Augmented Reality (AR) applications by seamlessly integrating virtual objects into real-world environments. Dynamic daylight conditions, characterized by varying sunlight intensity, direction, and ambient light, present significant challenges to achieving visually coherent and computationally efficient shadow rendering. This study offers a comparative analysis of diverse shadow rendering mechanisms, evaluating their effectiveness, performance, and suitability for AR applications under fluctuating lighting conditions. Techniques such as Light Direction Approximation, Shadow Mapping, Projected Planar Shadows, and Real-Time Ray Tracing, Dynamic Shadow Blending, Real-Time Sun Position and Shadow Adjustment, Hybrid Shadow Techniques, Brightness Induction and Shadow Inducers and Shadow Perception in AR are examined, highlighting their strengths, limitations, and application scenarios. The research also addresses factors influencing shadow intensity and alignment, providing insights into optimizing realism and computational efficiency in outdoor AR environments. By exploring innovative solution and proposing guidelines for shadow rendering mechanism, this study contributes to advancing AR technology, ensuring enhanced visual fidelity and user experience across dynamic settings.
Authors - T. A. Alka, M. Suresh, Aswathy Sreenivasan Abstract - This study aims to explore entrepreneurship trends in computer science through Bibliometric analysis. 5530 documents from the Scopus are selected based on inclusion and exclusion criteria in the initially selected documents. The Biblioshiny package under R programming is used for the analysis. The major findings are; entrepreneurship has wider applications in various domains. It is not a single-domain phenomenon. The trend topics and word cloud show the most trends in entrepreneurship in computer science including learning models, artificial intelligence, games, innovation, entrepreneurship education, digital transformation, computer simulation, etc. The limitations of the study are; papers from the Scopus database are only considered. Documents other than in English, and papers from other domains except computer science are ignored. This literature study lacks the benefits of primary data research. The inherent limitations of the bibliometric methodology will affect the results. The findings of this research provide knowledge on various aspects to the policymakers, practitioners, researchers, and academicians to foster an entrepreneurship ecosystem and understand the trends of entrepreneurship in the computer science domain. The novelty of the study is underlying the comprehensive review of the existing body of knowledge to draw future research directions. The main highlight of this literature review paper is that complete in-depth knowledge of the data is possible through bibliometric analysis.
Authors - Sathiyapriya K, S Bharath, Rohith Sundharamurthy, Prithivi Raaj K, Rakesh Kumar S, Rakkul Pravesh M, N Arun Eshwer Abstract - The convenience and security offered by voice-based authentication systems results in its increasing use in various sectors such as banking, e-commerce, telecommunications, etc. But these systems are open to vulnerabilities from voice spoofing attacks, including replay synthesis and voice conversion. The following work makes use of Mel-Frequency Cepstral Coefficients (MFCC), Constant-Q Transform (CQT), and a deep learning model Res2Net and creates a framework that can classify genuine and spoofed voices. MFCC and CQT are commonly used for feature extraction, and the Res2Net model classifies the audio. The system was evaluated against the ASVspoof 2021 dataset, the reason being that it has a diverse collection of audio samples (almost 180,000) samples, and also it is recognized by the research community. Our system recorded a low Equal Error Rate (EER) of 0.0332 and a Tandem Detection Cost Function (t-DCF) of 0.2246. This framework contributes to the advancement of secure voice authentication systems, addressing critical challenges in modern cybersecurity.
Authors - Martin Mollay, Deepak Sharma, Pankajkumar Anawade, Chetan Parlikar Abstract - This study examines how AI affects consumer choices in smart homes. This research determines how AI-supported technologies such as voice-controlled digital assistants, dynamic pricing models, and personalized recommendations significantly affect consumer tastes, behaviors, and purchasing decisions through the use of secondary data sources. Customers’ interactions with goods and services are personal and pragmatic as artificial intelligence is progressively included in smart homes. The study claims, however, that artificial intelligence has a two-edged effect on consumer decision-making. Two such areas where AI can enhance customer experience by improving interactions and decision-making processes are through personalization and optimization. This, however, gives rise to some critical ethical issues concerning algorithmic bias privacy and data security. As technology matures, it is essential to promote responsible AI practices, given its increasing ubiquity in daily life. According to the study findings, for instance, organizations must overcome these challenges if they are to preserve customer trust and ensure that artificial intelligence (AI) will ultimately enhance customer relationships. Reading through many kinds of research, company reports, and scholarly works on AI applications in consumer decision-making gives one a view of its current and potential future applications. Results underline that ethics matter when designing transparent AI systems in order to enhance customer loyalty and trust.
Authors - Payal Khode, Shailesh Gahane, Arya Kapse, Pankajkumar Anawade, Deepak Sharma Abstract - The proposed identity card processing system revolutionizes the traditional, manual, and semi-automated ID card creation processes by integrating advanced web technologies and artificial intelligence (AI). Designed for efficiency and user-friendliness, this system employs React JS and JavaScript for seamless operation, enabling students to input required details and generate a printable ID card within 15 minutes. This contrasts significantly with the time-consuming manual design methods using applications like CorelDRAW or Photoshop. Incorporating AI-driven features such as customizable designs and face detection technology ensures quick and accurate retrieval of student data from the school database. The system emphasizes real-time data processing, cross-platform accessibility, and a secure, intuitive interface, allowing users and administrators to handle ID card requests efficiently from any internet-enabled device. By addressing the limitations of existing methods, this automated solution ensures flexibility, reliability, and enhanced usability, making ID card issuance streamlined and error-free. The final system aligns with modern technical and operational requirements, delivering robust functionality and improved organizational efficiency.
Authors - Mumthaz Beegum M, Raseena Beevi, Aji S Abstract - Short-text similarity is a vital research area in NLP with significant implications for use cases like search recommendations and question-answer systems. Traditional models often focus solely on semantic similarity, overlooking syntactic factors. Our approach uses the AnglE (Angle Embedding) method for semantic similarity, which transforms text into high-dimensional vectors to capture the nuanced meanings and relationships between words and phrases. The cosine similarity measure is then employed to calculate the similarity score from these vectors. We apply the weighted Tree Edit Distance (TED) method for syntactic similarity, which measures structural differences between parse trees by calculating the minimum cost required to convert one tree into another through a series of edit operations. By integrating these two complementary similarity measures, our approach aims to deliver a more thorough and accurate evaluation of text similarity. This methodology introduces an advanced technique that combines semantic and structural information to enhance the assessment of short-text similarity. The integrated methodology introduces a sophisticated framework that not only enhances the precision of similarity evaluations but also bridges the gap between semantic and syntactic analyses, thereby offering a more comprehensive evaluation of text similarity.
Authors - Suhail Manzoor, Rahul Gupta, Prakhar Sharma, Yash Mittal, Mohammad Arshad Iqbal Abstract - Plants are mainly suffering from abiotic stress such as drought, salinity, and widely temperature decrease or increase. Thanks to notable advancements in machine learning and hyperspectral imaging, detecting stress in plants has never been easier. For that matter, different machine learning techniques such as Random Forest, Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Kernel Ridge Regression have been used. Hyperspectral image has been widely used in classifying crop water stress by classifiers such as Random Forest and SVM. CNNs have been widely used for plant phenotyping under multiple stresses thanks to their good prediction results, but that entails many computation problems. Other methods, such as Kernel Ridge Regression and Extreme Gradient Boosting have emerged to target specific stress indicators like leaf reflectance spectra at key wavelengths; however, these typically rely on specialized equipment and significant data preprocessing. Here, we synthesize these various approaches to plant stress detection and present an integrated approach for abiotic stress recognition in plants and advocate for models with high generalizability across different environmental conditions or types of biotic stresses.
Authors - Shriya Vadavalli, Geethika Bodagala, T Sridevi Abstract - This paper conducts a systematic literature review about the current status of the art in the detection and diagnosis of autoimmune skin diseases. This paper identifies recent studies and advancements in terms of key technologies, methodologies, and approaches used in this domain specifically with regard to imaging and deep learning techniques. Therefore, the work underlines scopes for further studies in terms of improving diagnostics accuracy and increasing robustness and accessibility for diagnostic solutions. This piece of work also puts a hole in the existing literature, and research gaps on machine learning algorithms and image processing have highlighted the enhancement of the precision as well as effectiveness of such detection systems with respect to skin diseases. This review is in a position to provide grounds for future research directions and innovations in skin disease diagnostics.
Authors - Ch. G.M.A.S. Teja, V. Manmohan, Ch. Srinith, N. Shiva Kumar, Vempaty Prashanthi, R Govardhan reddy Abstract - This review paper focuses on the emerging potential of FKP recognition as a strong modality for identity verification and authentication. Traditional biometric methods are fingerprint and iris recognition methods, which have been adopted more than others because they can be accurate and reliable, but these techniques also bear limitations, such as problem with false negatives, costly equipment, and vulnerability to data breaches. In For the response to these challenges, FKP offers a new approach in that the ability to search for unique patterns of knuckle skin is the stable and more non-invasive indicator. Unlike, which can deteriorate and is vulnerable to influences over time, FKP has little change from outer conditions and, therefore, is an attractive solution for secure and contactless authentication. This review synthesizes the most recent research and technical advancements in the recognition of FKP. Presenting the benefit of how bringing FKP within the multimodal system compiles diverse strengths of various biometric techniques under one framework and provides the enhancement of a holistic method toward security. The discussion continues with research gaps among the existing literature and ends with a call for further investigation. All the above said, the review given is going to validate the potentiality of FKP becoming a feasible, scalable surrogate to the traditional biometric schemes in various datasets, real word applications.
Authors - Kiruthika R, Gunavathi N Abstract - This study focuses on a conventional inset-fed rectangular patch antenna to investigate various substrate materials for terahertz (THz) frequency applications. The performance of different substrates is evaluated based on their electrical parameters. The operating frequency range by IEEE standards falls within the THz band, specifically from 0.1 to 3, with a center frequency of 1.5 THz. Key performance metrics such as return loss (in dB), bandwidth, gain, directivity, and efficiency are assessed, along with bending tolerance to evaluate mechanical stability. Teflon demonstrates superior radiation characteristics, achieving high gain and directivity values obtained through high-frequency structural simulator (HFSS) and computer simulation technology (CST). Additionally, based on statistical analysis, Arlon Diclad 880 provides better mechanical stability than other substrate materials. The equivalent circuit model is analyzed using advanced design system (ADS) software.
Authors - Agha Imran Husain, Sachin Lakra Abstract - In this paper, we proposed a novel swarm-based algorithm called the Flying Duck formation Energy Optimization Algorithm (FDEOA) for selecting cluster heads in Wireless Sensor Networks (WSNs). The FDEOA method tries to minimize energy usage in WSNs by lowering the number of messages delivered by the sensor nodes. It is motivated by the formation of a flock of flying ducks. The best sensor node for the cluster head is chosen by the algorithm using a multi-objective fitness function that takes into account both energy usage and network connection. In terms of network longevity, energy usage, and the number of dead nodes, the FDEOA method is contrasted with other well-known clustering algorithms, including LEACH and SEP. Simulation findings reveal that the FDEOA algorithm beats the existing methods in terms of network lifetime and energy usage while retaining a high level of network connectedness. The suggested approach can be used on low-power sensor nodes and is computationally effective. The FDEOA algorithm has the potential to be used in a variety of WSN applications where network longevity and energy usage are important factors.
Authors - K. L. Sudha, Kavita Guddad Abstract - NavIC (Navigation with Indian Constellation) is a satellite system consisting of seven satellites orbiting the Earth in GEO and GSO orbits. This satellite constellation offers Standard Positioning Service (SPS) for common public use and Restricted Service (RS) for approved users, using two frequencies: the L5 band and the S band, with the CDMA technique. This paper examines the suitability of three binary sequences — Gold, Weil, and Weil-like Sidelnikov-Lempel-Cohn-Eastman (WSLCE) sequences — as PRN codes for the primary in phase and quadrature-phase codes of the L5 band of IRNSS. It describes the generation of these sequences and compares them based on their even auto-correlation and cross-correlation values. The randomness of these sequences is evaluated using the NIST (National Institute of Standards and Technology) statistical test suite. A comparison of the three binary PRN sequences, each 10230 bits in length for the L5 frequency band, reveals that WSLCE sequences exhibit greater randomness compared to the other two sequences
Authors - Anupama Nayak, Shikha, Jahanvi Ojha, Kavita Sharma, S.R.N Reddy Abstract - Living in a world where science is advanced, the presence of technology can be seen in even the smallest of the appliances we use and it has become a crucial part of our lives. Technologies like the Internet of Things (IoT) along with modern wireless systems have led to the creation of smart appliances which have made the lives of people much simpler and more comfortable. Many automation devices in our homes are accessible remotely via a mobile application. This paper focuses on the design and development of an android mobile application, and its connectivity to a cloud database, which is also accessed by the hardware. Using Android Studio, Firebase cloud database, and Raspberry Pi, we successfully developed an android application that controls hardware remotely.
Authors - Vaishnavi Moorthy, Rupen, Dhruv Chopra, Anamika Jain Abstract - The issue of security is paramount in any organization. Since the advancement of technology and introduction of Generative AI such as ChatGPT, security concerns have skyrocketed for everyone alike. With the availability of these Generative AI platforms to virtually anyone with internet access the threat of security is bigger than ever before as these platforms can be used for malicious intents by a large number of people. Limited research has been performed by third party researchers on Generative AI as it is a relatively new technology. We intend to perform relative research in identifying potential vulnerabilities in Generative AI models, LLMs etc. The aim of this research is to document various ways cybersecurity can be used in GenAI with the intention of both securing assets and protection against malicious activity. The project also delves into potential applications of GenAI in helping identify, prevent and test various security infrastructure. Some potential threats and uses studied under this project include real time access management, phishing detection, jailbreak of ChatGPT. The mentioned use cases provide us with a wide picture of uses of these LLMs in the world of cybersecurity. The deployment of Generative AI systems introduces significant cybersecurity challenges, necessitating the need of safeguards for monitoring against threats and vulnerabilities. This project aims at performing the necessary research to identify such situations from affecting normal operations of an organization and to spread awareness regarding the use of Generative AI in cybersecurity.
Authors - Aatm Prakash Rai, Puneet Kumar Gupta, Santanu Roy Abstract - The proliferation of Generative Artificial Intelligence Chatbots, also known as Gen AI chatbots, are nowadays in the growth phase of integration with information systems for effective teaching and learning. The learning experience has been enhanced with the arrival of Gen AI tools such as machine learning and natural language processing. Gen AI Chatbots like ChatGPT, Copilot, etc. can be considered as computer programs that can trigger human-like interactions to aid investigating, developing and transferring knowledge. The objective of this research work is to scan the previous scholarly research works on Gen AI chatbots adoption by relying on bibliometric analysis. The study intends to contribute by identifying research trends and insights towards adoption of Gen AI chatbots in higher education sector in the Indian context. The outcome of the analysis highlights prospective research opportunity in Gen AI chatbots due to the evolution of the large language models and machine learning. This emerging technology may alter the course of future research in the higher education sector.
Authors - Amani A. Aladeemy, Sachin N. Deshmukh Abstract - Sentiment analysis (SA) discerns the subjective tone within text, categorising it as positive, neutral, or negative. Arabic Sentiment Analysis (ASA) has distinct obstacles owing to the language's intricate morphology, many dialects, and elaborate linguistic frameworks. This study compares SA models for Arabic text across multiple datasets, evaluating traditional machine learning (ML) algorithms, such as Random Forest (RF) and Support Vector Machine (SVM); deep learning (DL) models, including Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU); and transformer-based models like BERT, AraBERT, and XLM-RoBERTa. Experiments on datasets—HARD, Khooli, AJGT, and Ar-Tweet—covering MSA and dialects such as Gulf and Egyptian demonstrate that transformer-based models, particularly AraBERT v02, achieve the highest accuracy of 93.9% on the HARD dataset. The study highlights the significance of dataset characteristics and the advantages of advanced models, offering valuable insights into Arabic NLP and advancing SA research.
Authors - Ritika Upadhyay, Eshita Dey, Munmun Patra, Roji Khatun, Chinmoy Kar, Somenath Chaterjee Abstract - Predicting accurate rainfall is crucial for a country like India, which has a diverse economy. Agriculture is a vital aspect of life for many rural communities in India, making timely rainfall a significant concern for improving agricultural yields. However, predicting rainfall has become increasingly challenging due to drastic climate changes, resulting in more frequent natural calamities like floods and soil erosion. To address this issue, extensive research is underway to enhance rainfall prediction, allowing people to take appropriate precautions to protect their crops. Currently, predictive models tend to be complex statistical frameworks that can be expensive in terms of both computation and budget. As a more effective solution, using historical data combined with machine learning algorithms is being proposed. This research aims to improve rainfall prediction through algorithms such as Gradient Boosting and Random Forest. Model evaluation will utilize metrics like Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). This study has considered approximately 150 years of historical rainfall data (from 1813 to 2006) for different regions of India.
Authors - G.KALANANDHINI, VIGNESHWARAN.D, R.KARTHIKA, S.PUSHPALATHA, D.SAKTHIPRIYA Abstract - Activity delays confronted by crisis vehicles regularly result in basic time misfortune, imperiling lives. The Help to begin with Responders (AFR) framework addresses this issue by utilizing LoRa SX1276 communication modules designed as transmitters in crisis vehicles to communicate with recipients at activity intersections. This framework empowers programmed green light signals for drawing closer crisis vehicles, guaranteeing continuous section. GPS NEO-6M modules give directional data, whereas a centralized authorization component anticipates abuse. Particular vehicle IDs permit for prioritized reaction, with fire motors taking the most noteworthy need, taken after by ambulances and police cars. Activity policemen are informed to oversee synchronous mediations successfully. Typical activity flag operations continue when no crisis vehicle is recognized. The AFR framework leverages Arduino Nano for LoRa modules, Arduino UNO for activity control, and ESP8266 for authorization. This integration improves crisis reaction times and moves forward security for both patients and responders, displaying a noteworthy progression in urban activity management.
Authors - Varun M V, Venkat Raghavendra A H, V Hemanth, Ashwini Bhat Abstract - The work undertaken is a comprehensive analysis of cricket sounds, focusing on the interaction of the ball with the bat and the wicket, the study aims to distinguish between edged, shot, and bowled audio in both noisy and noise-free environments. Upon feature extraction, machine learning models XGBoost and Random Forest were trained, to accurately classify these distinct cricketing events. This not only enriches the realm of cricket analysis by facilitating informed decision-making and insights into player performance but also showcases the potential of audio-based sports analytics.
Authors - N.Janani Abstract - In clinical practices, almost 18-20% cases go either unnoticed or misdiagnosed due to overlapping and subtle features in imaging, especially in complicated cases. We tackle this by using Cross-Modality Attention Network (CMAN) which integrates details from multi-phase CTs. By leveraging the distinct advantages of each scan phase, this method provides a comprehensive understanding of the tumor’s structure and characteristics. The cross-modality approach employs an attention mechanism to integrate information from multiple scan modalities, each capturing unique details. This process emphasizes the most critical tumor-related features while effectively minimizing noise, ensuring enhanced classification accuracy. Achieving an impressive accuracy of 98.47% on the LIDC-IDRI dataset, the CMAN significantly reduces misdiagnosis in complex cases. This approach can be really helpful in filling the diagnostic gaps, facilitating more informed clinical decision-making and improved patient outcomes.
Authors - Parekh Rikita Dhaval, Hiteishi M. Diwanji Abstract - The Visual Question answering is an emerging multidisciplinary research field that intersect computer vision and natural language processing. Medical Visual Question Answering is one of the prominent area of VQA. Medical images and Clinical Questions related to the medical image is given as input to the VQA model and VQA model respond with corresponding answer in natural language. The aim of Medical VQA is to enhance interpretability of medical image data for enhancing diagnostic accuracy, clinical decision making and patient care. This paper presents a novel framework that integrates Vision Transformer (ViT), Language transformer (BERT), and a Convolutional Autoencoder (CAE) to improve the performance of Medical VQA task. The Vision Transformer is used to capture complex visual features from medical images, while BERT processes the corresponding clinical question to understand its context and generate meaningful language embedding. To further enhance visual feature extraction, a Convolutional Autoencoder [1], [2] is incorporated to preprocess and denoise the medical images, capturing essential patterns, compressing medical images without losing key features, thereby providing cleaner input to the ViT. The combined use of these three components enables the model to effectively align visual features with textual information, leading to more precise and context-aware answers. We evaluate the proposed ViT+BERT+CAE model on benchmark medical VQA dataset MEDVQA-2019, showing significant improvements over traditional methods based solely on convolutional or recurrent networks. The results demonstrate significant increase in accuracy, precision, recall, F1-Score and WuPS score after applying Covolutional AutoEncoder in Preprocessing stage.
Authors - Ritu Raj Pradhan, Darshan Gera, P. Sunil Kumar Abstract - This paper explores static facial expression recognition (FER) and presents a novel facial augmentation technique designed to enhance model training. By utilizing pre-trained facial landmark detection models, we analyze the spatial structure of faces within the FER training dataset. Based on the predicted landmark coordinates, facial images are augmented by strategically masking patches of varying sizes at key landmark locations. This approach emphasizes the structural significance of facial landmarks while preserving other critical facial features, enabling models to capture both global facial structure and nuanced expression-related details. Extensive experiments on benchmark datasets validate the effectiveness of the proposed method, showcasing its potential to improve FER performance, particularly in challenging scenarios.
Authors - Deepali, Karuna Kadian, Kashish Arora, Saumya Johar, Liza Abstract - The stock market has become increasingly unpredictable in recent years due to various factors like public sentiments, economy and geopolitical issues. The Traditional methods being used like time series model and Long Short-Term Memory (LSTM) models, often don’t make the correct predictions as they rely mostly on historical data of stock market and so they fail to grasp how market behaves or how chaotic behavior of market can be analyzed. These models hence may fail in case of making wise investment decisions. Our proposed methodology comes up with a hybrid approach using chaos theory, sentimental analysis for overcoming these challenges by analyzing the how stock prices might change according to the sentiments of people. We analyze 65,000 tweets of 95 organizations and their stocks and use chaos theory to find hidden patterns in stock movements. The classical computers take high computational time to analyze complex problems like stock market predictions. Hence, we combine these approaches with the Quantum Approximate Optimization Algorithm (QAOA) to solve the complex patterns of stock price prediction faster and more accurately than classical methods. We have used sentimental analysis, chaos theory with QAOA which is a combinatorial algorithm, being used to optimize the stock portfolio based on specific stock metrics- inclusive of F1 score(from sentimental anaylsis) and chaos theory assessments, it researches for the organisations with stability and low risk-high returns in stock market. Thus aiding investors and traders to make an informed decisions regarding where to invest with low risk and high returns.
Authors - Dipti Varpe, Kalyani Kulkarni, Vaidehi Deshpande, Vedaant Deshpande, Vaishnavi Habbu Abstract - Augmented Reality (AR) and Virtual Reality (VR) enhance traditional pedagogical methods by providing immersive, interactive and experiential learning environments, while catering to diverse learning styles. The paper examines their effectiveness in improving knowledge retention, fostering engagement, and enabling hands-on practice in simulated real-world scenarios, citing comparisons with traditional teaching tools. In education, AR and VR allow visualization of abstract concepts, collaborative virtual environments and gamified learning experiences that make complex subjects accessible and engaging. For training purposes, these technologies are instrumental in safe skill acquisition, particularly in high-risk fields such as healthcare and military operations. Challenges such as high costs of facility maintenance and safe implementation are also addressed. This review concludes with recommendations for leveraging this technology to create impactful and scalable solutions for learners and trainees in various disciplines.
Authors - R.V. Sai Sriram, A. Srujan, K. Rahul, K. Sathvik, Para Upendar Abstract - Freshness plays a crucial role in determining the quality of fruits and vegetables, directly impacting consumer health and influencing nutritional value. Fresh produce used in food processing industries must go through multiple stages—harvesting, sorting, classification, grading, and more—before reaching the customer. This paper introduces an organized and precise approach for classifying and detecting the freshness of fruits and vegetables. Leveraging advanced deep learning models, particularly convolutional neural networks (CNNs), this method analyzes images of produce. The training and evaluation dataset is large and varied, including diverse fruits and vegetables in various conditions. Freshness is determined by analyzing key features like color, texture, shape, and size. For example, fresh produce typically shows vibrant color and is free from mold or brown spots. Traditional methods for assessing quality through manual inspection and sorting are often slow and error prone. Automated detection techniques can significantly mitigate these challenges. Therefore, this paper proposes an automated approach to freshness detection, which first identifies whether an image shows a fruit or vegetable and then classifies it as either fresh or rotten. The ResNet18 deep learning model is employed for this identification and classification task. It also estimates the size of the fruit/vegetable using OpenCV. The qualitative analysis of this approach demonstrates outstanding performance on the fruits and vegetables dataset.
Authors - Manohar R, N Abhishek, Nagesh S, Sumith R, C Balarengadurai Abstract - Water quality monitoring is essential for public health and environmental stewardship. Conventional methods, while effective, are often costly, time-intensive, and require specialized skills. In response to these limitations, this paper explores machine learning as a rapid, scalable solution to classify water quality using key parameters, including pH, turbidity, organic carbon, and contaminants. By implementing algorithms such as Random Forest, SVM, and other advanced models, we seek to enhance the precision of water purity assessments. This paper shows the potential of ML applications in real-time monitoring, addressing the need for accessible, cost-efficient, and accurate water quality solutions suitable for broad deployment across diverse environments.
Authors - Hrudai Aditya Dharmala, Ajay Kumar Thallada, Kovvur Ram Mohan Rao Abstract - Recent advances in vision-language models have demonstrated remarkable multimodal generation capabilities. However, their typical reliance on training large models on massive datasets poses challenges in terms of data and computational resources. Drawing inspiration from the expert-based architecture of Prismer, we propose a novel framework for contextual visual question answering specifically tailored to remote sensing imagery. Our methodology extends the Prismer architecture through a two-stage approach: first, by incorporating a domain-specific segmentation expert trained on remote sensing datasets, and second, by integrating a fine-tuned Large Language Model (Mistral 7B) optimized using Parameter-Efficient Fine-Tuning (PEFT) with QLoRA for remote sensing terminology, with hyperparameters optimized with help of Unsloth framework. The segmentation expert performs the analysis of remote sensing imagery, At the same time, the language model acts as a reasoning expert, combining domain-specific knowledge with natural language understanding to process visual contexts and generate accurate responses. In our framework, the use of the Unsloth fine-tuning approach for the language model helps maintain high performance within the defined scope of remote sensing classes and terminology while avoiding hallucination or deviation from established classification schemas. This opens an exciting direction for making the use of Earth observation data more accessible to end-users, demonstrating significant improvements in accuracy and reliability compared to traditional approaches. Experimental results validate that this architecture effectively balances domain expertise with computational efficiency, providing a practical solution for remote sensing visual question answering that requires substantially fewer computational resources compared to end-to-end training of massive models.
Authors - Jenat Arshad, Afruja Akter, Tanjina Akter, Kingkar Prosad Ghosh, Anupam Singha Abstract - Cryptocurrencies have emerged as a significant financial asset class, attracting global attention for their potential to disrupt traditional financial systems. Due to its extreme price volatility and ability to be traded without the assistance of a third party, cryptocurrencies have gained popularity among a wide range of individuals. This paper presents a comprehensive study of machine learning techniques, particularly deep learning models such as Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Artificial Neural Network (ANN) in predicting cryptocurrency prices. Furthermore, this study addresses the security and privacy challenges inherent to blockchain technology, upon which cryptocurrencies operate. We predict the prices of popular cryptocurrencies like Bitcoin, and Ethereum, and lesser-known ones like Binancecoin, Litecoin, and Ripple through a hybrid deep learning model. This paper also compares cryptocurrency price prediction with machine learning models like GRU, ANN, and our proposed model Hybrid LSTM-GRU. The results demonstrate the efficacy of machine learning in price prediction, highlighting blockchain's potential to enhance security and privacy in financial transactions. Our model gives the value of MSE, RMSE, MAE and MAPE to determine the forecasting. We’ve also added the manual calculation for each metric and compared the actual price with the predicted price that our model gave.
Authors - Harsha S Khurana, Payal D Joshi Abstract - Stress is a major health concern that significantly affects mental stability and can have adverse effects on physical well-being if prolonged. Early detection of stress can help and prevent health-related issues. Individual stress patterns are detected using a variety of bio-signals, including thermal, electrical, auditory, and visual cues which are invasive methods. But according to the well-known saying statement, "Face is a mirror of mind," one can observe one’s emotion or mental state on one’s face. Based on this, Investigated the potential of using facial expressions as a non-invasive method to detect stress levels. Facial expressions could be analyzed and classified as stress and non-stress by examining facial expressions. To solve this problem we have used pretrained network models - Inception, Xception, MobileNetv2, Vgg19, EfficientNet deep learning models, and Affectnet Dataset for stress detection and also represent the comparative study of networks based on confusion and performance metrics. Testing on a separate set of data of images indicates that the MobileNetv2 and Xception models give more accuracy for stress detection.
Authors - Rinkesh N Parmar, Payal D Joshi Abstract - Cotton, an essential crop for the textile industry and millions of farmers, is vulnerable to diseases that can significantly affect yields and profitability. Traditional methods of disease detection, relying on expert visual inspections, are labour-intensive, time-consuming, and prone to errors, often causing delays in addressing problems. This study investigates the use of Convolutional Neural Networks (CNNs) for automated, early, and accurate detection of cotton diseases. CNNs are effective at extracting hierarchical features from raw image data, making them ideal for image classification tasks. In this approach, a labelled dataset of cotton plant images is utilized to train the CNN model, incorporating data augmentation to enhance variability and generalization. The model employs convolutional layers for feature extraction, max-pooling layers for dimensionality reduction, dropout layers for regularization, and fully connected layers for classification. The Adam optimizer, known for faster convergence, is used during training, along with categorical cross-entropy loss. The evaluation is based on accuracy, precision, recall, and F1-score. The model showed significant improvements in performance. The baseline CNN achieved 92.34% accuracy, but advanced architectures like Hybrid CNN-LSTM, DenseNet-121, ResNet-50, and InceptionV3 enhanced accuracy by 2-3%, along with increased precision, recall, and F1-score. The Hybrid CNN-LSTM model outperformed others, achieving 94.5% accuracy, 93.5% precision, 93.2% recall, and 93.3% F1-score. These results suggest that CNN-based models, particularly Hybrid CNN-LSTM, offer substantial improvements in cotton disease detection. The incorporation of data augmentation and dropout regularization strengthens the model, making it effective for real-time agricultural disease management. Future work will focus on expanding the dataset, improving the model, and implementing it in real-world cotton farming practices.
Authors - Joven A. Tolentino Abstract - The growing demand for electricity necessitates effective monitoring and forecasting of consumption trends. This study employs ARIMA modeling, using data from the Department of Energy, Philippines, to analyze and predict electricity consumption. The forecast for the next two years indicated an 18.99% increase in consumption between 2016 and 2017.To enhance analysis, the predicted data was clustered using the K-Means algorithm to group months with similar consumption patterns. This approach identified periods of high, medium, and low electricity usage, providing valuable insights into peak demand months. Such data-driven findings can guide electricity providers in prioritizing resources and implementing strategies to address fluctuations in consumer demand effectively. This study emphasizes the importance of forecasting and clustering as tools for decision-making to mitigate challenges arising from increasing electricity demand.
Authors - Manmeet Borkar, Suneeta Raykar Abstract - Monitoring biomarkers is essential for patients to effectively manage their health profiles and prevent potential complications. Regular tracking of these indicators allows for timely interventions and better control over one’s health, particularly when the methods employed are non-invasive and grant convenience and comfort to the patient. Conventionally, this monitoring is accomplished in pathology laboratories, by collecting blood samples or finger-pricking, which can be distressing and impractical for regular use. Given the growing need for more accessible and affordable healthcare solutions, the development of a cost-effective non-invasive method has become crucial. We propose the use of machine learning models to enable non-invasive measurement of biomarkers such as Total Cholesterol, Uric acid and Blood Sugar. Several Machine learning algorithms, including Linear Regression, K-Nearest Neighbors (KNN), Decision Tree, Random Forest and Support Vector Regression (SVR), were applied to the datasets constructed using the MAX30102 sensor. The metrics used to evaluate regression models were Mean Square Error (MSE) and Coefficient of determination (R²). The final prediction model was built using the algorithm that yielded the highest Coefficient of determination (R²). A user-friendly interface was developed using Tkinter, allowing the input of sensor values from the MAX30102 sensor. The prediction of biomarker values promotes health awareness and timely alerts against potential complications. The results obtained using this approach were validated against laboratory blood reports, revealing an average offset of less than 10% in the predictions.
Authors - Kirthika. P, M. Suresh, S. Kanagaraj Abstract - This paper explores the social barriers faced by Indian plantation communities. It focuses on how these obstacles impact their well-being, productivity, and social mobility. By analyzing historical, socioeconomic, and cultural factors, the study uncovers the multifaceted challenges plantation workers encounter, including income, education, social position in the community, social networks, migration, exploitation, and working and living conditions. The DEMATEL approach identifies the barriers and analyzes the interrelationships among those that impact social barriers among plantation workers. This paper identified seven barriers of impact from a literature review followed by interviews with experts to interpret the interconnection of barriers and investigate the interrelationships. The result says that income and education are the key barriers impacting the lives of plantation workers in their society. The present study incorporates the DEMATEL approach model to analyze the critical barriers in mapping the social barriers of plantation workers. The DEMATEL approach model is the first attempt to study the interrelationship among the barriers. The research overviews the prevailing issues through field surveys, interviews, and literature reviews. The paper will conclude with actionable recommendations aimed at policymakers, community leaders, and stakeholders to mitigate these barriers and promote a more inclusive and equitable environment for plantation workers.
Authors - Aishani Das, Sobitha Ahila, Sreyashi Dey Abstract - Sentiment analysis within the food industry offers essential insights into customer satisfaction, product perception, and emerging concerns. A novel sentiment classification model is developed for Amazon food reviews, leveraging Sentiments are categorized as positive, neutral, or negative using techniques from Natural Language Processing and Machine Learning. Traditional ML algorithms, such as Logistic Regression, Naive Bayes, and Support Vector Machines, are combined with the BERT deep learning model to enhance classification accuracy. With a dataset of over 500,000 reviews sourced from Kaggle, the methodology includes data cleaning, feature extraction, exploratory data analysis, model training, and evaluation. Initial findings demonstrate SVM’s high predictive accuracy in sentiment classification, while BERT’s advanced contextual understanding suggests further enhancements. Applications of this model extend to real-time feedback systems that assist businesses in identifying and addressing customer sentiments promptly. Future developments aim to improve accuracy, incorporate a diverse range of datasets, and integrate real-time processing and multilingual analysis for broader, more effective sentiment analysis capabilities.
Authors - Tinoy Santra, Sahil Neekhra, Ritik Gupta, Gunabalan Ramachandiran Abstract - With the rising environmental degradation and increasing global warming, electric vehicles are the promising concept in the automobile industry. Different sources of energy are available for giving power to drive the vehicle. Sunlight being an efficient and abundant resource, the world is moving towards solar energy leaving behind conventional power resources. Moreover, battery based electric vehicles have short driving range and speed which is not acceptable in the dog-eat-dog market. This paper discusses a simple approach for BLDC motor driven electric vehicle powered by buck-boost converter. The primary energy source is solar energy, and the PI controller holds the DC-DC converter's output constant. A 660 W, 48 V BLDC motor driven electric bike system is worth an alternative when it is solar powered which solves utmost all the problems faced in usage of EVs. The circuit is simulated in MATLAB environment and output parameters are observed for different load conditions. Overall, the motive is to prove that electric vehicles are more efficient and cost effective than the conventional ones.
Authors - A.Kousar Nikhath, J.Ananya Reddy, P.Aishwarya, S.Maadhurya Sri, P.Gowthami Abstract - Sign language is the medium between the people who can hear and speak and those who cannot. This project is set to be used in the development of technologies that are beneficial for the lives of individuals with disabilities. The project studies in-depth the use of computer vision and deep learning. The accurate and the regional language translation began with the gestures of the sign languages as the input information, and finally the software produced the accurate translation in the regional language. I am inspired by the prospect of using Artificial Intelligence technology in developing hereditary transmission from a worldwide venue and health diagnosis in a timely manner. Convolutional Neural Network (CNN) is employed to pick up characteristics from hand movement that belongs to the sign language. These attributes are used in the training set as features, themselves in the classification of the gesture, and the process is the learning of this model for the recognition of gestures in real-time. Further, there is an inclusion of computer vision for preprocessing and the sake of accuracy prediction of the recognition process. The functionality of the sign language recognition system is assessed by using a variety of experiments, including accuracy and speed. In general, the developed Sign Language Recognition System with integration of deep learning and computer vision techniques facilitates the precise and quick recognition of sign language gestures. Integration with a translator in addition to this not only makes it multi-language support but also guarantees the correct translation into regional languages.
Authors - Hritesh Kumar Shanty, Padirolu Moses, Tulasiram Nimmagadda, Samson Anosh Babu Parisapogu Abstract - In today’s digital world, combining image editing with secure NFT trading is essential. Imagify addresses this need by offering a unified platform with advanced artificial intelligence tools for image enhancement, recoloring, restoration, and object removal, empowering users to customize images to their preferences. Imagify also simplifies the NFT creation process, allowing users to seamlessly transform their edited images into NFTs that can be bought and sold on a blockchain-secured marketplace. This ensures transparent and secure transactions, providing peace of mind for both creators and buyers. With a flexible, credit-based system, users pay only for the features they choose, making it a cost-effective option. By merging intuitive image editing with a streamlined NFT marketplace, Imagify offers an accessible, user-friendly platform where creators and collectors can engage in digital image trading confidently. This integration creates an efficient and transparent process, supporting both casual creators and seasoned collectors seeking a secure, comprehensive solution for managing and trading digital images.
Authors - R V S S Surya Abhishek, T Sridevi Abstract - This paper provides an overview of the current state of AI-based approaches in virtual fitness coaching, focusing on posture estimation and exercise tracking along with real-time feedback. Advances in pose estimation models, including OpenPose, MediaPipe, and AlphaPose, are boosting personalized exercise correction and injury prevention within the sphere of fitness applications. Current literature varies from 2D to 3D pose estimation that includes action recognition and deep learning framework for specific inputs toward movement analysis and user engagement. There is still much room for improvement in current models, with regards to adaptation to individual needs and environments, such as the real-time accuracy that often has not been matched by the personal feedback and robustness of exercise variations. It discusses the approaches currently in use, their applications, and challenges, and by looking at the topic, this paper insinuates the improvement in the adaptability and customization of AI fitness solutions to perfectly emulate human trainers.
Authors - Mrudul Dixit, Rajiya Landage, Prachi Raut Abstract - The paper presents a comprehensive comparison of Speech-to-Text (STT) and Text-to-Speech (TTS) models, two foundational technologies in the field of natural language processing and human-computer interaction. The paper examines the evolution of these models, focusing on state-of-the-art approaches such as Whisper Automatic Speech Recognition (ASR), DeepSpeech, and Wav2vec, Kaldi, SpeechBrain for STT, and Tacotron, WaveNet, gTTS and FastSpeech for TTS. Through an analysis of architectures, performance metrics, and applications, the paper highlights the strengths and limitations of each model, particularly in domains requiring high accuracy, multilingual support, and real-time processing. The paper also explores the challenges faced by STT and TTS systems, including handling diverse languages, background noise, and generating natural-sounding speech. There are recent advances in end-to-end models, transfer learning, and multimodal approaches that are pushing the boundaries of both STT and TTS technologies. By providing a detailed comparison and identifying future research directions, this review aims to guide researchers and practitioners in selecting and developing speech models for various applications, particularly in enhancing accessibility for specially-abled individuals.
Authors - A.Kousar Nikhath, Aanchal Jain, Ananya D, Ramana Teja Abstract - The project focuses on creating an advanced system for visual speech recognition by performing lipreading at the sentence level. Traditional approaches, which were limited to word-level recognition, often lacked sufficient contextual understanding and real-world usability. This work aims to overcome those limitations by utilizing cutting-edge deep learning models, such as CNNs, RNNs, and hybrid architectures, to effectively process visual inputs and generate coherent speech predictions. The system's development follows a systematic approach, beginning with a review of existing solutions and their shortcomings. The proposed framework captures both temporal and spatial dynamics of lip movements using specialized neural networks, significantly enhancing the accuracy of sentence-level predictions. Extensive testing on diverse datasets validates the system’s efficiency, scalability, and practical applications. This study underscores the critical role of robust feature extraction, sequential data modeling, and hierarchical processing in achieving effective sentence-level lipreading. The results demonstrate notable improvements in performance metrics. Additionally, the project outlines future advancements, including optimizing the system for real-time processing and resource-constrained environments, paving the way for practical implementation in multiple fields.
Authors - Randeep Singh Klair, Gurkunwar Singh, Ritik Verma, Satvik Rawal, Rajan Kakkar, Agamnoor Singh Vasir, Nilimp Rathore Abstract - The most accurate way to measure galaxy redshifts is using spectroscopy, but it takes a lot of computer power and telescope time. Despite their speed and scalability, photometric techniques are less precise. Thanks to large astronomical datasets, machine learning has become a potent technique for increasing cosmology research’s scalability and accuracy. On datasets such as the Sloan Digital Sky Survey, algorithms such as k-Nearest Neighbors, Random Forests, Support Vector Machines, Gradient Boosting, and Neural Networks are assessed using metrics like R-squared, Mean Absolute Error, and Root Mean Square Error. Ensemble approaches provide reliable accuracy, whereas neural networks are excellent at capturing non-linear correlations. Improvements in feature selection, hyperparameter tuning, and interpretability are essential to improving machine learning applications for photometric redshift estimation and providing deeper insights into cosmic structure and development.
Authors - Sudha K L, Navya Holla K, Kavita Guddad Abstract - The antenna is a vital component of the Magnetic Resonance Imaging (MRI) machine which receives the radio frequency signals emitted by the protons in the body after the RF pulse is turned off. Specialized high frequency antennas can improve the quality, clarity, and resolution of the resulting MRI images. This paper deals with the design of Bow Tie antenna for X-Band in the frequency range 8–12 GHz, used in ultra-high field MRI systems. Using the Ansys HFSS tool, the antenna is designed and simulated and analysed. The fabricated antenna with the design specifications is tested in anechoic chamber for its working. Reflection coefficient at 10.5GHz is found to be around -14 dB for simulated antenna and -12 dB for fabricated antenna, which is satisfactory for practical application. Differences between the measured and simulated values were seen in results which are caused by cable loss in the measuring apparatus.
Authors - Bharati B Pannyagol, S.L Deshpande, Rohit Kaliwal, Bharati Chilad Abstract - The Internet of Things has revolutionized markets by connecting previously isolated devices, but this integration raises security risks from malicious nodes that can corrupt data or disrupt operations. This evaluation of Federated Learning's possible application as a decentralized node identification technique highlights its advantages over standard machine learning approaches. Internet of Thing devices may collaborate on model training while protecting sensitive data and reducing network use. Federated Learning and Blockchain interactions creates a robust framework addressing critical IoT challenges like data privacy, security, and trust. Blockchain enhances this system by providing a decentralized, tamper-resistant ledger that ensures data integrity and transparency. Automated processes, including model validation and incentive distribution, are facilitated by smart contracts. While this integrated approach improves data protection and scalability, challenges such as computational demands and consensus delays remain. The survey discusses practical applications, challenges, and future research directions for combining Federated Learning and Blockchain in IoT systems.
Authors - Bhadouriya Khushi Mukeshsingh, Rajput Adityasingh Shashikantsingh, Patel Swayam Vinodkumar, Ashish P. Patel, Nirav D. Mehta, Anwarul M. Haque Abstract - As digital infrastructure becomes more interconnected, effective cyber security has never been more important. This article explodes how advances in power electronics technology can support and improve cyber security frameworks. Energy management strategies, control systems, and semiconductor technologies can be used to increase the systems resilience to potential vulnerabilities that serve as possible entry points for cyber attackers. The research discussed in this article seeks to demonstrate that optimized distribution systems with adaptive control techniques can improve the stability and reliability of critical infrastructure, even in the face of cyber threats. This article discusses the inter-relationship between energy management and cyber security, showing the reader how power electronics can be important in developing a holistic security strategy. It describes a proposed approach to integrating power electronics into cyber security to create an adaptive, robust defence mechanism. This study provides valuable insights into the design of systems that are not only efficient but also fortified against evolving cyber threats, contributing to the broader understanding of how technology convergence can enhance overall infrastructure security.
Authors - Madhura Shankarpure, Dipti D. Patil Abstract - This paper presents a robust framework for YOLO (You Only Look Once) algorithm- based orange detection and localization in photos and videos is presented. The system combines contour-based bounding box localization with deep learning-based item recognition for increased accuracy. Transfer learning was used to refine a pre-trained YOLOv10 model on a Fruit 360 dataset. Data augmentation techniques such as random rotations, brightness changes, and scaling were applied to improve the model's resilience. Bounding boxes are created around identified oranges with a confidence threshold greater than 0.5 as part of the real-time video processing methodology. The model performed well on a balanced test dataset, achieving 95% accuracy, 92% precision, and 90% recall. These findings show how well YOLO works when combined with conventional computer vision methods for real-world uses like automated fruit sorting, fruit harvesting, and real-time market monitoring. The processed video output confirms the system's suitability for real-world situations.
Authors - Manjusha Pandey, Rajeev Kumar, Satyam Tiwary, Yuvraj Singh, Oindrella Chatterjee, Siddharth Swarup Rautaray Abstract - This paper delves into the complexities of providing equitable access to multimedia content across India's diverse linguistic landscape. It proposes innovative strategies for translating English video content into Indian regional languages, leveraging cutting-edge technologies such as machine translation, speech recognition, and text-to-speech synthesis. The suggested approach involves a systematic four-phase process, encompassing audio separation, text conversion, machine translation, and speech synthesis. [1] By utilizing open-source tools like IBM's Watson supercomputer and the Flite engine from Carnegie Mellon University, the system achieves a commendable 79% accuracy in terms of naturalness and fluency, as evaluated by native speakers. However, challenges persist in handling multi-speaker conversations and accommodating a broader range of Indian languages. Despite these limitations, the research lays a solid foundation for future advancements in the field. By fostering cross-cultural communication and knowledge dissemination, the proposed solution holds the potential to bridge linguistic barriers, empower marginalized communities, and foster an inclusive digital ecosystem in India.
Authors - Shubham Kadam, Chhitij Raj, Pankajkumar Anawade, Deepak sharma, Utkarsha Wanjari Abstract - This paper explores the phenomenon of Artificial Intelligence (AI) transformation in organizational culture evaluation, discussing capabilities, advantages, obstacles and future direction. While traditional means of mining forms like surveys and interviews are often lengthy and flawed due to human biases, AI tools rely on real-time data, natural language processing, and predictive analysis to deliver objective insights instantly. Such applications, including sentiment analysis, behavioural analytics, and cultural diagnostics, allow organizations to mitigate cultural misalignments in advance at the organizational level or within specific teams, idem for the employee's engagement and inclusivity. Nonetheless, ethical issues related to data privacy, security and algorithmic wage discrimination continue to pose significant challenges. The implications of this study highlight the increasing importance of artificial intelligence in enabling organizations to build dynamic, resilient, and agile organizational cultures.
Authors - Sakshi Sharma, Tanisha Verma, Shailesh D. Kamble Abstract - Accurate, timely detection of plant disease is critical to protect crop from being damaged and increase agricultural productivity. Many disease identification methods are labor intensive and only practical with an expert set of trained eyes. A mobile application for real time plant disease detection using CNNs presented in this paper allows farmers to have a simple yet powerful access to a diagnostic tool. CNN was trained on a big collection of plant leaf images to discriminate between diseases using Keras and TensorFlow. The application was built using Flutter for cross platform mobile development, trained model deployed on mobile devices using TensorFlow Lite, which allows offline inference. Users can capture images of affected plant leaves and get immediate diagnostic feedback as to the potential disease involved. Following data preprocessing and model optimization, the application uses a lightweight architecture that achieves high accuracy while meeting requirements for mobile deployment. This research shows integration of AI with mobile technology can provide a scalable, efficient and accessible solution to crop disease detection. The system as proposed is capable of improving crop health management, reducing losses, and working towards global food security.
Authors - Utkarsha Wanjari, Shubham Kadam Abstract - Gamification in HRM through AI is thus a total revolution that can maximize the engagement and productivity of employees. Game-like qualities such as rewards, badges, leaderboards, and challenges incorporated in the HR processes create a captivating environment that motivates and pushes an employee into an achievement culture. AI amplifies the effect of gamification: it enables data-driven insights, personalized experience, and real-time feedback loops. The paper also looks into the psychological underpinnings of gamification intrinsic and extrinsic motivation and their alignment with the organizational goals. It analyzes some of the challenges in incorporating gamification, including ethical considerations, potential overuse, and the balance between entertainment and productivity. It also reflects on some success stories and presents a pathway to implementing gamified AI solutions into the existing HR framework. This is because gamification, combined with AI, will alter the way human resource practice prevails, uplift employee productivity, boost employee satisfaction, and contribute to the long-term success of an organization. The present research study aspires to provide business organizations with the actionability of a very innovative method to remain ahead of their game in the changed wilderness of the workplace.
Authors - Shubham Kadam, Chhitij Raj, Pankajkumar Anawade, Deepak sharma, Utkarsha Wanjari Abstract - The paper investigates Green ICT leadership in e-governance towards carbon footprint mitigation from the digital government. E-governance uses information and communication technology (ICT) to deliver administrative services through enhanced technology in this service chain, thus increasing the efficiency of their services, which is guided by an aim for complete transparency that requires accurate information. However, digitalization is responsible for environmental problems such as carbon emissions produced by data centres, digital infrastructure, and devices. The paper emphasizes the importance of vision-oriented leadership in promoting sustainability through processes of strategic thinking, collaboration and innovation. The Guide presents a series of critical strategies, including energy-efficient data centres, virtualization and cloud computing, sustainable procurement, and citizen engagement to build green practices. Innovative technologies such as AI, IoT, and blockchain are labelled enablers for optimizing energy consumption and increasing transparency.
Authors - Alpa R. Barad, Ankit R. Bhavsar Abstract - Analysis of grass quality is essential to improve cattle health. To improve animals' health and productivity, it is necessary to survey quality food. Grass is a primary and major source of food for every cattle. As a part of vegetation quality of grass is decreasing day by day, and it’s also not possible to survey fresh grass on a daily basis. Proposed research is used to analyze the quality of grass based on its color space. The quality of grass differs over the grass species and weather, and it's become more difficult with a single model to recognize its quality. To solve this problem proposed research uses machine learning based hybrid approach. The proposed research uses Median filter with kmeans clustering. Based on the clustering, the Simulation uses color deflection code to identify threshold values for a given species of grass. Proposed research finds the remarkable performance of three different qualities of grass. Simulation of study uses a Wiener filter and data augmentation to identify the impact of the proposed k-means based hybrid approach for grass quality recognition.
Authors - Aryan Jain, Shrirang Joshi, Vatsal Jain, Dinesh Kumar Saini Abstract - 5G network roll-out is expanding globally, which further shows that low-cost and good modem design remains to be absolutely integral. Scaling here is tough, not to mention the complexity and cost of production involved in traditional hardware-based 5G modems. This analysis explores how advances such as Open Radio Access Networks (Open RAN), Software-Defined Networking (SDN) and Network Function Virtualization (NFV) could reduce the hardware requirements, leading to lower costs for 5G modems. We marvel over the functionalities which we take for granted in a modem, such as digital signal processing and base-band processing, are being virtualized so that it is done on general-purpose hardware rather than on parts custom designed to do these particular tasks. Adopting cloud-native and software-based solutions for these traditional hardware-driven processes can bring huge savings without compromising on performance. In addition, we discuss Dynamic Resource and Change in Network efficiency which are improved by Modem Allocation, Edge Computing, Network Slicing — SDN NFV open day light. This collection of methods is described in a comprehensive article on the application of virtual network technologies to improve 5G modem design, reduce deployment costs, and enable more flexible, scalable, and energy-efficient 5G solutions.
Authors - Mallu Praneeth Reddy, T. A. S. Vardhan, Kura Bhargava Gupta, Nagireddy Deekshitha, Pudari Shrainya Goud, Khalvida Pamarty, Sushama Rani Dutta Abstract - This paper aims to predict the calories burnt by a person using machine learning models built on several regression algorithms like Linear, Random Forest, XGBoost,and CatBoost based on gender, age, height, weight, duration of exercise, body temperature, and heartbeat of the person. In addition, the analysis compares the algorithms based on performance metrics like MAE (Mean Absolute Error), MSE (Mean Square Error), and R2 score and determines the most effective algorithm for calorie prediction.
Authors - Shailender Vats, Prasadu Peddi, Prashant Vats Abstract - Blockchain technology's explosive growth has created previously unheard-of potential in several industries, but it has also revealed fresh security flaws. To improve threat detection and response mechanisms, this paper provides a complete intrusion detection system (IDS) designed especially for distributed blockchain ledger security. It makes use of sophisticated smart contracts. We demonstrate the efficacy of the suggested IDS in detecting possible intrusions while preserving the integrity of the blockchain environment by validating it using simulation-based scenarios. According to the research, combining IDS with blockchain technology and smart contracts greatly improves security and is a viable way to address current cybersecurity issues.
Authors - M Nanda Kumar, Harsh Sharma, Rajan Kakkar, Tushar Naha, Atul, Rishabh Yadav, Naveen Abstract - The demand for autonomous vehicles (AVs) has grown rapidly due to their potential to revolutionize transportation by enhancing safety, efficiency, and convenience while reducing human error, a leading cause of road accidents. AVs leverage advanced technologies like machine learning, LIDAR, GPS, cameras, RADAR, and ultrasonic sensors for precise navigation, obstacle detection, and real-time decision-making. However, their reliability and safety in di-verse environmental conditions remain a significant challenge. Extreme weather events such as heavy rain, snow, fog, ice, hail, and dust storms can impair sensor performance, reducing visibility, traction, and the ability to detect road markings, obstacles, and other vehicles. These conditions degrade the accuracy of critical systems like LIDAR, RADAR, and cameras, raising concerns about AVs’ reliability, particularly in emergencies or unpredictable scenarios. This review paper explores the effects of adverse weather on AVs’ performance, analyzing the limitations of key sensors and assessing various mitigation strategies to enhance their resilience. By identifying technological gaps and emphasizing the need for weather-resilient solutions, the paper aims to guide future research and innovation to improve AVs’ safety and reliability in challenging real-world conditions, ensuring their readiness for broader deployment.
