Authors - Vandana R. Babrekar, Sandeep V. Gaikwad Abstract - This paper presents a literature review of current state of knowledge and innovation related to precision agriculture (PA) technologies leveraging Internet of Things (IoT), Artificial intelligence (AI), Computer vision (CV), Machine learning (ML), and Deep learning (DL). The review provides a thorough understanding of the various developments made in the above-mentioned technologies in precision farming. It also states several challenges after reviewing research publications, and it also recommends thrust areas for future studies.
Authors - Killol Pandya, Trushit Upadhyaya, Aneri Pandya, Upesh Patel, Poonam Thanki, Kanwarpreet Kaur, Jinesh Varma Abstract - The 2x2 Ultra Wide Band(UWB) MIMO antenna model with 31 x 26 x 1.56 mm3 are designed and presented. The proposed antenna system consists of two primary radiators over the surface layer and positioned with sufficient space between each other to receive adequate response. The conducting patches include U shaped and inverse U shaped slots to increase the current traveling surface. The bottom corners of the patches are having fractional geometry and supporting arms associated with microstrip line to offer resonances at desired frequencies. The Ultra Wide band characteristics are reported due to partial ground plane geometry. The additional strip with slots are deployed with ground plane to receive the satisfactory isolation as the radiators create mutual effect on the other ones which turns into performance degradation. For UWB, the structure exhibits gain of around 2 dB and around a bandwidth of 85% which shows the applicability of discussed research. The other diversity parameters such as Mean Effective Gain(MEG), Gain diversity, Channel Capacity Loss and Envelope Correlation parameter are analyzed and presented. The proposed antenna is well appropriate for WiMAX, WLAN and C band applications.
Authors - V V N Sai Rajeshwar, Gangalam Sumanth Abstract - Attributes like size, shape, color, quantity, quality, etc. are very hard to find in an image of a particular object. There are various ways to process an image and derive attributes namely – Faster R – CNN, SSD (Single Shot Detector), SPP (Spatial Pyramid Pooling) - Net. Many modern technologies use YOLOv8, the 8th version of YOLO (You Only Look Once), which is quite accurate and detects real - time objects. To enhance the proficiency and scalability of the model Deep Convolutional Neural Networks (DCNN) were used. This paper highlights a very unique strategy to enhance the abilities of YOLO with the help of dynamic masking and also advances the searching and findings of attributes in a unique way. The use of Compute Unified Device Architecture (CUDA) which uses GPUs rather than CPU, made it convenient to run on a higher loads of data. After masking an image or stream of images, detecting colors is easier, and expressing dominant colors is the motive of this research. Availing Coordinates of specified images in a particular image or stream of images is very helpful in various surveillance-related actions like military, and navy, and also useful in domestic purposes.
Authors - Priya Deokar, Sandhya Arora Abstract - Multiparty Computation (MPC) allows users to use their private inputs to compute a function without revealing anything about the inputs. This feature is useful in computing the aggregation of smart meter readings. The smart grid provides bi-directional communication between the smart meter and the utility supplier. Periodic sharing of fine-grained information by a smart meter presents a greater danger to privacy. Secure multiparty computing is one of the most efficient ways to preserve privacy among users. This paper briefly overviews MPC techniques and their usage in Smart Grid communication.
Authors - Tamanna Kalariya, Bimal Patel, Martin Parmer, Mrugendra Rahevar Abstract - The Agile model has demonstrated clear superiority over traditional software process models by offering enhanced flexibility and adaptability to evolving project requirements. Unlike rigid, linear methodologies, Agile promotes iterative development, allowing teams to quickly incorporate feedback and adapt to changing needs. This iterative approach is particularly advantageous for software testers, enabling continuous testing throughout the development lifecycle, which improves code quality and reduces defects. GitHub, as a collaborative development platform, amplifies these Agile benefits by providing robust version control and seamless integration with Agile workflows. For testers, GitHub facilitates real-time collaboration, enabling immediate feedback and shared responsibility for quality assurance. Features such as pull requests, issue tracking, and continuous integration streamline the testing process and enhance communication between developers and testers. Additionally, GitHub’s Agile-driven workflow provides a competitive advantage over other code-sharing tools by optimizing development cycles and fostering a strong collaborative culture. The platform's rich ecosystem of tools, combined with extensive community support, further boosts productivity and innovation. This paper examines how Agile methodologies within GitHub enhance both the development and testing processes, positioning GitHub as a leader in the collaborative development landscape and driving greater efficiency in modern software engineering practices.
Authors - Nishit Patel, Bimal Patel, Mansi Patel, Parth Shah, Nishat Shaikh Abstract - Agile model has demonstrated clear superiority over traditional software process models by offering enhanced flexibility and responsiveness to changing requirements, particularly essential in the fast-paced automotive industry. Prior to adopting Agile, Tesla encountered significant challenges, including prolonged development cycles and difficulties in scaling manufacturing processes to meet increasing demand. These constraints limited Tesla’s ability to innovate rapidly and deliver advanced features such as Full Self-Driving (FSD) capabilities. By implementing Agile methodologies, Tesla transformed its software development and manufacturing ramp-ups, enabling iterative development with continuous testing and integration. This shift significantly accelerated FSD development, allowing for faster adaptation to technological changes. The adoption of Agile led to measurable improvements, including reduced time-to-market for new features, improved cross-functional collaboration, and enhanced product quality. Compared to other automotive giants, Tesla’s Agile integration has provided a distinct competitive advantage in terms of operational efficiency and innovation. While competitors often rely on established, rigid processes, Tesla’s Agile approach enables rapid adaptation to market demands and continuous consumer feedback. This paper highlights the transformative impact of Agile methodologies on Tesla’s product development and operational strategies, demonstrating how Agile has positioned Tesla as a leader in automotive innovation, driving significant advancements in technology and customer satisfaction.
Authors - Aarti Agarkar, Ayush Sasane, Amankumar Kumare, Aditya Kadlag, Gaurang Khanderay Abstract - This paper presents a highly accurate intelligent traffic sign recognition system, which has potential to greatly improve safety on roads and make autonomous driving possible. It uses Convolutional Neural Networks (CNN) to detect and classify traffic signs, accurately robustly in real-time systems. The process involves pre-processing a large number of images, training the model and performing real-time detection with OpenCV in Python. The Node MCU Microcontroller is integrated to make the communication and responses more stable and can be set automatically after recognizing traffic signs. The findings show improved precision and stability of the properties that are essential for consideration in autonomous vehicle systems, where harmonious driving is needed to upgrade latest architectures.
Authors - Vidisha Deshpande, Gauri Shelke, Bhakti Kadam Abstract - Advancements in deep learning are fundamentally transforming assistive technologies, providing visually impaired users with unprecedented access to information and enhanced interaction with their surroundings. This paper comprehensively surveys traditional and emerging assistive technologies, focusing on real-time image caption generation systems. The modern advancements that bridge sensory limitations and digital interaction by covering a range of technologies such as Optical Character Recognition (OCR)-based text readers, object detection systems, image captioning systems, and intelligent haptic feedback devices are highlighted. In particular, the critical role of vision-language models and multimodal systems, which enable real-time auditory descriptions of visual scenes is studied. The survey also identifies significant gaps in real-world applications, particularly in terms of adaptability, cost, and inclusivity. These findings emphasize the need for more accessible, affordable, and real-time solutions that cater to the diverse needs of visually impaired individuals.
Authors - Renuka Sandeep Gound, Farhan Mujawar, Niraj Dhakulkar, Payal Rathod, Saish Bhise, Kavita Moholkar Abstract - With the growing interest in naturopathy and holistic health, it is more important to make information about medicinal plants easily accessible. The Virtual Healing Garden is designed to do just that, offering a dynamic platform where AYUSH students, professors, and plant enthusiasts can explore a variety of medicinal plants in a hands-on, engaging way. By blending traditional healing knowledge with modern scientific research, the platform brings these plants to life through interactive 3D models. Using cutting-edge algorithms like photogrammetry and 3D modeling, the garden creates realistic representations of the plants, giving users a detailed and immersive experience. The Virtual Healing Garden will feature a rich database of 3D plant models, each paired with detailed information about their medicinal properties. This will provide users with a visually immersive and informative resource, making it easy to explore and learn about various plants in a more engaging way, whether for education or research. This virtual garden not only raises awareness of the health benefits of medicinal plants but also makes learning about them interactive and fun.
Authors - Pankaj Chandre, Palash Sontakke, Rajkumar Patil, Bhagyashree D Shendkar, Viresh Vanarote, Dhanraj Dhotre Abstract - In today’s digital landscape, the prevalence of scams, phishing, and malicious attacks poses significant risks to both individuals and organizations. Mitigating these threats requires a comprehensive cybersecurity strategy that begins with user awareness and extends to robust protective measures and incident response protocols. By integrating education, proactive defenses, and responsive actions, personal and organizational cybersecurity can be greatly enhanced. Mitigating scams, phishing, and malicious attacks requires a comprehensive approach to cybersecurity and personal protection. This strategy begins with the User Environment, where devices connected to the internet become vulnerable to threats. Education and Awareness play a crucial role, providing training on recognizing phishing attempts and setting up reporting mechanisms to flag suspicious activities. Building on this, Protective Measures such as strong passwords, multi-factor authentication, regular software updates, and the use of security tools strengthen the defenses against cyber threats. Should an attack occur, Incident Response protocols are activated, including the detection and investigation of incidents, followed by recovery actions to restore security and prevent future attacks. By integrating these layers of defense, individuals and organizations can significantly reduce the risks of cyberattacks and safeguard sensitive information.
Authors - Swati Kiran Rajput, Sunil Gupta Abstract - Diabetes mellitus is the root cause of a disease known as diabetic retinopathy, which is a disorder that affects the retina. In every region of the globe, it is the leading cause of blindness. Early detection and treatment are very necessary in order to delay or avoid the deterioration of vision and the loss of eyesight. The scientific community has proposed a number of artificial intelligence algorithms for the aim of identifying and classifying diabetic retinopathy in fundus retina pictures due to the fact that this is the intended objective. Utilizing a Convolutional Neural Network (CNN), we suggested a method for the identification and early prediction of diabetic retinopathy in this particular piece of research. Using a wide variety of hyper parameters, such as epoch size, batch size, optimized, and so on, the Deep CNN has been used for both training and testing purposes. Examples of normal and abnormal retinal pictures have been included in the MRI dataset. When the findings of the experimental investigation were evaluated using machine learning and deep learning algorithms like SVM, ANN, and CNN, the results were shown to be accurate. In conclusion, the CNN achieves a detection and prediction accuracy of 96.60%, which is superior than that of the SVM and other artificial neural networks.
Authors - Nilesh Deotale, Nafiz Shaikh, Ashwin Katela Abstract - WhatsApp chats consist of various kinds of conversation held among two people or a group of people. This chat consists of various topics. This information can provide a lot of data for the latest technologies such as Machine Learning. The most important things for Machine Learning models are to provide the right learning experience which is indirectly affected by the data we provide to the model. This tool aims to provide in depth analysis of the data which is provided by WhatsApp. Irrespective of whichever topic the conversation is based on, our developed code can be applied to obtain a better understanding of the data. The advantage of this tool is that it is implemented using simple python modules such as pandas, matplotlib, seaborn, streamlit, NumPy, re, emojis and a technique sentiment analysis which are used to create data frames and plot different graphs, where then it is displayed in the streamlit web application which is efficient and less resources consuming algorithms, therefore it can be easily applied to larger dataset. The Accuracy of this project is 75%. In summary, this project makes use of state-of-the-art data analysis technologies such as scikit-learn, Topic Modeling, Named Entity Recognition, Clustering, Word Embeddings, Natural Language Toolkit, and more. Sequence-to-Sequence Models, Text Classification and so forth. Users can better understand how others communicate by using Language Model Fine-Tuning to extract relevant information from WhatsApp discussions.
Authors - Yogini Prasanna Paturkar, Amol P. Bhagat, Priti A. Khodke Abstract - Social media platforms generate vast amounts of interaction data, offering valuable insights into user behavior, preferences, and trends. However, the sheer volume and velocity of this data pose significant challenges for real-time analysis and computational efficiency. This paper proposes a framework for random sampling of social media interaction data to address these challenges. By employing probabilistic sampling methods, we aim to reduce data volume while preserving key statistical properties and minimizing information loss. The proposed methodology leverages stratified and weighted random sampling techniques to ensure the representation of diverse user groups and interaction types. Applications of this approach include sentiment analysis, trend detection, and user behavior modeling. Preliminary experiments demonstrate that random sampling can achieve a significant reduction in computational overhead while maintaining analytical accuracy within an acceptable margin of error. This framework has the potential to enhance data processing pipelines in fields such as marketing, public opinion analysis, and event monitoring, enabling timely and resource-efficient decision-making.
Authors - Riya Reddy, Ayoush Singh, Keshav Tanwar, Rashmy Moray, Shikha Jain, Sridevi Chennamsetti Abstract - This study examines the underlying drivers of usage intention of chatbots by bank customers using UTAUT model. This model assesses four key factors determining the usage intent of chatbots: performance expectancy, effort expectancy, social influence, and facilitating conditions. Primary data was obtained through structured questionnaire from retail banking customers. Structural Equation Modelling method is used and statistical tool SmartPLS 4.0 was employed to analyze complex associations between variables. The results show that performance expectancy, facilitating conditions, and motivation have a significant association with the usage intention of chatbots; however, effort expectancy and social influence have no connection. In addition, the results show that perceived security and trust are essential criteria for adopting a chatbot in banking. The study contributes value to the existing body of knowledge by proving the factor influencing the intent usage of chatbots in the banking industry. It also highlights future work directions in the form of long-term impacts and insights that can guide banks in designing customer-centric AI systems and improving chatbot services.
Authors - Soham Kulkarni, Suhani Thakur, Twsha Vyass, V. Yasaswini, Pooja Kamat Abstract - Dyslexia is a learning disorder that causes difficulty in reading and identifying relations between words and letters.[1] To improve reading accessibility within dyslexic patients, this study aimed to develop models for summarization of documents as well as provide a streamlined method to convert documents into dyslexia-friendly versions. Within this study, several summarization models were tested to generate effective text summaries, while defining Python functions to convert regular text into dyslexia friendly text. Models attempted with are: Term Frequency- Inverse Document Frequency Summarizer, Term Frequency-Inverse Document Frequency Summarizer with Support Vector Machine, and BART Transformer. After analysing the results, the BART Trained Summarization Model results are fruitful having a ROUGE R-1 F1 Score of 0.4510, a R-2 F1 Score of 0.2571 and a R-L F1 Score of 0.4177, ultimately successfully generating dyslexia-friendly summarized documents.
Authors - Mihir Deshpande, Hrishikesh Patkar, Madhuri Wakode Abstract - Intelligent Document Processing (IDP) automates extraction and categorization of information from unstructured and semi-structured documents. While standard Optical Character Recognition (OCR) computerizes the parsing of printed and scanned documents, so-called parsing tools like PyPDF2 and React-PDF take on the extraction of text from digital files. Emerging developments in IDP apply Large Language Models (LLMs) to enhance Natural Language Processing (NLP) to ensure the efficient interpretation, classification, and analysis of the extracted data. This paper provides a survey of IDP technologies with possible applications in financial and retail sectors-invoice processing, purchase order matching, and fraud detection. It also focuses on accuracy, scalability, and outlook of LLM-based IDP on the cloud, towards next-generation automation in more detail.
Authors - Sahana Balajee, Sakhi Saswat Panda, Anushruth Gowda, Animesh Giri Abstract - The extension of conventional Internet of Things (IoT) technologies from basic applications to advanced industrial processes has revolutionised operational efficiency, giving rise to a new class of IoTs called the Industrial Internet of Things (IIoT) systems. Despite these advancements, critical IIoT infrastructures remain exposed to a growing range of cybersecurity threats due to open vulnerabilities, stemming from the fact that these IIoT networks try to interconnect physical machinery with digital systems. Cyber attacks targeting IIoT systems attempt to exploit these vulnerabilities in order to cause severe operational disruptions and costly outages. These intrusions pose significant risks to data integrity, operational continuity, and total production, highlighting the need for effective cybersecurity measures in the industrial setting. To help prevent such intrusions this work proposes a unified predictive framework combining cyber attack detection with outage prediction to enhance resilience in IIoT environments. By leveraging machine learning algorithms, this framework analyses two types of data - network traffic for cyber threats and historical sensor data for forecasting the Remaining Useful Life (RUL) of critical components. This approach aims to identify risks and send alerts to minimise operational downtime preemptively, integrating predictive techniques for both cybersecurity and system reliability to strengthen IIoT systems, thereby helping industries maintain continuous, secure, and safe operations.
Authors - Sakshi Prakash Masand, Sadhana Shashidhar, Animesh Giri Abstract - High-stakes industries like the aviation industry demand minimal downtime requiring unified solutions that address all maintenance and troubleshooting needs. Existing solutions often function in isolation, requiring multiple systems for predictive analytics and manual-based repairs. To bridge this gap, this article presents a novel integration of machine learning based predictive maintenance and conversational AI for routine servicing and troubleshooting operations in industrial IoT systems, tailored to critical sectors such as aviation. A robust predictive maintenance model is built to predict the Remaining Useful Life (RUL) for aircraft components. Multiple traditional machine learning models like Random Forest, Support Vector Regression (SVR), XGBoost, and deep learning techniques like Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTM) are compared for performance and accuracy, ultimately focusing on a refined time series specific approach. When the predicted RUL of a component falls below a predefined threshold, operators are automatically alerted to schedule maintenance. For interactive support, Rasa, a customisable conversational AI framework and a fine tuned LLaMA model provide instant and enterprise specific guidance, offering step by step instructions and reducing reliance on lengthy manuals. This solution combines predictive maintenance with dynamic assistance, saving valuable time and resources for the industry.
Authors - P C Gita, Arthi R, R Geetha, Syed Hayath Abstract - Employee layoffs have significant emotional and psychological impacts, often reflected in public discussions on social media platforms like Twitter. Employee layoffs not only affect the employee at stake but also impacts the brand image of the company. This study explores the sentiments and emotions surrounding layoffs through a comparative analysis using three natural language processing (NLP) tools: TextBlob, VADER, and the NRC Emotion Lexicon. A dataset of layoff-related tweets was collected over six months, pre-processed, and analyzed for sentiment polarity and emotional tone. The analysis revealed predominantly negative sentiments, with emotions like anger, sadness, and anticipation being prevalent. While TextBlob and VADER effectively gauged sentiment, VADER performed better in handling informal language, and the NRC Lexicon provided a more nuanced emotional profile. The study highlights the psychological toll of layoffs and the importance of employer transparency in mitigating anxiety. Future research should consider advanced NLP models like BERT for improved sentiment detection and track the evolution of layoff-related sentiments over time..
Authors - Aarv Mankodi, Sanya Jain, Vedant Mundada, Dinesh Kumar Saini Abstract - Breast Cancer is a malignant tumor that occurs in the breast. It is a very serious threat to the health and well-being of women. Over the years the study of detection of this cancer using histopathology image recognition has become quite popular. Most of the methods, however, focus on creating new deep learning models or improving existing models like VGG or AlexNet. While these convolutional neural networks have been very successful in their implementation, it does not mean they do not have challenges, namely the problem of imbalance in datasets. It is for this reason that this paper instead tries to use graphs to solve the problem of breast cancer detection. For this paper, we use a graphical neural network that is trained to detect breast cancer in histopathology images. This is because the challenges faced by the deep learning models namely overcoming the imbalance in datasets may not be present in this graphical approach. It is also to find out if there are some previously unknown improvements in using a graph for detection instead of deep learning models.
Authors - Abhijeet G. Dhepe, Ashish R. Lahase, Shalini V. Sathe, Sagar Chitte, Pooja B. Abhang, Bharati W. Gawali, Sunil S. Nimbhore Abstract - This study introduces a novel crop diseases database design for Automatic Speech Recognition (ASR) systems. It aimed to predict crop diseases, addressing the critical role of speech technology in agriculture. The research bridges the gap between farmers and advanced diagnostic tools by creating a specialized voice corpus focused on agricultural terminology and disease names. The dataset, which includes various phrases related to crop health management, was collected in naturally noisy environments from farmers in the Marathwada region. Recordings captured the authentic speech patterns of both male and female participants, encompassing various dialects and accents. This approach ensures real-world applicability, enhancing the reliability and relevance of ASR systems. By including native and non-native speakers, the study promotes linguistic inclusivity. It aims to empower farmers with accessible, speech-based disease prediction tools, ultimately fostering greater efficiency and resilience in crop management.
Authors - Namrata Naikwade, Shafi Pathan Abstract - This paper presents a cost efficient and secure framework for data storage in cloud environments, It uses Discrete Cosine Transform (DCT) for data compression and Ciphertext-Policy Attribute-Based Encryption (CP-ABE) for fine-grained access control. The framework focuses on some of the main challenges in cloud storage, such as storage costs minimization, data privacy and security along with swift access revocation. Framework significantly reduces storage overhead by integrating DCT which compresses image data effectively while maintaining data quality. This storage optimization strategy enhances the cost-effectiveness of cloud storage making it suitable for large scale applications. CP-ABE provides a secure data access management by enforcing attribute based encryption which enables flexible and precise control over who can access the data ensuring privacy and security even in untrusted cloud environments. It also provides a rapid access revocation to safeguard data from risks associated with unauthorized access which is a critical risk in dynamic cloud environments. Experimental evaluations shows that the framework minimizes storage costs by up to 50% while significantly improving data security and efficiency demonstrating that proposed framework is a practical and scalable solution for secure and cost-efficient data management, addressing the needs of modern cloud-based systems.
Authors - Deep Dey, Hrushik Edher, Lalit M. Rao, Dinesh Kumar Saini Abstract - SSL is an important technique in machine learning which has high value for image classification tasks because acquiring labeled data is expensive. SSL trains on the labeled data and then we apply it predict the unlabeled dataset. Once it is trained, it can be further fine-tuned with the smaller labeled subsets for specific task. Recently, SimCLR, BYOL, and MoCo have shown impressive performances. SimCLR uses contrastive learning to train models by maximizing similarity between pairs classified under the same class and minimizing it between pairs belonging to different classes. BYOL, on the other hand, does not rely on negative pairs but implements a two-network architecture that eases training. MoCo develops a dynamic dictionary through which scaling SSL becomes efficient on massive data and performs well even with the smallest of batches. We can evaluate this models on CIFAR-10 and ImageNet, SSL approach is used in the areas where the labeled training data is scarce. In some research it is shown that SSL is competitive with SL when the amount of labeled training samples are small.
Authors - Prateeksha Chouksey, Tanvi Rainak, Piyush Shastri, Jayshree Karve, Ritesen Dhar Abstract - Identifying fishing spots is vital for sustainable fisheries management, conservation, and optimizing fishing activities. This paper provides a comprehensive review of Artificial Intelligence, Machine Learning, Big Data, and Data Analytics in enhancing fishing spot detection, surpassing the limitations of traditional methods such as fisher experience and historical catch data. These advanced approaches improve accuracy in locating productive fishing areas by integrating remote sensing, Geographic Information Systems, and real-time data from sonar and satellite imagery. The study emphasizes the role of environmental factors—such as water temperature, salinity, and ocean currents—in influencing fish distribution and explores the impact of technological advancements on eco-friendly fishing practices. Current challenges include data availability, environmental variability, and the need for interdisciplinary collaboration. Additionally, the paper outlines the transformative potential of these technologies to optimize resource utilization and preserve marine ecosystems, suggesting future research directions to address existing gaps. As these methods continue to evolve, their wider adoption is expected to support the sustainability of fisheries and environmental conservation significantly.
Authors - Arpita Mahakalkar, Karan Deshmukh, Kruthika Agarwal, Sudhanshu Maurya, Firdous Sadaf M. Ismail, Rachit Garg Abstract - Breast cancer remains a driving cause of mortality among ladies around the world, emphasizing the requirement for progressed location strategies. This considers creating a novel Computer-Aided Conclusion (CAD) framework leveraging MATLAB to progress the precision and proficiency of breast cancer location. The framework utilizes mammographic pictures and applies progressed measurable extraction procedures to analyze key characteristics, such as mass shape and edges. These highlights assist in classifying utilizing machine learning calculations, counting neural systems, and back vector machines. A one of a kind integration of wavelet change and multilayer perceptron strategies illustrated critical enhancements in recognizing Incendiary Breast Cancer (IBC) from non-IBC cases. The proposed approach beats conventional strategies, advertising improved symptomatic unwavering quality, decreased execution time, and tall exactness in cancer classification. This work underscores the potential of joining progressed machine learning procedures and picture-preparing apparatuses within the early and exact location of breast cancer, eventually supporting radiologists and decreasing demonstrative challenges.
