Authors - Lakshmi Priya G G, Padma Lakshmi G, Thomas Felix K Abstract - The conservation of natural habitats and the coexistence of humans and wildlife are vital for biodiversity preservation. In the Mudumalai Forest Conservation region of Tamil Nadu, India, achieving harmony between human activities and wildlife preservation presents a significant challenge. A framework for real-time monitoring of human-wildlife interactions leveraging a comprehensive integration of satellite imagery, Internet of Things (IoT) sensors, and deep learning techniques is discussed in this paper. The proposed system utilizes high-resolution satellite imagery to identify the hotspots, where human-wildlife interactions are most likely to occur or where conflicts are already prevalent. Deep learning algorithms are applied to analyze the satellite imagery data and detect patterns indicative of potential human-wildlife conflict areas. By training and continuously updating the models with real-time information, the system can accurately identify areas of heightened risk. Throughout the identified hotspots, IoT sensor networks are deployed strategically to monitor the real time human activities, wildlife movements, and predict the possibilities of human - wildlife interactions by employing light weight deep learning models. Based on the prediction, real-time alerts and early warnings with location are communicated via message and mobile apps notification to the relevant stakeholders for necessary actions. Moreover, by incorporating feedback loops, the system can adapt and improve its performance over time.
Authors - N. Mangaiyarkarasi, J. Arputha Vijaya Selvi, T. Pasupathi Abstract - Free Space Optical (FSO) communication systems offersultra high bandwidth, large data rate and very secure data transmission, making them a feasible solution for next generation communication networks. However, performance of the FSO communication system is greatly impacted by adverse atmospheric conditions such as heavy turbulence, rain, and fog, which set up errors and degrade the quality of the signal. In this paper an adaptive neural network-based approach is presented to mitigate errors under different seasons of atmospheric conditions. This method exploits a convolutional neural network (CNN) based architecture to predict and compensate the atmospheric induced distortions, thereby improving the performance of the FSO communication system. It significantly improves the Bit Error Rate (BER) and Signal-to-Noise Ratio (SNR). Real-time atmospheric data such as temperature (T in C), relative humidity (%), atmospheric pressure (Pa), and wind speed (ms-1) are collected and the CNN dynamically changes the parameters to optimize performance. Achieved result shows that the neural network model significantly improves the robustness and reliability of the communication system. This method triggers the way for more resilient FSO networks, which is more crucial for the implementation of 5G/6G and beyond communication infrastructures.
Authors - Shivani Kania, Yesha Mehta Abstract - Augmented analytics, managed by machine learning and natural language processing, handles data analysis findings, reducing the time-consuming pre-processing and feature development processes. The article focuses on the importance of Augmented Data Science (ADS), an interactive, data-driven system that combines personal judgement with analysis of statistics to improve decision-making in data interpretation. The challenges are developing the requirements for assessment, developing defined review methods, and comparing suggested methodologies to real-world datasets and use cases. The goal is to create and develop a model for data interpretation and natural language-based generated output in Augmented Analytics, with objectives including data processing, model design, query processing, and component analysis.
Authors - Saroj S. Date, Sachin N. Deshmukh, Mahesh B. Shelke, Daivat D. Sawant, Chatrabhuj B. Kadam, Kailas M.Ambhure Abstract - Analyzing text data in regional languages is essential for uncovering sociocultural insights. However, languages like Marathi face considerable challenges due to the scarcity of computational resources. Over the past few decades, Linguistic Inquiry and Word Count (LIWC) software has become a gold standard for text data analysis. It uses a dictionary-based approach to classify words into predefined psychological and linguistic categories, enabling researchers to explore aspects of personality, behavior, emotions, and social interactions. This research paper introduces the application of MR-LIWC2015 for analyzing Marathi text data. MR-LIWC2015 is a translation of the English LIWC dictionary into the Marathi language. In this paper, by leveraging a dictionary-based approach, MR-LIWC2015 analyzes psychological and emotional dimensions in textual feedback. Using a dataset of 185 student feedback entries, translated from English to Marathi, this research evaluates Marathi LIWC's efficacy by comparing its results with the original English LIWC. Findings indicate a high positive correlation between the two software, demonstrating the reliability of the Marathi LIWC. This advancement not only facilitates feedback analysis in Marathi but also opens avenues for diverse domains like sentiment analysis, mental health analysis, product review analysis, expressive writing analysis, etc. Future work aims to expand the lexicon set of Marathi LIWC and explore cross-domain applications. Furthermore, the software may be adapted for other regional Indian languages, marking MR-LIWC2015 as the first LIWC translation for any Indian language.
Authors - Hardik I. Patel, Dharmendra Patel Abstract - Higher education institutions' reputation, financing, and student achievement are all impacted by student retention, which has grown to be a major problem. In order to properly identify at-risk students, traditional methods to retention issues frequently lack the predictive capacity and flexibility required. In order to predict student retention rates, this study makes use of machine learning approaches, giving academic leaders useful information. The suggested approach builds a strong prediction model by combining a variety of information, such as financial, behavioral, academic, and demographic factors. The model finds important patterns and trends related to retention outcomes by using sophisticated techniques like gradient boosting and neural networks. A methodical procedure that includes feature selection, data preparation, and model assessment guarantees excellent accuracy and scalability. By employing Explainable AI technologies to make forecasts clear and actionable, the study also highlights the significance of interpretability. This method allows institutions to apply timely interventions, such academic help, counseling, or financial aid changes, by turning raw data into useful forecasts. The results show how predictive analytics has the power to transform retention tactics and promote an inclusive and effective educational system. This study offers a guide for incorporating machine learning into higher education's strategic decision-making process.
Authors - Dhanashree Joshi, Nilesh B. Korde, Pratibha Jape, Gayatri Newase, Mitali Gajbhiye, Maitriyee Kadam, Gayatri Gujar Abstract - Agriculture is a vital sector in many countries, especially in developing economies like India, where it contributes significantly to GDP and supports over half the population. The sector includes a wide range of crops, such as sugarcane, banana, apple, and pomegranate, which are crucial for food security and economic stability. However, the cultivation of these crops presents challenges, particularly in crop management and pesticide application. For instance, sugarcane fields are dense and tall, making it difficult for farmers to access all areas, and banana plantations in states like Tamil Nadu, Kerala, and Andhra Pradesh also have densely packed leaves that complicate manual spraying efforts. Similarly, apple orchards in hilly regions such as Himachal Pradesh and Uttarakhand, and pomegranate fields in Maharashtra and Karnataka, are challenging due to their dense canopies and rugged terrain. These obstacles make it difficult for farmers to spray pesticides safely and efficiently, and the presence of wild animals hiding within these fields adds an additional layer of risk. To address these challenges, advanced technologies like drone-based pesticide spraying systems are proposed. These drones, equipped with GPS, IoT sensors, and deep learning capabilities, can autonomously navigate dense crop fields and varied terrains. By targeting specific farm coordinates and adjusting their spraying patterns based on real-time environmental data, the drones ensure precise and even pesticide application. This technology not only enhances the safety of farmers by minimizing their exposure to hazardous environments but also improves the efficiency and effectiveness of pest control. As a result, the adoption of drone technology can reduce labour costs, increase crop yields, and promote sustainable farming practices in regions where traditional methods are inadequate and can improve efficiency of farming and reduce human load. Additionally, by taking drone in consideration while spraying pesticides in farming can improve overall efficiency.
Authors - Shoib Ahmed Shourav, Shahariar Sarkar, Salekul Islam Abstract - For developing countries, maintaining road network infrastructure is an essential concern. To strengthen a nation’s economy, road infrastructure must be maintained effectively. Potholes, speed breakers, and drain holes in roads are major reasons for causing accidents, traffic jams, and car damage. In addition to improving driving safety and minimizing vehicle damage and accidents, identifying road anomalies like potholes, speed breakers, and drain holes is essential for enabling authorities to effectively manage road maintenance. Self-driving cars need to be able to handle different road conditions. In this research, a custom dataset comprising potholes, drain holes, and speed breakers was developed. The study employs YOLOv11, a cutting-edge deep learning-based object detection model, to accurately detect these anomalies, including a comparison of road anomaly detection performance under daytime and nighttime conditions. The proposed approach achieved an accuracy of 83.8% on the daytime dataset, 81.6% on the nighttime dataset, and 84.4% on the combined dataset.
Authors - Madhusmita Mishra, R. Kanagavalli Abstract - This comprehensive survey examines advancements in semi-supervised learning (SSL) techniques developed to address imbalanced multi-class classification problems across a variety of real-world applications, including healthcare, fraud detection, and industrial monitoring. Traditional machine learning models often struggle with highly skewed data distributions, leading to biased predictions that favour majority classes while overlooking minority classes. SSL, which leverages both labelled and unlabelled data, has emerged as a promising approach, reducing the need for extensive labelled datasets while improving model generalization for minority classes. This review focuses on methodologies such as re-sampling, cost-sensitive learning, ensemble learning, hybrid techniques, active learning, and evolutionary algorithms, each offering unique approaches to mitigate the impact of class imbalance. Re-sampling methods, such as SMOTE (Synthetic Minority Over-sampling Technique) and its variants, augment minority classes by creating synthetic samples, addressing imbalances within SSL frameworks. Costsensitive learning introduces penalties for misclassifications, improving sensitivity to minority classes, while ensemble learning methods, like bagging and boosting, combine multiple classifiers to enhance predictive accuracy in multi-class settings. Additionally, hybrid techniques that integrate re-sampling with cost-sensitive approaches show promise in balancing class representation and boosting model robustness. Active learning, which iteratively selects the most informative samples, and meta-learning, which enables models to adapt dynamically to different class distributions, provide further innovation in tackling imbalances in SSL applications.
Authors - Khush Mendiratta, Shweta Singh, Pratik Chattopadhyay Abstract - Early detection of brain tumors through magnetic resonance imaging (MRI) is essential for timely treatment, yet access to diagnostic facilities remains limited in remote areas. Gliomas, the most common primary brain tumors, arise from the carcinogenesis of glial cells in the brain and spinal cord, with glioblastoma patients having a median survival time of less than 14 months. MRI serves as a non-invasive and effective method for tumor detection, but manual segmentation of brain MRI scans has traditionally been a labour-intensive task for neuroradiologists. Recent advancements in computer-aided design (CAD), machine learning (ML), and deep learning (DL) offer promising solutions for automating this process. This study proposes an automated deep learning model for brain tumor detection and classification using MRI data. The model, incorporating spatial attention, achieved 96.90% accuracy, enhancing the aggregation of contextual information for better pattern recognition. Experimental results demonstrate that the proposed approach outperforms baseline models, highlighting its robustness and potential for advancing automated MRI-based brain tumor analysis.
Authors - Ninaad Nagaraj Yeligar, Rajesh Prakash Unakal, Soumya H Hooli, Prerana Girish Karoli, Kiran M R, Suneeta V Budihal Abstract - The work aims to address the need for efficient energy management by implementing a smart energy meter using LTE technology. We are developing a smart energy meter system capable of accurately measuring and transmitting energy usage data. This work features easy hardware implementation using a ESP32, a cost-effective microcontroller and a LTE module to establish a connection for transmitting data. The system utilizes LTE technology to ensure reliable and long-range communication. Energy consumption data is then monitored and the captured which could be used in future for different purposes. This work provides a comprehensive and user-friendly solution for continuous monitoring and management of energy consumption. The LTE system’s ability to capture real-time energy usage data enhances accessibility and facilitates data-driven decision-making. This innovative solution is ideal for applications where efficient energy management is crucial, such as in residential, commercial, and industrial settings.
Authors - Syed Abidhusain, Baswaraj Gadgay, Shubhangi D C Abstract - A notable problem in the contemporary expanding networking paradigm is the congestion resulting from the substantial volume of data transmitted between nodes in any network. During congestion, packets may become damaged or lost. By evaluating the relationships among various network QoS measures, we may identify and address congestion issues that occur in such scenarios. This study use singular value decomposition (SVD) to mitigate congestion issues. Singular Value Decomposition (SVD) is a matrix factorisation method utilised in diverse applications, such as principal component analysis (PCA) and linear regression. We employ an innovative method known as SVDULR, which utilises singular value decomposition and linear regression to efficiently identify congestion and improve service quality.
Authors - Ajay Singh, Ambu Sharma, Pawan Kumar, Sanjay Taneja, Mukul Bhatnagar Abstract - Leveraging the unparalleled adaptability and hierarchical feature stratification capabilities of deep learning, this study constructs a sophisticated framework for fraud detection, seamlessly integrating convolution and recurrent neural architectures with advanced anomaly detection algorithms to decode complex, non-linear transactional patterns within heterogeneous financial datasets, thereby enabling real-time fraud identification while addressing pivotal challenges of algorithmic interpretability, adversarial resilience, regulatory compliance, scalability, and data confidentiality, ultimately redefining the paradigm of automated financial security in an increasingly digitized global economy..
Authors - Nadiya Hoque Shudha, Fatema Tuj Johura, Anupam Singha, Kingkar Prosad Ghosh Abstract - Sign language is vital for effective communication among deaf individuals, helping them to connect with others and break down communication barriers, which improves their overall well-being. For those with hearing impairments, knowing sign language is key to facing these challenges. While sign language is not used everywhere, sign language recognition has gained significant attention in computer vision and deep learning, aiming to improve this communication method for broader use. This paper introduces a convolutional neural network model designed to identify images of Bengali sign language gestures for native speakers through image classification. The model was trained using two publicly available datasets: one for one-handed Bengali sign language with 30 different sign alphabets and another for two handed Bengali sign language with 36 distinct sign alphabets. Various image preprocessing methods including gamma correction, grayscale conversion, CLAHE, resizing and normalization were used to enhance the datasets, making the model more robust. The results were impressive, with the model achieving 98.58% accuracy on the one-handed dataset and 94.86% on the two-handed dataset. These results show the model’s effectiveness in classifying Bengali sign alphabets, which could improve communication access for the hearing-impaired community.
Authors - Ravi Kumar Suggala, B Hema, B Naga Jahnavi, Ch Anannya Sai, Ch Saumya Prasanna, J Hari Keerthi Abstract - The basic modeling strategies of infectious diseases have become particularly important, especially during the times of the COVID-19 crisis. Although GNNs achieve remarkable accuracy in mimicking inter-regional interactions using spatiotemporal information, they infrequently capture the causal factors that govern the spread of epidemics. In an attempt to fill this research gap, this study puts forward the Multi-Relational Graph Attention Depth wise Separable Convolutional Neural Network with Satin Bowerbird Optimization Algorithm (MRGADSCNNet-SBOA). Both DSCNN and MRGAT components of the model are used to model temporal and spatial correlations of epidemic data. DSCNN can capture spatial interconnections well while adjusting node attributes through graph attention based on the relations that existed between neighboring areas even though it only has relatively few parameters for temporal relations. Here, the result reflects the model with the help of improved Satin Bowerbird Optimization Algorithm (SBOA) with the RMSE 1231, MAE 13823, MAPE 10.24%, PCC 96.42% and CCC 98.91%. Due to its high reliability, the model offers a reasonable instrument for explaining epidemics in time and space.
Authors - Huzaib Shafi Shah, Rajendra Gupta Abstract - 5G has introduced unprecedented speed, reliability, and low latency in telecommunications. 6G is expected to enhance these capabilities further. OpenRAN architectures for these networks offer notable cost efficiency and operational flexibility advantages but introduce greater complexity in deployment and orchestration (Azariah et al., 2024). This study critically evaluates four OpenRAN orchestration systems across five essential factors. The findings reveal limited support for multi-vendor interoperability and significant variability in the systems' practicality and coverage of use cases. While some systems focus on specific RAN functions, others adopt a more holistic approach. Furthermore, inconsistent key performance indicators (KPIs) complicate direct comparisons.
Authors - Parambrata Sanyal, Mukund Kuthe, Sudhanshu Maurya, Ajay Kumar, Firdous Sadaf M. Ismail, Rachit Garg Abstract - The integration of artificial intelligence (AI) in healthcare and its significant rise has gained attention in recent days. The rapid and notable advancements in AI technologies and their co-domains have truly aided the healthcare sector with its praiseworthy way of shaping and serving healthcare that is beneficial for society. From disease diagnosis, doctor assistance, and patient assistance to healthcare machinery advancements, AI, along with its codomains such as the Internet of Things (IoT) and robotics, have played a crucial part in aiding society. However, with the unprecedented benefits, AI comes with different challenges. The evident and immense potential of AI and its co-domains can be highlighted, but its ethical and responsible use should be underscored and ensured. This paper aims to navigate ethical and technical considerations, ultimately leading to a revolutionized impact that is ethically grounded and innovative at the same time by responsible AI use. Addressing these challenges can bring out the full potential of AI and enhance the overall patient treatment outcomes along with the quality of care. This paper also systematically reviews all the AI-driven advancements in the recent past and their overall performances, which different stakeholders and users underscore.
Authors - Sidra Zaidi, Nandini Paliwal, Riya Baby, Amala Siby Abstract - Smart healthcare systems, powered by artificial intelligence (AI), enables drastic shift in healthcare sector. The benefits of data driven solutions anchored in advanced disease diagnosis, efficient administrative records management and operational efficiency. AI algorithms, including machine learning and deep learning, enable real-time data analysis from diverse sources such as Internet of medical Things (IoMT), wearable devices, and medical imaging. This facilitates early disease detection, personalized treatment plans, and predictive analytics, leading to improved health outcomes. Additionally, AI-powered systems optimize resource management, reduce human error, and streamline administrative tasks. While challenges such as data privacy, integration complexities, and ethical concerns exist, the potential of smart healthcare systems to revolutionize healthcare delivery is undeniable. This study explores the role of AI in advancing smart healthcare, focusing on the integration of data-driven technologies to create a more efficient, accessible, and patient-centered healthcare ecosystem.
Authors - Nomusa Vumisa, Hendrik Pretorius, Marie Hattingh Abstract - Persuasive features in video games play an important role in encour-aging continuous indulgence and behavior change. Research has been conducted to investigate the different motives, game design elements and features that en-hance the gaming experience. However, there is a gap in understanding how per-suasive features in video games have an impact on individuals, resulting in be-havioral changes. An understanding of the role each feature plays in a video game is crucial for the successful creation and design of a video game aimed to “per-suade” players to change their behavior. This paper presents a systematic review that covers 30 publications and is aimed at investigating the persuasive features in video games and further providing a classification of those features. The results of the study provide a guide to the main theories on behavior change and a clas-sification of the identified persuasive features. Additionally, this study provides a reference for video game designers and developers to utilize when undertaking persuasive projects.
Authors - Sahil Thakur, Saloni Mahadule, Palash Singh Chandel, Sudhanshu Maurya, Firdous Sadaf M. Ismail, Rachit Garg Abstract - As CCTV technology has continued to mature quickly, important and fundamental questions about secure, scalable and transparent storage are posed. Most traditional stored-concentrated models can have challenges on the data’s integrity since other unauthorized users may easily manipulate or delete the video data. This paper explores the design of a decentralized CCTV surveillance system and with motion detection and preprocessing and cloud computing technology. Focused on motion detection, video frames are recorded and compressed with the help of OpenCV and then stored on AWS S3 for further instant access and in AWS Glacier as final storage. Each of the defined operations—upload, deletion, and modification—of the stored video frames is logged transparently on the Ethereum block chain. AWS provides scale and security to the cloud, and block chain provides for the possibility of non-tamperable records. This architecture does not only secure the video data from other people’s violation as well as prevent themselves from being permanently erased but also manage massive video data. The findings presented here show that integration of AWS cloud services with block chain could provide a highly secure, scalable, and transparent solution for today’s CCTV systems.
Authors - Shailaja B. Jadhav, D. V. Kodavade, Suhasini S. Goilkar Abstract - Data centric applications are increasing worldwide, inspiring data scientists to devise more sophisticated methods capable of modelling highly dynamic, extremely speedy data. There are existing approaches which adopt concept learning, dynamism, combining different approaches and heterogeneous classifiers. But, very few of them consider real time data generated through live data savvy applications. This necessitates Streaming data analytics as emerging area of research traditional data mining is not sufficient to achieve desired efficacy. This research aims to focus on streaming data classification particularly flight stream data and presents a comprehensive design framework of multi-layered ensemble built through pool of classifiers selected with prequential evaluation. The model is experimented with various known platforms of streaming data analysis like scikit multiflow, MOA etc. through systematic experimental work. Also, considering the volume of streaming data the experiments have also utilised GPU environments and Google TensorFlow wherever necessary. This Research addresses data streaming analytics majorly, as it needs more attention from research community. There is still scarcity of established benchmarks and standardized frameworks. Major observations, evaluation of design finds that the designed model is able to capture the dynamic nature and improves the classification accuracy as compared with that of the available traditional ensemble models.
Authors - S. P. Vibhute, S. C. Patil, S. A. Bhisikar Abstract - The relentless advancement of wireless communication technologies has ushered in the era of 6G networks, necessitating innovative strategies to enhance their energy efficiency without compromising performance. This study addresses the critical need for sustainable and efficient 6G networks, particularly in the context of growing environmental concerns and escalating energy demands. Existing models, while foundational, often fall short in optimizing energy consumption, grappling with issues such as high latency, increased complexity, substantial costs, and limited scalability. To bridge this gap, our work systematically reviews and analyses various models, including Sleep Scheduling and Intelligent Routing, among others, to augment the energy efficiency of 6G networks. The review process is comprehensive and multidimensional, comparing these models across key performance metrics such as delay, complexity, cost, energy efficiency, and scalability. By employing a meticulous and structured approach, this study elucidates the strengths and limitations of each model, providing a holistic understanding of their applicability in real-world scenarios. The implications of this work are far-reaching, offering invaluable insights for stakeholders in the wireless communication domain. It equips practitioners and researchers with empirical evidence to identify and implement the most optimal models, thereby significantly enhancing the efficiency and sustainability of 6G networks. The findings from this study are poised to contribute substantially to the development of more robust, energy-efficient, and scalable wireless communication systems, aligning with the global drive towards sustainable technological advancements.
Authors - The Tung Than, Thi Phuong Nhi Le, Dong Thanh Vo, Minh Son Nguyen Abstract - Object tracking is crucial in computer vision, particularly in robotics, but Visual Object Tracking (VOT) faces significant challenges, with occlusion being the most critical. Occlusion disrupts tracking accuracy and poses difficulties when integrating VOT algorithms into embedded robotic systems due to computational and real-time constraints. To address this, we propose a robust method tailored for resource-limited systems, combining the Kernelized Correlation Filter (KCF) and Kalman Filter (KF). By leveraging the Average Peak-to-Correlation Energy (APCE) index, our method detects occlusion, dynamically adjusts the model’s learning rate, and improves performance under challenging conditions. Experimental results on the OTB-100 benchmark highlight our tracker’s effectiveness in handling occlusion, achieving a success rate of 0.602. This demonstrates the method’s robustness under challenging conditions while maintaining real-time processing at 30 FPS (Frame per Second) on Jetson Nano, making it an ideal solution for embedded robotic systems.
Authors - Pulipaka Hrishitha, Hima Atluri, Kovvur Ram Mohan Rao Abstract - In this study, we evaluate two distinct chatbot models integrated into a comprehensive healthcare platform, with a focus on addressing medical and mental health inquiries. The chatbot, driven by LLAMA 2, equipped with an inbuilt Retrieval-Augmented Generation (RAG) mechanism, specializes in retrieving and generating precise responses to medical queries and is tailored to offer personalized and empathetic support for mental health concerns. Through meticulous analysis, we assess the effectiveness of these chatbots against a spectrum of functional and non-functional requirements, encompassing usability, security, scalability, accuracy, and empathy. Our investigation delves into LLAMA 2's performance across four distinct scenarios related to mental health inquiries. These scenarios involve variations in fine-tuning and the provision of custom prompts to the chatbot. We scrutinize LLAMA 2's responsiveness in both finetuned and non-fine-tuned states, as well as with and without custom prompts, aiming to discern the impact of these optimization strategies on the chatbot's capacity to deliver empathetic and supportive responses. Our findings provide valuable insights into the nuanced intricacies of LLAMA 2's role in mental health support within AI-driven healthcare solutions, offering implications for further development and refinement in this critical domain.
Authors - V.S.N. Murthy, S. Sai Nikitha, T. Nithya Sri, P. Sushma, V. Kavya Harshitha, M. Hema Lalitha Abstract - Users today often have to navigate multiple websites to find information that matches their preferred level of understanding and format, whether it's text, audio, or video. This fragmented approach can be time-consuming and frustrating, especially when seeking information suited to specific needs. The challenge is to find a streamlined solution that provides comprehensive, and customizable content in one application eliminating the need for users to jump from one site to another. Our project resolves this issue by developing a unified web-based application that allows users to input a topic and select their preferred content format and level of understanding. The platform uses LLM to generate detailed information and seamlessly convert it into the chosen format, whether it be text, audio, or video. This integrated approach ensures that users receive the information they need in their desired format, all in one convenient location. By simplifying the process, our platform provides a more efficient and user-friendly way to access information suited to their preferences, making it easier for users to learn.
Authors - Lakshya Khanna, Shriniwas Mahajan, Varun Kadu, Sudhanshu Maurya, Firdous Sadaf M. Ismail, Rachit Garg Abstract - Sales forecasting assumes a significant part in essential navigation and asset allotment for organizations across different businesses. Realizing the patterns can change and help in the plan of market procedure, particularly now, when the Electronic Vehicles (EV) market is at its pinnacle. In this paper, we investigate the utilization of measurable models and some high-level AI procedures, specifically Random Forest and Long Short-Term Memory (LSTM) models, for anticipating sales information patterns. The review plans to assess the exhibition and dependability of these models in estimating sales data, utilizing genuine world datasets spreading over several years. Execution assessment of the models is led utilizing measurements like Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared. Also, stability analysis is performed to evaluate the unwavering quality of each model in catching and foreseeing exact patterns. The discoveries of the exploration feature the viability of the measurable models and ML models in anticipating sales data patterns. The two kinds of models show promising execution, with the LSTM model areas of strength for displaying in catching transient conditions and long-haul designs in the sales data. In any case, contrasts in execution and strength between the models are noticed, giving important experiences to choosing the most appropriate determining approach in view of explicit business prerequisites.
Authors - Ravi Kumar Suggala, Penumala Syamya, Pokuri Venkata Naga Rohitha, Nunna Reshma Sri Hanu, Vuyyuri Gnana Prasuna, Vegesana Naga Sai Pujitha Abstract - Dental cavity identification using advanced image processing and machine learning techniques, especially through X-rays, plays a crucial role in early diagnosis and treatment planning. Traditional detection systems often suffer from high error rates and low accuracy. To address these challenges, a sophisticated model based on Riemannian Residual Neural Networks with Improved Sooty Tern Optimization (RR2Net-ImSTOpt) is proposed. The model uses the DENTEX dataset for analysis, incorporating noise reduction and image enhancement using Guided Box Filtering (GBF). Feature extraction is performed using the Inception Vis-Transformer, followed by optimization of RR2Net's weight parameters via the Improved Sooty Tern Optimization Algorithm. This approach achieves impressive results with a recall of 99.8% and an accuracy rate of 99.9%, surpassing current methods in accuracy and reducing false positives. RR2Net-ImSTOpt’s capability to handle large medical datasets makes it an ideal solution for clinical dental cavity detection, enhancing diagnostic efficiency and precision.
Authors - Aisha Karigar, Mohammed Qadir Ternikar, Harsh Nesari, Vanashree N, Prema T. Akkasaligar Abstract - Problematic Internet Use (PIU) is a growing concern, especially among adolescents, with significant impacts on mental and physical health. This study aims to predict the severity of PIU, measured by the Severity Impairment Index (SII), using a combination of physical activity, demographic, and behavioral data. Machine learning models, including XGBoost, CatBoost, TabNet, and LightGBM, were employed to classify participants into SII categories: none, mild, moderate, and severe. Data were sourced from the Healthy Brain Network (HBN) dataset, which includes accelerometer data, internet usage, fitness assessments, and physiological measures from over 3,000 participants aged 5 to 22 years. Key feature engineering steps included creating interaction terms (e.g., BMI × Age) and applying Autoencoders for dimensionality reduction on the high-dimensional actigraphy data. The results indicated that CatBoost performed best in predicting minority SII categories, handling imbalanced data effectively. XGBoost and LightGBM demonstrated stable performance, while TabNet provided interpretability but lower overall predictive power. Evaluation metrics, particularly Quadratic Weighted Kappa (QWK), were used to assess model performance, with QWK offering insights into the ordinal nature of misclassifications. This study highlights the value of combining physical activity and behavioral data in predicting PIU severity. The findings underscore the potential of machine learning in identifying individuals at risk for severe PIU and suggest avenues for future interventions to reduce the negative impacts of excessive internet use.
Authors - Sneha Singh, Deepak Kaushal, Bhupinder Preet bedi, Sanjay Taneja, Pawan Kumar Abstract - An ever-increasing number of individuals from all over the world are devoting a significant portion of their time to activities that take place in the digital realm, such as communicating with one another and looking for information. There is no denying the fact that social media platforms, which include Facebook, Twitter, sites like Instagram, and video sharing platforms like YouTube, play a vital role in the day-to-day lives of individuals, thereby altering the way in which people go about their routines. Over the past few years, electronic word-of-mouth communication, often known as eWOM, has seen a significant surge in popularity. Accordingly, the purpose of the study is to gain an understanding of the current situation regarding eWOM and social networks by means of a comprehensive review of the relevant literature. A comprehensive selection of 100 research studies was obtained from Scopus. The findings will offer a new direction to academicians in the future.
Authors - Akash K, Joseph Jilvis J, Felicia Lilian J, Subhashni R Abstract - This paper focuses on a voting system which is on a blockchain technology platform. To address issues that are known to be present in voting, it employs decentralized applications of ethereum known as dApps. Some of the contemporary matters raised include fraud as well as complexity. The proposed dApp is based on the use of smart contracts, as well as two-factor authentication through Metamask. A number of features might be noted. For example, one of the services provided by the system is event coverage such as the elections results. There is also a Voter Analysis Report Feature. This particular feature provides information on demography and the voting behaviour and it best viewed in pie chart. This dApp employs technologies including HTML, CSS, JavaScript & solidity in the process of its development. All in all, it seeks to enhance integrity, accessibility and transparency of the voting system. By doing this, it intends to increase trust and openness in elections more effectively.
Authors - Eshwari Khurd, Tushar Nasery, Rupesh C. Jaiswal Abstract - Data storage and applications have observed a large shift from being stored and used in local drives just a decade ago to being almost entirely cloud dependent today. This change in usage has brought about new challenges to be dealt with. Traditional security solutions were developed keeping in mind the use case for local storage. Techniques like cryptography have evolved to be more adaptive and secure. Yet, time after time, it has been proven that they can be broken. However, this is no longer adequate as the working and use of cloud networks is vastly different than local storage devices. Thus, new solutions need to be developed in order to secure this already established pattern of data consumption.
Authors: Shailesh T. Khandare, Nileshsingh V. Thakur Abstract: Image segmentation is the key and important process in the image analysis. In general, thresholding technique is used for the grey level image segmentation and when it comes to apply for the color images, the RGB color image is separated in three grey level planes and then it is applied on these grey level planes or else the color image is directly converted to grey level image and then it is applied on this converted grey level image. This paper addresses the issue of computation time requirement to carry out these three grey level plane image segmentations through the generation of grey level image without using any inbuilt function of tool or platform. The data fusion approach is proposed which is based on the trichromatic coefficients. A single grey level image is formed from the available IR, IG, and IB grey level planes using the trichromatic coefficients. Obtained results are compared on the basis of bilevel and multi-level thresholds. Otsu bilevel threshold of obtained grey level image differs with the Otsu bilevel threshold of converted grey level image by 11 %. Obtained grey level image by proposed approach is visually near about similar to converted grey level image. Error between the thresholded images of proposed approach and converted image is less. Obtained multi-level threshold values are close with the multi-level threshold of converted image.
Authors - Rajni, Parminder Kaur, Harmandar Kaur Abstract - The current world is run by technology and network connections, which are indispensable parts of day-to-day life. Corporate organizations, the military, and the government have adopted automation, and computers connected to the network are being used for the storage and sharing of vital, highly confidential, and valuable information. Hence, is essential to prevent the attackers from exploiting the vulnerabilities for illegally accessing the crucial data. With increased dependency on the internet owing to the proliferation of technologies such as cloud computing, the Internet of Things (IoT), wireless communication, and social media networks, high security is required in cyberspace. Cybersecurity provides the methods used to protect sensitive information in cyberspace. Disturbed denial of services (DDoS), phishing, man-in-the-middle (MiTM), passwords, SQL injection, Cross-site scripting, malware, and drive-by download are a few types of cyberattacks. Traditional methods such as firewalls, intrusion detection systems, antivirus software, access control lists, etc., are no longer productive in detecting new generation attacks. Therefore, there is an urgent need to design new methods to prevent these sophisticated cyberattacks. This paper explains the main reasons for cyberattacks and reviews the various types of cyberattacks, their vulnerabilities, detection and prevention techniques. To prevent current and future cyberattacks such technologies as machine learning, cloud platforms, big data, and blockchain can play an important role. The solutions provided by these technologies may assist in detecting malware, intrusion detection, spam identification, DNS attack classification, fraud detection, recognizing hidden channels, and distinguishing advanced persistent threats, enhancing the overall defense against sophisticated cyberattacks.
Authors - Shubham Garg, Kanika Monga, Nitin Chaturvedi, S. Gurunarayanan Abstract - Approximate computing has emerged as a promising paradigm for error- tolerant AI/ML applications deployed on energy-constrained edge devices. It has gained significance for edge devices due to its potential to reduce power consumption. In conventional computing systems, implementing computationally intensive machine learning algorithms results in large power consumption. Addressing this challenge, the complexity of hardware computing units can be reduced by optimizing the circuit logic while slightly trading off the computational accuracy. This technique is termed as Approximate computing where the circuit provides close-to-accurate results rather than precise results with significant reduction in power consumption. Therefore, in this work, we propose two approximate adder configurations that utilize novel logic optimization techniques to lower the power consumption and the hardware complexity of the circuit. The proposed approximate adders are designed using 55 nm technology and evaluated based on power consumption, delay, area, and power delay product (PDP). The simulation results indicate a reduction of 46.9% and 57.21% in power consumption for the approximate adder-1 & adder-2 compared to the conventional full adder. Furthermore, to validate the reliability of the proposed design, we also evaluated and calculated the accuracy metrics in terms of mean error distance (MED) of 0.25, which reflects the error tolerance of the proposed design.
Authors - Parambrata Sanyal, Mukund Kuthe, Sudhanshu Maurya, Sushmit Partakke, Firdous Sadaf M. Ismail, Rachit Garg Abstract - The most important public health challenge of myocardial infarction is caused by the obstruction by cholesterol and plaque accumulation in arteries, resulting in morbidity and mortality across the globe, especially in low and middle economies that lack health services, preventive measures, and early detection facilities. This study seeks to support the development of effective strategies by proposing a stacking ensemble model for timely forecasting and treatment of this disease in a serious way to improve healthcare significantly around the globe. The proposed methodology has been implemented on a retrospective dataset acquired from IEEE Dataport. The methodology involves normalization and standardization of the dataset, ensuring uniformity so that the machine learning classifiers work well. Our research compares several widely used machine learning classifiers, including Support Vector Machines (SVM), Gradient Boosting (GB), and Naive Bayes (NB), whose hyperparameter tuning has been done by grid search CV (GCV). The proposed stacking ensemble model stacks Light Gradient Boost and Cat Boost algorithms after being hyper-tuned by the Particle Swarm Optimization technique to enhance the overall predictive capacity. The results demonstrate that the proposed stacking ensemble model surpasses the individual classifiers in metrics, including the F1 score, recall, accuracy, and precision that are considered in this paper. Future directions of the research would be to work on expanded datasets and, most importantly, increase population diversity, add clinical parameters, and instead utilize more sophisticated machine learning techniques.
Authors - Mirza Zuber Baig, Vivek Nainwal, Anoop kumar, Bharat Kumar Abstract - Quantum computer simulators play a crucial role in understanding and analyzing the behavior of quantum systems. However, simulating large-scale quantum systems over classical machines can be computationally expensive and time consuming, limiting the practicality of many quantum algorithms. In this research paper, we explore the methodology employed for accelerating indigenous density-matrix based quantum computer simulator by using state of art libraries for GPUs (Graphics Processing Units) effectively increasing the number of Qubits it can simulate. The paper discusses the methods and techniques employed to identify computationally intensive and time-consuming functions within the simulator. By analyzing the profile results, we identified specific functions that required significant computational resources. To accelerate these functions, we utilized GPU acceleration techniques, leveraging parallel processing power. Our study demonstrates a significant improvement in simulation speed, achieving a significant speedup, showcasing the effectiveness of GPU acceleration in quantum computer simulations.
Authors - Ravi Kumar Suggala, Khushi Kumari, Mathi Gayathri, Koppisetti Deepika Naga Sree, Nekkalapudi Gayathri, Suma Kadali Abstract - Malware file production grows rather actively, which is explained by the development of digital structures. The proliferation of cyber trends poses severe security challenges due to the increasing complexity of attacks. These files could be difficult to detect when they share characteristics with normal files or if they are altered. Internet of Things (IoT) networks put a probability of vulnerability akin to Mirai malware to cyberattacks. There is a need to develop complex procedures for top security since it is important for such networks. This paper presents a new framework of preprocessing techniques, feature selection, and classification for predicting Mirai malware IoT security attacks. The preprocessing part uses the Global-Local Depth Normalization (GLDN) of features for dissolving noise and for better normalization of feature depths to enhance the learning factor. Practical feature selection is performed by using a combination of Gooseneck Barnacle Optimization (GBO) and Human Memory Optimization (HMO). This hybrid makes an intelligent dimensionality reduction decision determined by choosing appropriate features from among the set by the right balance between exploration and exploitation using biologically inspired optimization algorithms. For classification, there is proposed a Stereoscopic Scalable Quantum Convolutional Neural Network (sQCNN) that applies quantum computation principles to enhance computational scalability at the quantum level. The Banyan Tree Growth Optimization (BTGO) algorithm can optimize the classifier with high accuracy and attack detection immunity. The concept of Banyan tree growth in a hierarchical structure is similar to the classifier structure. Experiments conducted on the N-BaIoT dataset successfully prove the idea behind the proposed approach. The results propose that the new methods ensure better results over the traditional methods concerning the achieved accuracy of 99.67% and precision of 99.61%, while also incorporating reduced computational over- head. This new framework is a major step forward in defending IoT networks against current emerging threats, stressing the collaboration of preprocessing, feature selection, and quantum learning.
