Authors - Nisarg Dobariya, Rutik Dobariya, Rikita Chokshi, Sarita Thummar Abstract - The transition from traditional to smart grids has been driven by the pursuit of greater efficiency, reliability, and consumer engagement. While smart grids offer numerous benefits, they are vulnerable to cybersecurity threats. Intrusion detection systems (IDS) are indispensable tools for safeguarding smart grid operations by identifying and preventing malicious attacks. This research investigates the application of various IDS models, classifiers, datasets, and algorithms in smart grid environments. The study underscores the importance of using datasets specifically designed for smart grid networks to ensure accurate and reliable IDS performance. Moreover, the research demonstrates the potential of distributed approaches and advanced algorithms in enhancing IDS capabilities, thereby bolstering the security and resilience of smart grid infrastructure.
Authors - Revathy P, Rakshana A, Tinu A V, Vijayakumar R Abstract - The increasing demand for efficient package delivery has led to a challenge in detecting food spills during transit. Traditional methods rely on manual inspection, which is time-consuming and prone to human error. This study proposes an AI-based approach using Convolutional Neural Networks (CNNs) implemented with TensorFlow to detect both damaged packages and spilled food packets. The model is trained on a large dataset of package and food packet images, learning key features indicative of physical damage and identifying food spills. By fine-tuning pre-trained CNN architectures, the model achieves high accuracy in detecting both damage and spills. The interface is attached with an alert mechanism that notifies when damage or spill is detected. The TensorFlow framework is used for building, training, and deploying the model efficiently. The proposed system aims to automate package and food packet inspection, reduce human labor, and improve delivery service reliability by providing fast and accurate damage and spill detection.
Authors - Smita Mahajan, Archana Chaudhari, Ameysingh Bayas, Devika Shrouti Abstract - Drowsiness is a critical issue that contributes to a significant number of accidents in various scenarios, such as driving and hazardous work environments. Existing drowsiness detection projects often rely on subjective measures and single modality detection, leading to limited accuracy and applicability. This research proposes a drowsiness detection system that employs deep neural networks and machine learning-based object detection techniques to overcome these limitations. The ability of the recent drowsiness detection systems to reliably and impartially detect drowsiness is restricted. The proposed model uses computer vision and machine learning algorithms to identify drivers' drowsiness based on facial attributes like eye movement monitoring. The model aims to improve the accuracy and reliability of drowsiness detection by combining multiple modalities. The implementation includes using the Keras library, which is required for a Convolutional Neural Network (CNN) architecture. The model is trained on a customized dataset of facial images with open or closed eyes labels. The CNN discovers the complex relationships and features from the data, classifying drowsiness critically. The proposed drowsiness detection system's results demonstrate an optimistic accuracy of 98.88%. The system signals real-time alerts when the drowsiness in the behavior of the driver is caught, potentially averting accidents and enhancing safety. This technique suggests an accurate and trustworthy approach for detecting drowsiness in different domains, including driving and unsafe work environments, with 98.88% accuracy. This system can be a valuable means for improving safety and controlling the accidents caused by driver drowsiness.
Authors - Aswathkrishna S D, M. Sujithra Abstract - In today's rapidly evolving world, recognizing student food choices is crucial. This study explores food choices and how they align with areas containing restaurants and grocery stores. Clustering techniques including K-Means, Hierarchical Clustering, and DBSCAN were employed with the silhouette score used to validate and determine the most effective method for analysis. Based on food choices data sourced from Kaggle and location data from the Foursquare API, the research provides location recommendations for students. Suggestions guide students to areas that align with their food choices aiming to enhance their overall experience.
Authors - Ofaletse Mphale, V. Lakshmi Narasimhan, S. Sasikumaran Abstract - The Internet of Medical Things (IoMT) presents transformative potential for healthcare by enabling real-time patient monitoring, advanced diagnostics, and personalized treatments. However, its adoption in developing countries is hindered by significant ethical, security, and privacy challenges. Studies focused on developing countries often identify these challenges but rarely propose rigorous frameworks for successful adoption. This study employs a desktop search methodology to comprehensively review the existing literature, identifying crucial ethical, security, and privacy concerns associated with the IoMT adoption. Through this analysis, the study proposes potential mitigation strategies and a framework to facilitate the effective adoption of IoMT in developing countries. Findings will support healthcare decision-makers and policymakers in developing countries, enabling them to devise strategies that ensure ethical practices, secure patient data, and safeguard privacy in healthcare IoT integration. This will lead to improved healthcare delivery and enhanced patient outcomes.
Authors - Priyal Donda, Vatsal Upadhyay, Janhavi Gulabani, Sharvari Patil, Vinaya Sawant Abstract - Phishing is increasingly being one of the frequent cyber-attacks. Since this trend has seen the incidence increased significantly in the last few years, people and organizations have been highly affected by data breaches and financial losses. Such growth only increases the demand for effective mechanisms of defense, as traditional approaches of machine learning like SVM, Random Forest, and Long Short-Term Memory networks often fail to detect phishing attempts with accuracy. SVMs can be computationally expensive, sensitive to noise, and require careful selection of kernel functions, while LSTMs are complex, prone to overfitting, and require substantial amounts of labeled data. In light of these limitations, the use of GANs has been recent in order to improve detection capabilities. GANs create realistic phishing URLs that advanced detection models struggle to distinguish, using semi-supervised training to differentiate between adversarial and legitimate URLs. Specifically, this holistic approach grapples with the sophistication of phishing attacks and places an emphasis on adaptive defense, since it has changed the basis for detection from content-based to URL-based techniques. Finally, these novel approaches introduce a promising pathway for the mitigation of phishing risks and sensitive information safeguarding, thus building security strength in the digital world.
Authors - Amol Mashankar, Smita Kalokar Abstract - The retail business marketplace is experiencing a significant shift, with a growing emphasis on the innovations brought by internet of things (IoT) technology. The retail aspect is rapidly evolving, driven by new improvements in internet technology, which play a important role in the transformation of the retail sector. The new updation involves continuously adapting to the fast-paced changes within the retail environment. New techniques and innovations are emerging daily to better address customer needs and satisfaction preferences. This paper focus on to explore the practices and performance of IoT technology in the retail sector. It also emphasis an analytical framework for evaluating the approaches to IoT technology practices and their effectiveness in retail stores.
Authors - Bakka Vamshi, Munnuru Umakanth, Kadwasra Swapna, Punuru Venkata Usha Sree, Mannepalli Rohini Sri, Sushama Rani Dutta Abstract - Predicting heavy rainfall remains a significant challenge for meteorological departments as it greatly impacts economies and human lives. Severe rainfall can result in natural disasters like floods and droughts, impacting millions of people globally every year. Precise rainfall prediction is especially important for nations like India, where agriculture serves as a key economic pillar. Due to the atmosphere’s dynamic nature, statistical methods often fall short in achieving high prediction accuracy. The complex, nonlinear characteristics of rainfall data make Artificial Neural Networks a more effective method. This paper reviews and compares various methods and algorithms employed by researchers for rainfall forecasting, presenting the findings in a tabular format to make these techniques accessible to non-specialists.
Authors - Rajitha Kotoju, Sugamya Katta, Abrar Khan Abstract - Real-time air quality monitoring and predictive pollution control are critical for addressing escalating environmental and public health challenges, particularly in low-income areas with limited infrastructure. This paper explores the integration of Big Data analytics and IoT to develop cost-effective, scalable solutions for real-time air quality assessment. The proposed framework aims to identify pollution patterns, predict air quality trends, and provide actionable insights for policymakers. A unique feature of this study is its emphasis on low-cost sensor deployment and edge-computing techniques to ensure accessibility in resource-constrained settings. The interdisciplinary approach combines environmental science, AI, and public health perspectives to establish a holistic framework for data collection, analysis, and decision-making. Additionally, this paper addresses the integration of findings into policy frameworks by proposing data-driven recommendations for urban planning, industrial regulation, and community health interventions. The results demonstrate significant advancements in predictive accuracy and actionable intelligence generation while minimizing implementation costs.
Authors - D.K. Chaturvedi, Nisha Verma Abstract - Artificial Intelligence and Machine Learning (AIML) are quickly proceeding in many areas. These technologies, including the smart footwear (SF) industry, have significantly impacted the consumer goods market. AIML are widely used in the design and production of SF. There are different applications of SF such as healthcare SF, assistive SF for old age persons or impairments, navigating footwear for unknown areas, mobility and gait analysis, safety footwear, anti-skid footwear, footwear for army personnel, and power generated footwear etc. The SF helps in acquisition of real-time data of patients to monitor and suggest suitable treatment. Besides these, SF can be classified based on the different architecture and processing techniques. This paper includes different research studies conducted in the past on various tools and techniques used to create smart footwear for different applications.
Authors - Keesari Abhinav Reddy, Vanaparthi Sai Charan, Md. Sufiyan, Puvula Kiranmai, Madhuri. T, M. Venugopala Chari Abstract - The major challenge for road safety and traffic regulation continues to be categorized traffic offenses that include speeding, running of red lights, improper parking, and distracted driving. Recent innovations in artificial intelligence (AI) and machine learning (ML) have made it possible to develop automated systems that can detect and classify varied traffic violations in detail. This paper analyzes studies that have emerged recently, focusing on advanced technologies, including those such as YOLO-based object detection, OCR, integration with IoT, and real-time monitoring. The paper evaluates datasets, performance metrics, and methodologies covering violations including helmet use, lane changing, and the use of a mobile phone while driving. Significant challenges that have been touched upon in the review include issues of data privacy, high computational requirements, and environmental limitations. Some of the encouraging solution includes use of sophisticated deep learning models, big data analytics, sensor fusion, and edge computing as pathways to enhance scalability and reliability. Future effort will include improvement of real-time systems, reduction of false positives, and addressing socio-technical problems. Using approaches that merge existing advances, this paper has suggested some pathways for using AI-driven systems towards the improvement of road safety and adherence to traffic rules.
Authors - Indushree Shetty, Prerna Agrawal, Savita Gandhi Abstract - The Chronic respiratory diseases, including Chronic Obstructive Pulmonary Disease (COPD), Cystic Fibrosis, Chronic Bronchitis, Interstitial Lung Disease (ILD), Pleural Effusion, Pneumothorax, and Mesothelioma contribute significantly to global mortality and morbidity. The lung diseases in India are influenced by various demographic, environmental, and lifestyle factors like air pollution, high smoking rates, climate change and weather patterns, genetic and hereditary factors, etc. This paper highlights the current scenario of various lung diseases affecting Indian population, highest incident being of COPD to the extent of 89%. The study in this paper surveys the comparison of detection of different lung diseases using machine learning in an Indian Scenario with respect to different parameters like diseases predicted, dataset used, source of dataset, findings, limitations, future score, methods used and accuracy. Based on the comparative study, this paper also highlights various research gaps for future scope in an Indian Scenario. By prioritizing the solutions to the identified research gaps, medical practitioners would be able to handle better India's high respiratory disease burden, increasing the likelihood of more dependable and inclusive healthcare solutions.
Authors - Siddhi Mulewar, Abhijay Patil, Gauri Patil, Nikhil Chame, Smita Kulkarni Abstract - E-commerce has completely transformed traditional retail by lowering operating expenses and enabling worldwide access. Online shopping experiences have been further changed by the integration of artificial intelligence (AI) and machine learning (ML), especially with the advent of Fashion Recommendation Methods (FRM) that employ deep learning techniques. This research introduces a unique FRM that uses a single image input to provide tailored fashion suggestions based on user preferences, improving the quality of the shopping experience. Collaborative filtering (CF) is preferred method in this research work, which encourages users to explore a wider range of content and become more engaged. In this research work ResNet50 pre-trained neural networks proposed to extract information from photos, enabling precise and customized fashion recommendations. Comparative studies show that ResNet50 performs better than other CNN models, leading to increased personalization and accuracy. In the highly competitive world of e-commerce, this study emphasizes the potential of AI-driven suggestions to improve the online shopping experience, stimulate user engagement, and foster loyal consumers. VITON is a Virtual Try-On Network that uses images instead of 3D data to overlay clothes on a person’s image. It creates and refines photo-realistic images with natural clothing deformation using a coarse-to-fine strategy.
Authors - Manasa S Desai, Nirmala M B, Veeresh Kumar Y M, Varsha G C, Vinnet Gokhale, Sushma E Roa Abstract - Electronic Know Your Customer (e-KYC) system is essential for banking and identity providers to verify customer identities efficiently. With the widespread adoption of cloud computing, due to its resource efficiency and high accessibility, many sectors have implemented their e-KYC systems on the cloud. This shift, however, raises significant concerns about the security and privacy of e-KYC documents stored in the cloud. Blockchain technology, a recent innovation, offers potential solutions to enhance various application domains, including digital identity verification. This project proposes a Blockchain-based e-KYC system to address these concerns. This system provides a secure, efficient, and reliable method for identity authentication, which is particularly beneficial in sectors such as banking, tele communications, and government services. By utilizing a distributed ledger to store and verify customer data, the proposed e-KYC framework ensures data integrity and minimizes fraudulent activities. In this framework, customer data is stored on a distributed ledger and encrypted to enhance security. This encryption safeguards sensitive personal information from unauthorized access and cyber threats. This project combines the Ethereum blockchain with Zero-Knowledge Proof (ZKP) technology to provide strong digital identity verification, maintain data integrity, and reduce fraud. The decentralized nature of proposed e-KYC system not only boosts security but also reduces reliance on central authorities, thereby accelerating the verification process and lowering operational costs. This approach offers arobust solution for secure digital identity verification.
Authors - Chilakala Sudhamani, Akula Spoorthi, B. Srilatha Abstract - In today’s world, women face numerous safety challenges, including harassment and molestation. In this paper, we proposed a self-defense stun gun as an effective and efficient solution for women’s safety. This portable device contains a high-voltage generator, GSM and GPS module, panic and taser button and an Arduino Uno with Atmega328 AVR microcontroller. When the device is activated in a dangerous situation, it immediately sends an SMS with the user’s location and distress signal to pre-selected contacts. It also generates a 1000kV electric shock to temporarily immobilize an attacker, allowing the user to escape or seek help. This device aims to enhance the safety and security of women in urgent need or dangerous circumstances for proactive measures against gender-based violence.
Authors - Riddhi Sonawane, Ganesh Bhutkar, Swarup Vishwas, Vivek Badade, Akshay Shingote Abstract - Traditional persona classification methods rely on static, time consuming techniques like surveys and interviews. To address this limitation, we propose F², a novel approach that leverages facial recognition and digital footprint analysis for dynamic persona classification. By integrating real-time data from various digital platforms, F² creates more accurate and up-to-date user profiles. Our system prioritizes user privacy and adheres to relevant data protection regulations. Through robust facial recognition and advanced machine learning algorithms, F² effectively categorizes users into distinct personas, enabling tailored experiences and personalized interactions. This innovative approach has the potential to revolutionize user modeling and enhance digital experiences across diverse domains.
Authors - Aafiya Anjum Abdul Rafique, Martin H Mollay, Shailesh Gahane, Deepak S. Sharma, Pankajkumar Anawade Abstract - There have been remarkable adoptions and uses of Information Technology (IT); therefore, there has been a significant surge in energy consumption and carbon emissions in recent times. While most industries are increasingly relying on digital technologies, IT operations are also increasing their impact on the environment, thereby making green IT a vital necessity. Green IT is an all-encompassing method of managing the environmental footprint of IT through the reduction of energy consumption, electronics waste, and optimum resource efficiency. This paper discusses, from a critical perspective, the role of Green IT in reducing the carbon footprint of IT operations through sustainable technologies and practices. Beyond this, it also discusses challenges and potential solutions for a more green IT landscape in the data center, cloud computing, virtualization, energy-efficient hardware, and new sustainable development practices in software. To sum up, this paper focuses attention on some of the critical factors for driving the adoption of sustainable IT solutions: policy, education, and cross-sector collaboration.
Authors - Dheekshitha Bazar, Gajelli Sai Susmitha, Shreshta Myana, Ramu Kuchipudi, Ramakrishna Kolikipogu, P. Ramesh Babu, K. Gangadhara Rao Abstract - Strategic planning, grid management, and lessening the financial burden on Telangana’s power sector all depend on accurate demand forecasts for electricity. Currently, forecasting methods rely primarily on traditional approaches, but these models often fall short in capturing complex demand patterns at multiple time intervals, especially in dynamic sectors like agriculture. Existing forecasting methods, focused mainly on traditional approaches, often fall short in capturing complex demand patterns across multiple time scales, particularly in sectors like agriculture. This study introduces a comprehensive multi-scale forecasting model for Telangana’s electricity consumption over the next five years, targeting yearly, monthly, weekly, and daily intervals, with a focus on peak load forecasting. Time series techniques such as ARIMA, Prophet, Weighted Moving Average (WMA), and Error Trend Seasonality (ETS) are leveraged to capture seasonality, trends, and short-term fluctuations in demand, providing actionable insights for the Telangana SLDC. Methods for machine learning such as Long Short-Term Memory (LSTM) networks and Gradient Boosting Machines (GBM), are integrated to capture complex temporal patterns and improve predictive accuracy. This study offers a scalable framework for electricity demand forecasting, adaptable to other regions and utilities, advancing methodologies in the power sector. The suggested approach uses metrics to assess the model’s performance such as Root Mean Square Error, Mean Absolute Error (MAE), Both Mean Absolute Percentage Error (MAPE) and RMSE are used to choose the most precise model for every period.
Authors - Hemal S, Sohana R, M Shahina Parveen, Tarun Pradeep Kumar Abstract - Childhood fever poses a significant health concern in India, necessitating timely intervention and effective healthcare strategies. However, predicting fever prevalence accurately remains a challenge due to the diverse healthcare landscape and maternal-child health indicators. This research aims to develop a systematic methodology for predicting childhood fever prevalence based on maternal and child healthcare indicators in India. Leveraging machine learning algorithms, particularly Support Vector Regression (SVR), the study seeks to provide an effective tool for early detection and intervention in infant fever cases. Using data from the "India - Annual Health Survey (AHS) 2012-13" dataset, specific maternal and child healthcare indicators relevant to childhood fever prevalence are identified. These indicators encompass ante-natal care, delivery care, immunization, breastfeeding, and supplementation practices. Various regression algorithms, including SVR, are trained and evaluated to accurately predict childhood fever prevalence. Experimental results demonstrate that SVR outperforms other regression algorithms, showcasing its effectiveness in capturing non-linear relationships and handling outliers. This study offers a structured framework for early detection and intervention in childhood fever cases, leveraging machine learning algorithms and maternal-child health indicators. By accurately predicting fever prevalence, healthcare practitioners can implement timely interventions, ultimately improving healthcare outcomes for infants in India.
Authors - Shivam Kumar Singh, Sindhu Chandra Sekharan, Aishwarya Mondal, Nitin Nagar, Shruti Shreya, Yuting Zhu Abstract - This work presents a comprehensive IoT-based smart assistant device aimed at providing essential navigation and safety support for physically challenged individuals, especially those with visual impairments. The device is equipped with advanced functionalities, including GPS tracking for real-time location monitoring, MobileNet-based object and face recognition, OCR capabilities for reading printed text, and ultrasonic sensors for detecting obstacles, which trigger an alarm to alert the user. Its design prioritizes energy efficiency, allowing it to run effectively on low power while offering reliable real-time processing. By combining multiple assistive features into a single, cost-effective, and portable device, this solution sets itself apart from traditional options that often focus on one functionality or rely on expensive hardware. The modular and scalable architecture not only makes it an affordable and practical solution but also allows for easy customization and potential wireless enhancements. This flexibility opens up possibilities for broader applications in fields like assistive healthcare, autonomous navigation, and consumer electronics, making it a pioneering tool in inclusive technology that enhances mobility, security, and overall independence for its users.
Authors - Sujith Kumar Banda, Ramzan Shareef, Swathi Sowmya Bavirthi, Mohammed Arbaz Ahmed Abstract - While meetings help make company decision-making more effective, documenting and distilling the material turns out to be a lot of time-consuming work and may also contain mistakes. The project provides an automated way of transcribing audio recording of meetings into text and applying NLP for perfect creation of useful summaries. As opposed to the existing techniques that resort to either means of human beings or platform-specific ones, our solution is a versatile way that can handle transcripts from a variety of online resources. This is a system that offers both abstractive and extractive summary techniques in the form of developed transformer models, such as BERT, to form logical summaries and TF-IDF and TextRank to focus the most important points in the summary. A wider applicability of Named Entity Recognition (NER) and Part-of-Speech (POS) tagging will allow summarization over key elements, including decisions taken and responsibilities assigned. The approach aims to make the capture of output from the meeting more efficient and reliable by automatically summarizing proceedings in meetings. User input and ROUGE scores will assess how well the system performs and guarantees quality useful summaries to stakeholders.
Authors - Rajeev Sharma, Santanu Sikdar, Govind Murari Upadhyay Abstract - To protect network infrastructure from new vulnerabilities and security dangers caused by the rapid growth of Internet of Things (IoT) devices, robust and adaptable Intrusion Detection Systems (IDS) are necessary. Due to their limited scalability and reactivity to different attack patterns, conventional intrusion detection systems (IDS) struggle to meet the unique demands of Internet of Things (IoT) networks. The novel Intrusion Detection System introduced in this paper is based on deep learning and is tailor-made for Internet of Things (IoT) environments. It employs complex neural network topologies to enhance the accuracy and efficiency of detection. Regarding the massive amount and variety of data generated by IoT devices, our suggested method improves performance without compromising detection accuracy by combining feature selection and dimensionality reduction strategy. Standard IoT network datasets were used for training and validation, with several assaults implemented to ensure comprehensive threat coverage and practical applicability. The results of the experiments show that the proposed system outperforms the state-of-the-art machine learning-based intrusion detection systems in detection accuracy, false positive rates, and scalability in contexts with limited resources for the Internet of Things.
Authors - Rutuj Barudwale, Vijeyandra Shahu Abstract - This article focuses on attempting artificial intelligence in stock price forecasting. Common stock market predictions and their prices can be assessed using dual primary analytical models known as technical and fundamental analysis. I employed a technical analysis of price trends predicting price movements using regression machine learning (ML). For instance, predicting how the price of a particular stock will close at the end of today based on historical price trends. In contrast to this approach of technical analysis, fundamental analysis can be applied to supervised machine learning algorithms to assist with identifying how news and social network users appear to be for or against certain entities. In the technical analysis, the historical price trends are retrieved from Yahoo, and in the fundamental analysis, the stock market tweets are analyzed to assess how the public feels about the stock prices. The findings portray an average performance; therefore, given the present environment of - technology, it is rather optimistic to presume that technology will ever beat the stock market consistently.
Authors - Satvik Taviti, Srreyasri Kurlagunda, Nandikanti Sri Gayatri, R M Krishna Sureddi, Raman Dugyala Abstract - Electronic Health Records (EHR) are considered to be amongst the most crucial elements for exchanging data in healthcare services. Thus, security for these records is the keystone of patient privacy and easy cooperation between the service providers. This review looks at four primary approaches to EHR security and predictive management: Blockchain, Attribute-Based Encryption (ABE), Deep Learning, and Access Control Models. Blockchain ensures data integrity, transparency, and traceability but scalability issues, high transaction costs, and interoperability challenges prevent its widespread adoption. ABE is appropriate for fine-grained access control in data sharing under the patient-centred approach but cumbersome and resource-intensive for managing encryption across the large healthcare network. Deep learning helps predictive analytics, personalized medicine, but with high computational demands that affect its real-time application in the clinical environment. While in terms of data confidentiality protection, models such as Role-Based or Attribute-Based Access Control may ensure proper restriction of authorized access, they might not suffice for dynamic, multi-provider health environments. Comparing the techniques will outline their relative merits, weaknesses, and security considerations, thus helping to understand how safe yet scalable systems for EHR storage could be built.
Authors - Edidiong Akpabio, Supriya Narad Abstract - This review aims to understand the integration of two emerging technologies: artificial intelligence and the Internet of Things. IoT is defined as the capability of implementing connections between regular items and industrial apparatuses that can liaise in real-time, exchanging and analyzing data. AI is an ideal companion to IoT in the sense that it brings decision-making into the equation and boosts the effectiveness and functions of IoT systems. This paper aims to review the use of AI and IoT in various fields, namely, smart cities, health, farming, and transport. In smart cities, IoT and AI applications are also applied to enhance traffic, energy consumption systems, and urban design. It has changed the way that the healthcare industry operates through better methods of patient monitoring, performance analysis, and telehealth. In agriculture, IoT sensors help monitor the effectiveness of crop management and the use of AI-based automation. It also covers the implementation of AI and IoT in autonomous vehicles, particularly the use of sensors for data processing, decision-making, and real-time data communication. However, the use of AI and IoT has some limitations, such as data limitations, security and privacy, and environmental impact. Indeed, the paper dwells upon these issues and provides the outlook for further research regarding edge AI, IoT sustainability, and the further evolution of the connections. With technological progress still in the process of evolving AI and IoT, future advancements hold more potential in terms of creating better connected, efficient, and sustainable solutions, not to mention the fact that AI is capable of solving existing challenges.
Authors - Rydhm Beri, Parul Sachdeva Abstract - The advent of IoT technology has significantly transformed the industrial sector, paving the way for the emergence of Food Industry 4.0. This research explores the integration of edge–cloud computing and IoT to create a smart framework tailored for the food industry. Central to this framework is the appli-cation of a Bayesian belief network (BBN) on an edge–cloud platform, enabling data-driven insights into food quality. The framework assesses data to calculate the Probability of Food Quality (PFQ) and utilizes the Food Quality Analysis Measure (FQAM) to evaluate food outlets. A bi-level decision-tree model further enhances the evaluation process by providing an in-depth analysis of food quality metrics. To address concerns around data security, blockchain technology is implemented, ensuring the protection of food-related information. The model is rigorously tested on a comprehensive dataset encompassing 43,520 instances from four restaurants. Simulation results highlight its high performance, achieving a temporal delay of 96.43 seconds, and the system demonstrates an accuracy of 98.93%, showcasing its robustness in real-world applications.
Authors - Rydhm Beri, Parul Sachdeva Abstract - Plant diseases present a serious threat to all forms of life. Early detection is vital which allows farmers to take prompt action, improving both their response and productivity. Our research centers on five common rice leaf diseases—bacterial leaf blight, leaf blast, brown spot, leaf scald, and narrow brown spot—along with a category for healthy leaves. Additionally, we examine two types of betel leaves: healthy and unhealthy. This study propose an innovative deep ensemble model that combines the EfficientNetV2L, InceptionResNetV2, and Xception architectures. This model addresses issues of underfitting and performance by utilizing advanced techniques including data augmentation, Global Average Pooling, preprocessing, Dropout, L2 regularization, PReLU activation, Batch Normalization, and multiple Dense layers. This robust approach surpasses existing models by managing both underfitting and overfitting, while delivering superior performance.
Authors - T. Sridevi, Chidhrapu Harini, Kurella Sai Veena Abstract - Sign Language is the primary means of communication among 1.8 million deaf people across India, and although Indian Sign Language (ISL) translation to technology-based effective solutions is still very limited, tremendous effort has so far been made in global research in sign language recognition. Nevertheless, the challenge persists in transcoding of text from ISL. This project will fill the vacuum by developing a deep-learning-based model capable of generating subtitles for ISL videos. With a pre-trained Convolutional Neural Network (CNN) for spatial feature extraction and a Recurrent Neural Network (RNN) for encoding the temporal pattern, the model learns on the Indian Sign Language Videos dataset. Designed in a manner to achieve high-accuracy captioning of ISL for reliable communication with the Indian deaf community. This will provide access to means of communication for millions of ISL users, but at the same time offers a critical communication tool meant to facilitate improvement in circles of education, social life, and professional circles in India.
Authors - Anudeep Arora, Vibha Soni, Lida Mariam George, Anil Kumar Gupta, Ranjeeta Kaur, Neha Arora, Neha Tomer, Prashant Vats Abstract - In the field of financial analytics, stock market prediction continues to be one of the most difficult and sought-after objectives. A key component of stock price modeling and forecasting is time series analysis, a statistical technique that examines sequences of data points gathered at successive times. A thorough review of time series analytic techniques for stock market prediction is given in this article. These techniques include machine learning and deep learning, as well as more sophisticated approaches like GARCH and ARIMA. It addresses the drawbacks and advantages of these methods, looks at the difficulties in putting them into practice, and identifies new developments in time series forecasting. Investors and analysts may improve their ability to anticipate the future and make better judgments in the ever-changing stock market environment by being aware of these techniques and how they are used.
Authors - M R Shreyaank, Dhanush Karthikeya A J, Dhanush Rajan S, Ashwini Bhat Abstract - This research attempts to investigate the potential healing effects of Vedic chants and music on the human brain through an in-depth analysis of EEG signals. The Vedic chants are known for their inherent calming and meditative attributes and are believed to impart positive influences on the human mind and body. The study employs a simulative model to analyse EEG signals during exposure to Vedic chants. Recorded EEG signals from MDD (major depressive disorder) subjects are subjected to preprocessing and feature extraction processes involving frequency-domain analysis and power spectral density. The study compares the extracted features between conditions of Vedic chant exposure and controlled settings and shows that there is significant increase in alpha and beta powers after listening to the specified chants. Rejuvinating and Calming chants showed the best positive impact.
