Authors - Kajal Joseph, Deepa Parasar Abstract - This study conducts a predictive analysis of company status using various machine learning algorithms, aiming to identify the models that deliver the highest accuracy and reliability for decision-making in finance and business intelligence. The study employs a range of algorithms, including Logistic Regression, DecisionTreeClassifier, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naive Bayes, Gradient Boosting Machines (GBM), XGBoost, AdaBoost, LightGBM, CatBoost, and Extra Trees Model, each rigorously tested on a preprocessed dataset split into training and testing sets to ensure robust validation. (Kunjir et al., 2020) Results indicate that ensemble models, particularly XGBoost and Random Forest, outperformed other methods, achieving accuracy rates exceeding 93%. This high level of performance highlights the value of ensemble techniques for handling complex predictive tasks, showcasing their suitability for applications where precise forecasting is critical. The study underscores the importance of model selection in predictive analytics, as it directly impacts the reliability of predictions in financial contexts. These findings suggest that machine learning, especially ensemble models like XGBoost and Random Forest, can significantly improve the accuracy of company status predictions, offering a dependable tool for stakeholders operating in uncertain environments. This research contributes valuable insights into the efficacy of machine learning in predictive tasks, advocating for data-driven decision-making approaches that can enhance business intelligence and strategic planning. (Liaw et al., 2019)
Authors - Meenu Suresh, Tonny Binoy, Saritha M S, Vimal Babu P, Dheeraj N, Aiswarya R Lakshmi Abstract - The present work introduces a video steganography technique which employs Finite Ridgelet Transform (FRT) and Elliptic Curve Cryptography (ECC)-ChaCha20 encryption to hide confidential information. The proposed method begins by identifying key frames through the detection of scene changes. The FRT is employed to analyze the key frames, extracting their orientation and subbandswithin which the secret data is encoded. To boost security, ECC-ChaCha20 encryption technique serves as a preprocessing step prior to incorporating the secret data. The technique attains an embedding capacity of 72%, SSIM of 0.9890 and PSNR value range from 70dB and 72 dB. The experimental results highlight that the algorithm besidesboosting security also ensures superior resilienceand video quality.
Authors - Jagriti Singh Chundawat, Ashish Kumar, Monika Saini Abstract - The purpose of this paper is to optimize the availability of a thermal power plant. A thermal power plant (TPP) is a comprehensive system with multiple interconnected subsystems which are used for power generation. This TPP system has three subsystems such boiler, superheater and reheater. These subsystems connect to each other in series configuration. To improve the availability of the system a study-state availability is derived with the help of normalizing equations and the chapman Kolmogorov equations are derived from Markov birth-death process. The system’s failure and repair rates are statistically independent and exponentially distributed. The numerical results show that availability increases from 0.997903 to 0.998725 as the repair rate increases.
Authors - Mannem Sri Nishma, Satendra Gupta, Tapas Saini, Harshada Suryawanshi, Anoop Kumar Abstract - Face recognition-based authentication has become a critical component in today's digital landscape, particularly as most business activities transition to online platforms. This is especially evident in the finance and banking sectors, which have shown significant interest in adopting online processes. By leveraging this technology, these industries can enhance operational efficiency, promote business growth, reduce reliance on manpower, and automate several processes effectively. However, face recognition systems are susceptible to face spoofing attacks, where malicious actors can attempt to deceive these systems using facial images or videos. Some attackers even use masks resembling authorized individuals to trick recognition cameras into perceiving them as real users. To counter such threats, liveness detection has emerged as a critical research area, focusing on identifying and preventing face spoofing attempts. The proposed approach utilizes a deep learning technique tailored for face liveness detection. The experiments are conducted using the Replay-Mobile, MSU-MFSD, Casia-FASD and our own datasets, which are widely used for recognizing live and spoofed faces. The system achieved an impressive area under the ROC curve (AUC) of 0.99, demonstrating its effectiveness in detecting face spoofing.
Authors - Kusuma B S, Meghana Murthy B V, Preksha R, Srushti M P, C Balarengadurai Abstract - Against the backdrop of either a Deaf World or hearing people, the major challenges which face modern society concern communication barriers in general. The paper proposes a system for translation through gestures in Indian Sign Language to audio and video outputs for non-signers to enable easy interaction with them. Advanced machine learning techniques, such as Support Vector Machine and Convolutional Neural Network, will be used to enable this tool to recognize motions of ISL in real time. It converts these into the correct format for video and audio. In this respect, the paper claims to "make communication more accessible and bridge the gap in communication in which gestures are recognized and translated." Real-time recognition algorithms overcome the challenges faced by hand gesture detection to provide an intuitive and seamless interaction experience. This approach is an effective strategy to enhance communications in government and industry with special focus on smart writing. Results confirm this method's promise in the broader social interaction by significantly improving the speed and accuracy of deaf individuals.
Authors - Bitan Pratihar Abstract - We, human-beings, have two different forms of memory, namely pulling memory and pushing memory (also known as working memory). A pure pulling memory pulls a person towards itself, and consequently, he/she spends some significant amount of time on memorizing the incident but does not gain anything significant in his/her decision making directly. On the other hand, a pure pushing memory pushes a human-being to take some decisions, and thus, it may have direct influence on his/her learning. However, neither pure pulling memory nor pure pushing memory alone may be beneficial to effective learning of human brain. A proper combination of pulling and pushing memories may be required to ensure a significant effect of memory on learning of neural networks. The novelty of this study lies with the fact of formulating it as an optimization problem and solving the same using a recently proposed nature-inspired intelligent optimization tool. The effectiveness of this novel idea of correlating the combined form of memory with learning of neural networks has been demonstrated on two well-known data sets. This combined form of memories is found to have a significant influence on learning of neural networks, and this proposed approach may have the potential to solve the well-known memory loss problem of neural networks.
Authors - Martin Mollay, Deepak Sharma, Pankajkumar Anawade, Chetan Parlikar Abstract - The primary research intention of the present study is to find out the impacts of laws such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) on the digital marketing landscape. The set of regulations relates to data protection, which involves a stringent regime for how the firms gather, process, and hold the privacy of their clients. Therefore, the two main bottlenecks of marketers are fewer consent mechanisms, less data, and a need for more options for personalizing. Still, technology is a fashionable thing that has been launched, and the effectiveness of the new technology revitalizes it. Firms mostly turn to first-party data that question the need for intermediaries. This means that they can collect information directly from the consumer, which then naturally results in much more productive and meaningful customer relation-ships. Getting hold of advanced technologies, for instance, artificial intelligence and machine learning, which work with smaller datasets, also provides a window for companies to discover a large number of customized, and possibly even more valuable, aspects through customer behavior without invading the privacy of the person who is identical to the threat of the law. Additionally, the price of compliance with the regulations is high, notably for Small and Medium sized Enterprises (SMEs). In contrast, it is the most highly cost-effective way for the consumer to win consumers’ trust in the brand and make them loyal to it in the long run. In this new era, ethical marketing follows the footsteps of the evolutionary journey where complete openness and consumers’ private space value are the main topics. Personal data can be acquired in a way that is not compliant with privacy laws. However, zero-party data or consumer information given to businesses might still be the source for personalized experiences that are privacy compliant.
Authors - Sachi Joshi, Upesh Patel Abstract - Cancer is a grave category of illnesses in which the body's aberrant cells proliferate and spread uncontrollably. It can appear in nearly every tissue or organ and take many different forms, each with its own distinct set of symptoms and side effects. Environmental variables, lifestyle decisions, and genetic abnormalities are typically linked to the development of cancer. The varied approaches to cancer diagnosis are examined in this study, with a focus on early detection and therapeutic strategies. This literature review covers a wide range of cancer kinds, such as brain tumours, leukaemia, breast, lung, and cervical cancer, and offers recommendations for creating reliable ma-chine learning-enhanced cancer detection techniques. The research elucidates several applications, techniques, and comparative analysis in this significant subject, ranging from imaging analysis to biomarker identification. The study explores the developing methods that lead to a more precise diagnosis. The study offers insights with a thorough examination of the benefits, drawbacks, and innovations of each technique, ranging from conventional diagnostic procedures to state-of-the-art technologies. It also directs future research efforts towards the hunt for more effective personalized illness management.
Authors - Akshay Honnavalli, Hrishi Preetham G L, Aditya Rao, Preethi P Abstract - In todays information-driven world, organizing vast amounts of textual data is crucial. Topic modelling, a subfield of NLP, enables the discovery of thematic structures in large text corpora, summarizing and categorizing documents by identifying prevalent topics. For Hindi speakers, adapting topic modelling methods used for English texts to Hindi is beneficial, as much of the research has focused primarily on English. This research addresses this gap by focusing on Hindi language topic modelling using a news category dataset, providing a comparative analysis between traditional approaches like LDA, LSA, NMF and BERT-based approaches. In this study, six open-source embedding models supporting Hindi were evaluated. Among these, the l3cube-pune/hindi-sentence-similarity-sbert model exhibited strong performance, achieving coherence scores of 0.783 and 0.797 for N-gram (1,1) and N-gram (1,2), respectively. Average coherence scores of all embedding models significantly exceeded traditional models, highlighting the potential of embedding models for Hindi topic modelling. Also, this research introduces a novel method to assign meaningful category labels to discovered topics by using dataset annotations, enhancing the interpretation of topic clusters. The findings illustrate both the strengths and areas for improvement in adapting these models to better capture the nuances of Hindi texts.
Authors - Guhan Senthil Sambandam, Priyadarshini J Abstract - Machine learning has significantly impacted daily life, with machine translation emerging as a rapidly advancing domain. In healthcare, machine learning presents opportunities for innovation, particularly in translating medical documents into low-resource languages like Tamil. This research develops a transformer-based model fine-tuned for medical terminology translation from English to Tamil. A major challenge was the lack of English-Tamil medical datasets, addressed through innovative data collection methods, such as extracting bilingual subtitles from Tamil YouTube videos. These datasets complement existing resources to enhance model performance. The final model was deployed as a REST API using a Flask-based server, integrated into a React Native mobile application. The app enables users to scan English medical documents, extract text via on-device Optical Character Recognition (OCR), and obtain Tamil translations. By combining advanced Natural Language Processing (NLP) techniques with user-friendly application design, this end-to-end system bridges linguistic gaps in healthcare, providing Tamil-speaking populations with improved access to critical medical information. This study highlights the potential of NLP-driven solutions to address healthcare disparities and demonstrates the feasibility of adapting machine translation systems to specialized domains with resource limitations. The approach also emphasizes scalability for broader applications in similar low-resource settings.
Authors - Sakshi Limkar, Chhandavi Gowardhan, Devyani Dahake, Sneha Naik, Arti Vasant Bang Abstract - Chatbots powered by artificial intelligence (AI) are becoming more and more inventive tools in the field of mental health treatment. They provide scalable, affordable, and easily accessible support for people struggling with stress, anxiety, depression, and other mental health conditions. These conversational bots provide real-time therapeutic interventions, such as promoting emotional well-being by mimicking human interaction through Natural Language Processing (NLP) and Machine Learning (ML) techniques. We have therefore developed a chatbot for mental health assistance named CalmConnect. It is designed to assist users in identifying and addressing mental health concerns.
Authors - Akshar Thakor, Tanya Khunteta, Kaushal Shah, Hargeet Kaur Abstract - It is essential to control access to data. Since the beginning of digital communication, scholars have been at work figuring out ways to prevent eavesdropping on data. They have been successful in doing so through cryptography techniques such as RSA, and AES. Modern Quantum computing is shown to be able to break them. Thus, research and development in this field have been rapid in the last decade. It is better to take precautions in its infancy and develop a future-proof cryptography technique. The following article describes the issues with contemporary ciphers and how they are vulnerable against quantum computers, then goes on to suggest lattice-based cryptography as a strong contender for the solution to this vulnerability by providing its benefits and properties synergizing with certain domains in digital technology. It explains why it is a strong contender by providing examples of its performance in IoT devices that are prevalent today and are only going to increase as this era progresses. By providing a comprehensive overview of developments in this realm, it presents to, new researchers in this field, the importance of Lattice-Based Cryptography, by suggesting why Lattice-Based Cryptography should be the focus of the field of cryptography in the future.
Authors - Nitin Pandit, Sandeep Chaware, Adit Bagati, Yashraj Shegokar, Omkar Jadhav, Om Nikam Abstract - DOS attacks or denial of service have become common among hackers who use them as a way to gain reputation and respect in the cyber underground. A denial-of-service attack essentially means denying legitimate and user network services to a target network or server. Its main purpose is to attack so that legitimate users are temporarily unable to use the services on the network. In other words, we can define a DOS attack as an attack that clogs the target’s memory, making legitimate users unable to help. Or, you send packets that the target cannot process, causing the target to fail, reboot, or deny service to legitimate users. We develop an online DOS protection software that can protect web servers.
Authors - Vedant Patil, Bhargavi Bhende, Omkar Jadhav, Gitanjali Shinde, Kavita Moholkar Abstract - The increasing realism of AI-generated faces, driven by advancements in Generative Adversarial Networks (GANs) like StyleGAN and ProGAN, poses significant challenges in security, identity verification, and digital forensics. Current detection methods, primarily relying on Convolutional Neural Networks (CNNs), struggle to identify subtle artifacts in high-quality synthetic imagery. This paper proposes a hybrid model combining Vision Transformers (ViT) and XceptionNet in a soft-voting ensemble framework. ViT captures global spatial patterns, while XceptionNet excels in detecting localized texture inconsistencies. The ensemble achieves 92.3% accuracy, 92.5% precision, and an F1-score of 0.922 on a dataset of 188,800 real and AI-generated faces. Extensive experiments demonstrate the model’s robustness against diverse deepfake architectures, including those with minimal artifacts. This approach offers a state-of-the-art solution for differentiating real and AI-generated faces, with significant implications for fraud prevention, content moderation, and digital forensics.
Authors - Rupali Ramdas Shevale, Monika Sharad Deshmukh Abstract - For efficient real-time decision-making in a variety of domains, including cybersecurity, finance, and the Internet of Things, accurate and trustworthy event categorization is crucial. By maximizing feature integration, this study explores how incorporating Redpanda, a real-time data streaming platform, into predictive algorithms might improve event categorization. Continuous, high-throughput data processing is made possible by Redpanda's low-latency, fault-tolerant architecture, which enables the real-time extraction of a variety of accurate attributes. Predictive models may use Redpanda's capability to access current, augmented feature sets, which will greatly increase classification accuracy and dependability. The integration process is thoroughly examined in the research, along with its effects on feature variety, model accuracy, and system robustness. The benefits of real-time data streaming in predictive analytics are demonstrated by empirical results, which indicate a significant boost in event categorization performance. By improving feature extraction and enhancing the dependability of predictive systems in dynamic contexts, the results establish Redpanda as a scalable and robust solution.
Authors - Jayashri D.Palkar, Anuradha S. Deshpande Abstract - The crop protection plays vital roles in the food supply and depends on how healthy the crops are, which influences the agricultural production; any adverse condition on crops will be leading to economic loss. Grapes find much use, being important and widely cultivated crops primarily in the Mediterranean regions that control an outgoing market of over 189 billion United States dollars. They are grown for consumption as fresh fruits, as well as in various processed forms such as drinks and sweets. These would be grapes, which, unlike many other plants, thrive and develop despite sickness, thus their control mechanisms must also function well. At the same time, many instances of diagnosis of these infections being wrong can lead to inadequate treatments for the known diseases, inducing even more generalized losses amounting from 5-80% on the crop under inspection. Current computer-based solutions may not be precise enough, leading to high running costs, operational difficulties, and image quality issues due to distortions. The body of literature based on different algorithms for the detection and classification of grape crop diseases remains vast and continues to grow rapidly with the newly emerging algorithms. It presents the overview of different disease-detection algorithms for optimizing grape disease detection, thereby aiding farmers in choosing the appropriate algorithm based on particular diseases and weather condition. This study presents a systematic review of various methods implemented in literature and provides a framework for use of AI-ML for effective detection of disease.
Authors - Shilpa M Katikar, Vikas B Maral, Nagaraju Bogiri, Vilas D Ghonge, Pawan S Malik, Suyash B Karkhele Abstract - Effective forecasting and modeling in food demand supply chains are critical to minimizing waste, reducing costs, and ensuring product availability. This paper explores a comprehensive approach to forecasting food demand by leveraging regression-based models for analysis. We investigate how various machine learning regressors can predict food demand more accurately by examining key supply chain factors such as seasonal trends, price fluctuations, and consumer behavior. The study implements and compares multiple regressors to assess their performance in predicting demand. Metrics Evaluation is done by predicting various models which are Ensemble Learning Models and Neural Network Models to calculate the model’s accuracy. By doing prediction, we identified that Gradient Boosting and XGBoost have overall good accuracy in forecasting and it has provided optimized solutions in the supply food chain. This research mainly focuses on using the best modeling techniques which will help the end users to make proper decisions and bring efficiency in food demand management.
Authors - S. B. Hema Anjali, Manikanta Sai Sumeeth, Sushama Rani Dutta Abstract - This study makes use of a machine learning system that predicts health insurance costs, a relevant issue given the increasing need for such estimates in a post-COVID-19 world. Using the Medical Cost Personal Dataset available at Kaggle offering 1,338 entries, we applied various ensemble models, notably XGBoost, Gradient Boosting Machine (GBM), Random Forest, and Support Vector Machines (SVM). Among our results, XGBoost gives out the best accuracy of the estimates, but the implementation of this technique was expensive. Random Forest was non-intrusive and went on to be of high efficacy. We also discussed how the big data paradigm was implemented using Spark as a means to enhance performance in working on large datasets. As a whole, this work positions XGBoost the ban for the cost of health insurance prediction claiming that there exists scope for improvement by deploying ML methods in decision making in healthcare processes.
Authors - Shrikant Bhopale, Tahseen Mulla, Madhav Salunkhe, Sagarkumar Dange, Sagar Patil, Rohit Raut Abstract - Cardio-Vascular Disease (CVD) continues to be a prominent issue in worldwide health, emphasizing the crucial importance of accurate forecasting and timely prevention. Machine learning (ML) has become a vital tool in the quest to improve CVD diagnosis. The present study aims to conduct a comparative analysis of various machine learning (ML) algorithms in terms of their performance, which includes Naïve Bayes, Logistic Regression, Random Forest, Decision Tree, Artificial Neural Network, Support Vector Machine and XGBoost, in the prediction of CVD. Our results reveal that XGBoost outshines other models, achieving outstanding accuracy, precision, recall, and F-measure. Its exceptional ability to balance precision and recall makes it an excellent choice for the early identification of CVD. This study makes a valuable addition to the expanding field of study on CVD prediction. It underscores the significance of employing advanced ML algorithms, that have the possibility to significantly influence public health outcomes.
Authors - Yathin Reddy Duvuru, Seshank Mahadev, Saranya P Abstract - In this paper, we implement a deep learning model for photovoltaic (PV) power forecasting using Global Horizontal Irradiance (GHI) values which are the major determiner of photovoltaic cell power output. We use a multilayer Long Short-Term Memory (LSTM) model combined with explainable AI (XAI) techniques, aimed at improving the interpretability of predictions across various forecasting horizons. The model utilizes global horizontal irradiance (GHI) data, which undergoes thorough pre-processing, including cleaning and downsampling to ensure data quality and computational efficiency. The LSTM model is designed with multiple layers to capture temporal dependencies and nonlinearities, which are crucial for accurately forecasting PV power under variable environmental conditions. To evaluate model performance, multiple error metrics such as R², MAE, RMSE, and MAPE are utilized. In addition, a benchmark model is built as a reference to compare against the LSTM-based model, providing a baseline for assessing performance improvements. The use of XAI further enables the interpretation of the LSTM model’s predictions, providing an understanding of feature importance and model behavior. We use the SHAP library to perform XAI analysis by calculating Shapley Values. We demonstrate how the SHAP library can be used on 3D LSTM data. Furthermore, the SHAP graphs provide a sense of the importance of each feature’s role in the prediction.
Authors - Suresh V Reddy, Sanjay Bhargava Abstract - Cybercrime on social media platforms such as Facebook and Twitter has emerged as a significant challenge due to the open, interactive nature of these platforms. Various machine learning (ML) and deep learning (DL) techniques have been deployed to detect different forms of cybercrime, including phishing, spamming, hate speech, and identity theft. This paper provides a comparative analysis of these approaches, focusing on their application to cybercrime detection on Facebook and Twitter. Through a detailed literature review, we evaluate the strengths and weaknesses of these techniques, considering their performance and scalability. Moreover, the ethical challenges and the need for privacy-preserving mechanisms are discussed, along with future directions for research.
Authors - Rakesh Babu B, Rajesh V, Syed Inthiyaz, Srinivasa Rao K, Sri Sravan V Abstract - Brain tumours are life-threatening disorders with significant fatality rates. Patients have a higher chance of survival when brain tumours are diagnosed early and treated more effectively. Therefore, for the purpose of better and boost the early identification of brain tumours, computerized segmentation as well as classification techniques are needed. It is possible to safely and promptly detect tumours using brain scans such as computed tomography (CT), magnetic resonance imaging (MRI) and other techniques. Revolutionary changes have occurred in many different disciplines as a result of recent developments in artificial intelligence (AI). AI models are becoming essential tools for interpreting images in bio medical field. Deep learning is one of these that signifies extraordinary capacity to deal with enormous data collection, revolutionizing numerous fields in the biomedical profession. This article evaluates a state-of-the-art AI based segmentation and classification system and discovers major classes for brain tumours. The potent learning capability and effectiveness of AI approaches have been assessed. Convolutional Neural Network (CNN) is one of the AI subfields that has demonstrated remarkable performance in analysing medical imagery. Consequently, the processing of medical imagery, particularly brain MRI images, was the main emphasis of this review paper, which also examined different deep learning model architectures in addition to CNN.
Authors - Shubham Kadam, Chhitij Raj, Pankajkumar Anawade, Deepak sharma, Utkarsha Wanjari, Janhvi Shirbhate, Sharvari Pipare Abstract - Artificial Intelligence (AI) is increasingly being hailed as the key to the future of healthcare supply chain management in countries such as India, where healthcare is a particularly complex setting for an integrated supply chain. This review presents the various Data-driven Artificial Intelligence (AI) technologies such as Machine Learning (ML), Natural Language Processing (NLP), Computer Vision, and Robotic Process Automation (RPA) that help in the automation of essential processes like demand forecasting, inventory management, and cold chain logistics in an efficient and timely manner. AI helps deliver vital supplies on time and minimizes any disruptions of services by utilizing predictive analytics and real-time monitoring. However, high implementation costs, data privacy concerns, the need for integration with legacy systems, and a need for more skilled professionals are barriers to the adoption of AI computing. To extract the maximal potential AI can offer healthcare logistics, the issues above need to be addressed. Upcoming research directions include further development in quantum computing, IoT integration, and collaborative AI platforms to fulfil resilience and sustainability objectives for supply chains. The results underscore the potential of AI to transform health supply chains and provide an opportunity to realize more scalable, responsive, and efficient health services.
Authors - Rajeshree Khande, Sachin Naik, Akshay Tayade, Amar Kale, Kunal Phalke Abstract - The authors propose for the LSTM-XGBoost model for portfolio optimization as well as stock price prediction. The model has incorporated the benefits derived from XGBoost, a gradient-boosting algorithm that enhances the ability of a model to predict structured and improved data, and Long Short-Term Memory (LSTM) networks, which excel at characterizing time-series data based on temporal relationships. The XGBoost model takes advantage of the LSTM model by utilizing the anticipated outputs it makes for improving the precision and overall efficiency of the model while the LSTM model is designed to work with ordered data peculiar to stock markets specifically on patterns and trends over time. In the study authors employ this type of hybrid to determine variables such as volatility and the moving average of historical stock price index of NIFTY50. The authors have obtained total model accuracy of 98.33%. Authors also use the Sharpe ratio to maintain an optimal portfolio because it shows investors the optimal ratio of expected stock returns. This research contributes to enhancing financial forecasting by integrating deep learning and machine learning techniques, ultimately offering the formulation of a new risk avert portfolio as well as stock price prediction.
Authors - Jaiditya Nair, Sunil Kumar Abstract - The increasing demand for AI-driven solutions in development has encouraged people to conduct various research into generating code from natural language prompts. My paper presents a Retrieval-Augmented Generation (RAG) pipeline for code generation, making use of embedding models, contextual retrieval, and advanced language models such as Mistral and CodeLLama. This approach incorporates document indexing and metadata extraction to create context-aware code snippets and at the end of the process, we get a python file with the generated code present in it.
Authors - Nishita Shekhar Bala, Sree Vani Bandi, Stephen R, Ravi Dandu, Balakrishnan C Abstract - These days internet is became an essential part of human life and affects various domains which includes education, business, social interactions, mental health. It pushes the society ahead through increasing innovations, amplifying learning techniques, connecting people across the globe and access to vast resources which makes it a valuable tool in this modern society. But it comes with problems such as internet addiction, sleeping disorders, health complications. This abstract discusses about dual impact of internet uses, focusing on its significant benefits and possible dangers. Hence, there is need to mange use of internet so one can make use of its benefits at the same time reducing the affects which are caused by internet on human life.
Authors - Chandan Raj B R, A. Yasaswi, Deepika K, Uday Bhaskar Reddy, Delina Yadav K, Joshna K Abstract - It is quite difficult to communicate with deaf individuals. This article extends the complexity of Indian Sign Language (ISL) character classification. Sign language is insufficient for the hearing and speaking disabled. Hand gestures of disabled individuals may appear confused to those who have not learnt the language. Communication should be two-way. In this essay, we will discuss how to learn a language through sign language. Images are processed using computer vision processes, including grayscale conversion, dilation, and masking. We employ Convolutional Neural Networks (CNN) to train and recognize images. Our example has an accuracy of approximately 95%. Gestures serve as a nonverbal communication tool in language. People with hearing or speech difficulties frequently utilize them to communicate with others or among themselves. Many loudspeakers are created by various manufacturers around the world. This study demonstrates that many experiments are undertaken each year, with several articles published in journals and conferences, and that research on vision-based gesture recognition is ongoing. Cognitive navigation focuses on three areas: information retrieval, environmental information, and gesture representation. In terms of identity verification, we also evaluated the authentication system's effectiveness. The physical movement of the human hand generates gestures, and gesture recognition contributes to improvements in autonomous vehicle operation. This paper use the convolutional neural network (CNN) classification technique to detect and recognize human motions. This workflow consists of region-of-interest coordination via masking, finger segmentation, normalization of segmented finger pictures, and finger recognition using a CNN classifier. Use the mask to separate the hand portion of the image from the rest of the image. The histogram equalization approach is used to improve the contrast of each pixel in an image. This work uses a variety of scanning techniques to classify fingerprints from hand photographs. The segmented fingers from the hand image are put into the CNN classification algorithm, which separates the image into different groups. This research proposes gesture recognition and recognition methods based on CNN classification, and the technology achieves good performance using cutting-edge methodologies.
Authors - Anudeep Arora, Ranjeeta Kaur, Neha Tomer, Vibha Soni, Neha Arora, Anil Kumar Gupta, Lida Mariam George, Prashant Vats Abstract - The incorporation of data analytics into internal audit operations is a noteworthy progression in augmenting the efficacy and productivity of audits. In this paradigm, strategic analysis refers to using data-driven insights to evaluate risks, expedite audit procedures, and enhance organizational controls. This article examines the use of strategic analysis in data analytics and internal audits, including important techniques, advantages, and difficulties. It talks about how sophisticated data analytics methods, such as machine learning, statistical analysis, and visualization software, can change the way that auditing is done today. In addition, the paper looks at case studies and potential future developments in the subject, giving readers a thorough understanding of the various ways internal auditors might use data analytics to provide audit results that are more precise and useful.
Authors - Artika Singh, Manisha Jailia Abstract - Effective management of infectious disease outbreaks rely heavily on informed decision-making processes. There are many approaches given for decision-making some of them are expert decision-making, creative problem solving, public engagement, and decision-making under deep uncertainty (DMDU) in outbreak management (OM). The integration of these aspects is critical to enhancing the responsiveness and efficiency of public health interventions. This paper discusses the current state of expert decision-making processes, the role of creativity in managing complex situations, the impact and challenges of incorporating public and patient engagement (PPE) in OM. The paper concludes with recommendations for future research and practice to improve outbreak management strategies.
Authors - Prateeksha P Malagi, Priyanka R Patil, Shamshuddin K G, Suneeta V Budihal Abstract - The advent of blockchain technology presents a transformative opportunity for enhancing the integrity and efficiency of voting systems. This paper explores the design and implementation of a blockchain-based voting system aimed at addressing common challenges faced in traditional electoral processes, such as voter fraud, lack of transparency, and low participation rates. By leveraging the decentralized and immutable nature of blockchain, our proposed system ensures secure voter authentication, real-time vote tracking, and tamper-proof record keeping. The study outlines the technical architecture, including smart contracts and cryptographic techniques, while evaluating the system's performance through simulated voting scenarios. Furthermore, we discuss the implications of this technology for promoting democratic engagement and restoring public trust in electoral outcomes. Our findings suggest that a blockchain-based voting system not only enhances security and transparency but also offers a scalable solution to modern electoral challenges.
