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.
