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.
