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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Manjaragi, Shivanand V. Asrani, Deepak Kumar, Arvind Sandesh, R. Maindola, Meenakshi Sai, Aarush Department of Computer Science and Engineering Hirasugar Institute of Technology Karnataka Nidasoshi591236 India Department of Computer Science and Engineering MIET Uttar Pradesh Greater Noida India SCA MRIIRS Haryana Faridabad India Department of Computer Science and Engineering ATME College of Engineering Karnataka Mysuru India Department of Computer Science Engineering Graphic Era Deemed to be University Uttarakhand Dehradun India Department of CSBS Gyan Ganga Institute of Technology and Sciences Madhya Pradesh Jabalpur India

ISBN: (纸本)9798350374933

Man-in-the-Middle (MitM) cyber-attacks provide a substantial risk to network security, since they allow hostile individuals to intercept and alter communications between two parties. Conventional detection approaches frequently fail to recognize these complex threats, requiring the creation of more resilient and precise solutions. This study introduces a model called XGBoost-based Gradient Boosting Machine (GBM) to identify MitM attacks in advance. The system employs extensive datasets from Kaggle, namely CICIDS 2017 and UNSW-NB15, for the purpose of training and evaluating the model. By carefully preparing the data, selecting relevant features, and doing thorough cross-validation with hyperparameter optimization, the suggested model attains exceptional performance metrics. The assessment findings indicate an accuracy of 0.98, precision of 0.97, recall of 0.96, F1-Score of 0.965, and a remarkable ROC-AUC score of 0.99. Our model regularly outperforms existing technologies like as Random Forest and Traditional GBM in identifying MitM attacks, achieving higher performance metrics across the board. This research demonstrates the effectiveness of the XGBoost-based GBM model in improving cybersecurity defenses and suggests that it might serve as a standard for future advancements in cyber-attack detection systems. The suggested system's exceptional accuracy and resilience make it an invaluable tool for proactively detecting and reducing MitM attacks. © 2024 IEEE.

关键词： Cyber attacks

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An Analyzation Of Data Privacy In Sharing And Storing Data In Cloud Environment 10

An Analyzation Of Data Privacy In Sharing And Storing Data I...

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10th International Conference on Electrical Energy Systems, ICEES 2024

作者： Subhasreevignani, R.S. Babu, N. Raji, D. Sowmya, K.N. Afreen Zaiba, S. Reddy Abhishek, G. Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Andhra Pradesh India Andhra Pradesh India

ISBN: (纸本)9798350353778

The Wide spread use of cloud storage services and their demonstrated power provide the foundational underpinning for the cloud computing industry rapid development. Nevertheless, a lot of security incidents still occur at the cloud storage layer due to deliberate attacks and management negligence, resulting in significant leaks of critical data. To address the issue, we inculate AES and Cryptographic Pseudo Random Number Generator Algorithms. The proposed standard illustrates the process of dividing the data into segments which is encrypted with aes individually to ensure the strong security for data. By distributing the scattered data segments among several cloud sites, security is improved and the possibility of a single point of failure is decreased. Securing access control mechanisms makes it easy for authorized individuals to access and decode data. © 2024 IEEE.

关键词： Cloud security

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Patient Monitoring for Personalized Mobile Health (PMH) Based on Medical Virtual Instruments

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第16期17卷 82-94页

作者： Sweetline, B. Christina Vijayakumaran, C. Samydurai, A. Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu KattankulathurChennai India Department of Computer Science and Engineering SRM Valliammai Engineering College Tamil Nadu KattankulathurChennai India

One of the newest technologies, mobile health, has the potential to support the provision of care for older adults and offer them individualised treatment. This study’s goal is to evaluate the benefits and challenges of personalised mobile health (PMH) for elderly residential care. Virtual worlds are quickly integrating into the landscape of instructional technologies. One of the most well-known of these settings is Second Life (SL). Despite the potential of SL for health professions education, there aren’t many official SL applications for this purpose, and the effectiveness of these applications hasn’t been evaluated to the fullest extent possible. Similarly, it appears that nothing is known about the use of virtual worlds for continuing medical education. In order to better grasp the fundamentals of the aid of MVIs for personal health monitoring (PHM), we were able to pinpoint the key disease regions, sensors, channels, calculations and communication protocols. The main obstacles limiting MVIs’ degree of integration into the international health care system were also identified. The analysis demonstrates that MVIs offer an excellent possibility for the creation of affordable, personalised health systems that meet the unique equipment requirements of a certain field of medicine. © 2023 by the authors of this article. Published under CC-BY.

关键词： mHealth

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The Politics of Fear and the Experience of Bangladeshi Religious Minority Communities Using Social Media Platforms

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Proceedings of the ACM on Human-computer Interaction 2024年第CSCW2期8卷 1-32页

作者： Rifat, Mohammad Rashidujjaman Das, Dipto Podder, Arpon Jannat, Mahiratul Soden, Robert Semaan, Bryan Ahmed, Syed Ishtiaque Department of Computer Science University of Toronto TorontoON Canada Khulna University of Engineering & Technology Khulna Bangladesh Department of Women and Gender Studies University of Dhaka Dhaka Bangladesh Computer Science and School of the Environment University of Toronto TorontoON Canada Department of Information Science University of Colorado Boulder BoulderCO United States

Despite significant research on online harm, polarization, public deliberation, and justice, CSCW still lacks a comprehensive understanding of the experiences of religious minorities, particularly in relation to fear, as prominently evident in our study. Gaining faith-sensitive insights into the expression, participation, and inter-religious interactions on social media can contribute to CSCW’s literature on online safety and interfaith communication. In pursuit of this goal, we conducted a six-month-long, interview-based study with the Hindu, Buddhist, and Indigenous communities in Bangladesh. Our study draws on an extensive body of research encompassing the spiral of silence, the cultural politics of fear, and communication accommodation to examine how social media use by religious minorities is influenced by fear, which is associated with social conformity, misinformation, stigma, stereotypes, and South Asian postcolonial memory. Moreover, we engage with scholarly perspectives from religious studies, justice, and South Asian violence and offer important critical insights and design lessons for the CSCW literature on public deliberation, justice, and interfaith communication. © 2024 Copyright held by the owner/author(s).

关键词： Social psychology

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Modeling a Hybrid Stemmer for Kokborok

Modeling a Hybrid Stemmer for Kokborok

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2023 International Conference on Intelligent Systems, Advanced Computing and Communication, ISACC 2023

作者： Debbarma, Pankaj Hrangkhawl, Pusparwng Debbarma, Swapan Tripura Institute of Technology Department of Computer Science and Engineering Narsingarh India Ambedkar College Department of Information Technology Fatikroy India National Institute of Technology Department of Computer Science and Engineering Agartala India

ISBN: (纸本)9798350399387

Kokborok is a resource scarce and vulnerable language and is spoken only by around a million people in the north-east Indian state of Tripura. Lots of unstructured textual data in Kokborok is now available however efficient data mining for such a resource is not applicable. Reasons are the absence of an efficient pre-processing step that can help in solving the information retrieval or natural language processing. In this work, we present a model of hybrid stemming algorithm for the vulnerable and resource-scarce Kokborok language and assess its performance using various methods. Evaluation of the hybrid stemmer was performed on a corpus of 52,482 words that resulted in achieving an accuracy of 89.28%. Recall and Precision of 89.28% and 89.61 %, respectively, with F1-measure of 89.44 has been found for the stemmer. © 2023 IEEE.

关键词： Data mining

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Optimizing Energy Usage in Educational Environments through the Internet of Things and Deep Learning Techniques 3

Optimizing Energy Usage in Educational Environments through ...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Preethi Salian, K. Salian, Supriya Rao, Abhishek S. Nmam Institute of Technology Department of Information Science and Engineering Nitte India St. Joseph Engineering College Vamanjoor Department of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798350369083

Excessive electricity consumption in educational environments, particularly classrooms, poses significant financial and environmental challenges. Existing solutions utilize various technologies such as surveillance cameras, power monitoring modules, and occupancy sensing systems to detect occupancy and control electrical appliances accordingly. However, the inefficient management of energy resources within classrooms leads to unnecessary consumption and increased costs. Artificial Intelligence (AI) can assist by implementing smart systems that use IoT devices and deep learning algorithms to autonomously detect occupancy and manage electrical appliances. This approach optimizes energy usage, reduces wastage, and leads to more sustainable and cost-effective educational facilities. The proposed study addresses this pressing issue through the integration of IoT technology and deep learning algorithms to autonomously identify occupancy status within classrooms. By leveraging this data, the system optimizes energy usage by controlling electrical appliances, thereby mitigating unnecessary wastage, and promoting sustainable practices within educational institutions. The study assesses existing energy-saving methods in classrooms and identifies their limitations. A novel solution is proposed that utilizes surveillance cameras for occupancy detection, streamlining energy management without additional hardware costs. The proposed model demonstrated 92% accuracy when compared with the state-of-the-art architecture YOLOv3 with SSH, representing conclusive outcomes derived from its operational processes, delineated both qualitatively and quantitatively. © 2024 IEEE.

关键词： Costs

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Preserve the Medical Data using Secure Partially Homomorphic Encryption with Blockchain technology in Smart Healthcare 2

Preserve the Medical Data using Secure Partially Homomorphic...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Kavitha, S. Pavithra, B.S. Shashikala, A.B. Jagadeesh, B.N. Mageswari, P. Uma Marichi, Dileep Nitte Meenakshi Institute Of Technology Department Of Mca Bengaluru India East West Institute Of Technology Department Of Computer Science And Engineering Bengaluru India Rns Institute Of Technology Department Of Information Science And Engineering Bengaluru India Presidency College Department Of Computer Applications Bangalore India

ISBN: (纸本)9798350381665

Healthcare information is vital for both individuals and service providers, necessitating secure sharing and maintenance of electronic healthcare records (EHR). Traditional EHR systems have relied on centralized architectures, such as the cloud, which may expose sensitive information to undisclosed intelligence. However, these systems have struggled to address the diverse needs of stakeholders while ensuring separation, security, and compliance with legal requirements. Blockchain technology offers a decentralized and distributed ledger system, providing secure, verifiable, and immutable data transfer capabilities. By employing cryptographic techniques like hashes, blockchain eliminates the need for a central authority, making data exploitation challenging and evident. This study proposes a blockchainbased architecture utilizing the Proof of Stake (POS) consensus mechanism to authenticate user identities and facilitate secure EHR exchange across multiple healthcare systems. Additionally, to enhance data security, Secure Partially Homomorphic Encryption (SPHE) is recommended for outsourced data, enabling users to perform operations on encrypted data. The proposed technique surpasses alternatives in terms of energy efficiency, authenticity, and protection of medical data. © 2024 IEEE.

关键词： Blockchain

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Congestion Control Routing Scheme for WSN using AI Technologies 3

Congestion Control Routing Scheme for WSN using AI Technolog...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Basha, S.K. Mahaboob Gurumoorthi, E. Beulah, E. Mercy Kesavan, R. Kalaiselvan, S.A. Kumar, Lenin Tkr College of Engineering and Technology Department of Computer Science and Engineering Telangana India Cmr College of Engineering & Technology Department of Computer Science and Engineering Telangana India Vel Tech Multi Tech Dr. Rangarajan Dr.Sakunthala Engineering College Department of Computer Science and Engineering Chennai Avadi India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India Vignan Institute of Technology and Sciences Department of Electronics Communication and Engineering Telangana India

ISBN: (纸本)9798350369083

An essential part of wireless sensor networks (WSN) is managing congestion, which occurs when the amount of traffic exceeds the combined or individual capacities of the underlying channel. As a result, developing more advanced methods to prevent, detect, and alleviate congestion requires particular considerations. In order to obtain the highest throughput, these strategies must take into account the limited resources of the WSN. Dedicated congestion control strategies and routing protocols that help in congestion detection as well as management are two of the many methods that have been created in recent years. As a means of avoiding congestion, the sink node in the previous designs would reset the topology and reduce bulk traffic. In this article, we lay the groundwork for WSNs and AI, the latter of which will try to prevent congestion in WSNs from the get-go and the former of which will try to minimize the spread of impending congestion by rerouting traffic. An integral part of modern life, Artificial Intelligence (AI) allows us to create self-governing systems. It is possible to enhance performance and reliability by making use of several sophisticated AI approaches. Better network management and routing in WSN may be the outcome of studying the AI algorithm used by WSN, leading to a more dependable network. Here we'll take a look at several AI algorithms that have found their way into WSNs, and we'll talk about how these algorithms might help WSNs overcome their challenges and become more reliable and efficient. Additionally, artificial intelligence has often been disregarded in these networks because of limitations on data processing as well as power consumptions. Thanks to AI, WSN is now lightning fast, error-free, as well as effective in almost every way. Using AI methods in nodes of wireless sensors for congestion aware routes is the goal of this work. © 2024 IEEE.

关键词： Traffic congestion

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Enhancing Accuracy with Recursive Feature Selection Using Multiple Machine Learning and Deep Learning Techniques on NSL-KDD Dataset 1

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2nd International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Mohanty, Subrat Kumar, Satendra Agarwal, Mayank Department of Mathematics Indian Institute of Technology Patna India Department of Computer Science and Engineering Indian Institute of Technology Patna India

ISBN: (数字)9789819995189

ISBN: (纸本)9789819995172

The world has moved toward digital revolution and more and more services are now being available online. This has presented significant challenges in ensuring the availability, integrity, and confidentiality of networks. Therefore, the implementation of intrusion detection systems (IDS) that actively monitor network activity and dynamically analyze incoming traffic has become essential. In modern computer networks, IDS play a critical role in identifying and alerting administrators of any malicious activities that could threaten data *** research aims to evaluate machine learning (ML) and deep learning (DL) techniques for intrusion detection using the NSL-KDD dataset. While having many features may seem advantageous, it doesn’t always lead to improved performance on large datasets. Reducing and picking just the right set of features may improve both speed and accuracy. This is why a method called recursive feature elimination (RFE) is utilized to select the most relevant features for the NSL-KDD dataset. In order to test the efficacy of ML and DL methods in IDS, we have undertaken a comprehensive experiment. Specifically, we tested the effectiveness of several algorithms, including CNN, RNN, LSTM, GB, CART, and CatBoost. Our study involved evaluating the performance of various algorithms and comparing them, and the detection accuracy [lowest-highest] for all the attacks are as follows: DoS [98.79–99.81%], Probe [96.34–99.64%], R2L [96.15–98.66%], U2R [79.98–99.73%]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Intrusion detection

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Femur Osteoporosis Risk Assessment through Deep Learning Models

Femur Osteoporosis Risk Assessment through Deep Learning Mod...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Kaur, Sandeep Singh, Jaspreet Guru Nanak Dev University Department Of Computer Engineering & Technology Punjab Amritsar India Chandigarh University Department Of Computer Science & Engineering Punjab Mohali India

ISBN: (纸本)9798350366846

One common health problem, especially among older people, is osteoporosis. This condition is marked by decreased bone density and an increased risk of fractures. Effective intervention and treatment rely on early diagnosis and risk assessment. Using deep learning models called Faster R-CNN (Region-based Convolutional Neural Networks), we provide a new method for assessing the risk of femoral osteoporosis in this research. The approach requires amassing X-ray pictures of the femur from a varied group of people, including those with varied levels of bone health. Image quality and feature normalization are improved using preprocessing approaches. In order to precisely identify and pinpoint areas of interest pertaining to structural integrity and bone density, the Faster R-CNN model is trained using annotated datasets. The Faster R-CNN model successfully identifies osteoporotic characteristics in femur X-rays, as shown by our findings. The model is able to differentiate between normal and osteoporotic bone structures with a high degree of sensitivity and specificity. The approach is also well-suited for clinical uses and large-scale screening programmes because of how quickly it can examine data. © 2024 IEEE.

关键词： Risk assessment

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