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4th International Conference for Emerging technology, INCET 2023

作者： Nair, Keertana Gopikrishna, P.B. Rubell Marion Lincy, G. Indian Institute of Information Technology Kottayam Department of Computer Science and Engineering Kottayam Pala India

ISBN: (纸本)9798350335750

Brain-computer Interface (BCI) is a system which is used to interact with the computer system and can be used to control different assistive devices by utilizing the brain signals such as Electroencephalography (EEG). Motor Imagery (MI) is considered one of the prominent fields of BCI systems which are based on EEG signals. These systems have the capability to restore motor ability in humans. Though a good deal of Machine Learning (ML) approaches were investigated in recent years, studies that explore BCI with Deep Learning methods or Wavelet Scattering Transforms have not been extensively used. Also, conventional classification methods show longer computational time and they are incapable of processing non-linear and non-stationary EEG signals. The proposed system aims to explore the area of a calibration-free or a subject-independent model by integrating Deep Convolutional Neural Network (CNN) with Wavelet Scattering Network by utilizing feature maps learned from both CNN and Scattering networks to tackle the non-linearity and non-stationarity of the EEG signals and thereby to improve the classification accuracy of the model to build a more robust and generalized MI-based BCI system. The proposed model outperforms the state-of-the-art techniques, achieving an accuracy of 87% and 93% on BCIC IV 2a and SMR-BCI datasets respectively. © 2023 IEEE.

关键词： Brain computer interface

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Prediction of strength properties of concrete under the influence of recycled aggregate using machine learning models

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Interactions 2024年第1期245卷 1-21页

作者： Ashwathi, R. Soundariya, R.S. Tharsanee, R.M. Yuvaraj, S. Ramya, R. Department of Civil Engineering Bannari Amman Institute of Technology Erode India Department of Computer Technology Bannari Amman Institute of Technology Erode India Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Civil Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Information Technology Dr NGP Institute of Technology Coimbatore India

The upsurge in urbanization is depleting natural resources due to the wide usage of aggregates in construction, posing a menace to further progress. If the contemporary state persists, it becomes a threat for further progress. As an initiative to preserve the natural resources, recycled aggregate is employed as a partial additive of fine aggregate, this practice endorses sustainability but demands a cautious investigation of its effect on concrete strength. Predicting the strength characteristics of concrete becomes crucial in this situation. Machine Learning (ML) algorithms estimate these properties, enabling engineers to evaluate and enhance the concrete mix with recycled materials, confirming durability and performance without solely depending on outdated testing methods The proposed work attempts how ML algorithms have been utilized to forecast the strength properties of concrete. Input samples were collected from the experimental setup for three grades of concrete (M15, M20, M25) with four various proportions (5%, 10%, 15% and 20%) at 7, 14, 21 and 28 ages of curing excluding the conventional mix. The ML models are developed by training the collected laboratory samples with respect to four parameters– cement, fine aggregate (FA), coarse aggregate (CA) and recycled aggregate (RA). The trained models predict the three properties of concrete– compressive strength, split tensile strength and flexural strength. The test outcomes are further validated using statistical measures such as correlation coefficient– R2, Mean Square Error (MSE) and Mean Absolute Error (MAE) to assess the performance of the model. The performance evaluation of the proposed approach shows that The Support Vector Regressor outperforms the other ML models, achieving an R² value of about 90% and showing a reduced error rate in terms of MSE and MAE. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Tensile testing

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An efficient machine learning based CPU scheduler for heterogeneous multicore processors

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International Journal of information technology (Singapore) 2025年第4期17卷 2493-2501页

作者： Allaqband, Sugariya Firdous Nazish, Mir Allaqband, Saltanat Firdous Bashir, Janibul Banday, M. Tariq Department of Electronics & amp Instrumentation Technology University of Kashmir Srinagar India Department of Computer Science & amp Engineering Indian Institute of Technology Madras India Department of Information Technology National Institute of Technology Srinagar India

While machine learning has excelled in various domains, its impact on computer architecture has been limited. Despite powerful, flexible multicore CPUs, efficient thread scheduling remains challenging due to mapping overhead. This study introduces a novel approach that leverages machine learning to enhance CPU scheduling in heterogeneous multicore systems by developing a mapping methodology that improves system throughput. The proposed scheduling approach uses Long Short-Term Memory, Artificial Neural Networks, and Linear Regression to predict thread parameters and Instructions Per Cycle for threads running on different cores. The predicted values are then utilized to schedule threads using the mapping that maximizes Instructions per Cycle count. The results demonstrate that the artificial neural network outperforms Linear Regression for predicting Instructions Per Cycle values. In addition, compared to traditional heterogeneous scheduling, the proposed machine learning-based scheduling approach enhances the performance of the heterogeneous system by about 1.2 times and increases throughput by 20%. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： ANN CPU scheduling Linear regression LSTM Machine learning

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An approach to on-stream DDoS blitz detection using machine learning algorithms

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Materials Today: Proceedings 2023年 80卷 3492-3499页

作者： Manjula, H.T. Neha Mangla Atria Institute Of Technology Bengaluru India Department Of Computer Science & Engineering CMRIT Bengaluru India Department of Information Science & Engineering Atria Institute of Technology Bengaluru India

Distributed Denial of service (DDoS) attacks is an enormous threat to today's cyber world, cyber networks are compromised by the attackers to distribute attacks in a large volume by denying the service to legitimate users. The toughest and challenging task in today's network and network security engineers is to identify compromised traffic (attacked) and legitimate (normal) traffic. The main goal of the paper is to detect DDos attacks using classification algorithms. To achieve the goal the proposed system uses attacking tool to initiate attacks using Loic attacking tool with the data set extracted from open source tool Wireshark and transferring the dataset to apache Spark for detection analysis. The system also uses Apache spark machine learning algorithms (MLib), classification algorithms to classify the dataset. We use Naive Bayes, KNN and Random forest classification algorithms to classify normal traffic and attacked traffic. Our system is capable of detecting attacks with respect to any traffic protocols ICMP, TCP, or UDP. The accuracy of detection is compared on three classification algorithms and noted that random forest gives the accuracy of 96.75%. © 2021

关键词： Denial-of-service attack

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Reinforcement Learning based Energy Management system for Hybrid Electric Vehicles 3

Reinforcement Learning based Energy Management system for Hy...

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3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems, ACCESS 2023

作者： Vishnu Narayanan, A. Lincy G, Rubell Marion Gopakumar, Parvathy Indian Institute of Information Technology Kottayam Department of Computer Science and Engineering Kottayam Pala India

ISBN: (纸本)9798350346169

Hybrid Electric Vehicles (HEV) (Self-charging Hybrid Vehicles) have been exemplified as the timely, practical, balanced solution for fuel-efficiency and eco-friendly environment. Considering the design perspective of the powertrain, the different strategies employed for Energy Management have depicted vast improvements in the energy efficiency of the hybrid powertrain models. With companies like deep-mind promoting Reinforcement Learning (RL) with the human-level efficiency to task multi-games, RL-based algorithms have been arduously researched to develop Energy Management System (EMS) owing to the capability to self-learn interacting with any complex environment by the return in rewards for each action. The recent research proved this to be on a larger efficient scale compared to the predefined algorithms. This article reviews the different powertrain models and their working along with some basic RL algorithms for any alterations from the pre-existing models to improve the efficiency and develop the easiness of the management system. Ample importance is given to the classification of EMS, and the primary EMS created using RL in this review. A model for the EMS is also discussed along with the results. The future scope of work is also discussed. © 2023 IEEE.

关键词： Energy efficiency

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Unleashing the Potential of Data science Driven Society 5.0: Applications and Implications 5

Unleashing the Potential of Data Science Driven Society 5.0:...

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5th IEEE India Council International Subsections Conference, INDISCON 2024

作者： Saurabh, Kumar Mishra, Supriya Kabra, Avani Teja, M. Adithya Kumar, Alok Indian Institute of Information Technology Allahabad Department of Information Technology Prayagraj India Sir Padampat Singhania University Department of Computer Science & Technology Udaipur India Sardar Vallabhbhai National Institute of Technology Department of Computer Science & Engineering Surat India

ISBN: (纸本)9798350376753

In Society 5.0, social reform will lead to a forward-thinking community, whose members respect one another across generations, and where everyone can lead a fulfilling life. The success of Society 5.0 depends on various academic fields of data science, including Social engineering, Big Data, Machine Learning, Artificial Intelligence, and Natural Language Processing. Given its influence on the real world, data science has a broad reach for the present and the future. To fully realise its relationship with Society 5.0 innovations, we present a comprehensive review on the applications and implications of data science in Society 5.0, which is lacking in the existing literature. To address these gaps, this study focuses on specific data science technologies and explores the diverse applications across industries, smart cities, healthcare, and education intending to shed light on how data science can be harnessed to bridge the gap between the Utopian vision of Society 5.0 and its tangible realization. © 2024 IEEE.

关键词： Data science

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Academic Dishonesty Detection in Exams Using Pose Extraction 7th

Academic Dishonesty Detection in Exams Using Pose Extraction

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7th International Conference on Inventive Systems and Control, ICISC 2023

作者： Binu, Dhanush Bellamkonda, Sivaiah Department of Computer Science and Engineering Indian Institute of Information Technology Kottayam Kerala Kottayam India

ISBN: (纸本)9789819916238

Academic dishonesty has been a significant setback to the proper functioning of educational institutions worldwide, as cheating in examinations is a significant and persistent issue contributing to academic dishonesty. With cases of cheating in examinations increasing year after year, it can be asserted that traditional invigilation and monitoring methods are ineffective. This work proposes a method to analyze cheating behavior in examination halls through pose estimation. First, we analyze the frame for any movement using a motion detection algorithm. The marked frame is sent to the pose extraction phase, where the pose of the person/persons present in the frame is extracted and given to the machine learning model, which identifies whether a student has been cheating. Furthermore, the frame marked with face recognition is stored for later use, along with the time and name of the student, in a separate file for reference. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Which is more faithful,seeing or saying? Multimodal sarcasm detection exploiting contrasting sentiment knowledge

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CAAI Transactions on Intelligence technology 2025年第2期10卷 375-386页

作者： Yutao Chen Shumin Shi Heyan Huang School of Computer Science and Technology Beijing Institute of TechnologyBeijingChina Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications BeijingChina

Using sarcasm on social media platforms to express negative opinions towards a person or object has become increasingly ***,detecting sarcasm in various forms of communication can be difficult due to conflicting *** this paper,we introduce a contrasting sentiment-based model for multimodal sarcasm detection(CS4MSD),which identifies inconsistent emotions by leveraging the CLIP knowledge module to produce sentiment features in both text and ***,five external sentiments are introduced to prompt the model learning sentimental preferences among ***,we highlight the importance of verbal descriptions embedded in illustrations and incorporate additional knowledge-sharing modules to fuse such imagelike *** results demonstrate that our model achieves state-of-the-art performance on the public multimodal sarcasm dataset.

关键词： CLIP image-text classification knowledge fusion multi-modal sarcasm detection

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Reputation-based Resource Allocation Prioritization in 6G: A Coalitional Game Theoretic Approach 15

Reputation-based Resource Allocation Prioritization in 6G: A...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Pathak, Vivek Rathore, Muktesh Singh Sharma, Aryan Pandya, Rahul Jashvantbhai Department of Electrical Engineering Indian Institute of Technology Dharwad India Department of Engineering Physics Indian Institute of Technology Dharwad India Department of Computer Science and Engineering Indian Institute of Technology Dharwad India

ISBN: (纸本)9798350370249

Exploiting the 0.1-10 THz spectrum range in sixth-generation (6G) wireless networks promises significant advancements;however, it presents challenges in resource allocation (RA) and security to meet diverse user demands. This paper introduces a novel approach integrating blockchain technology (BCT) and deep learning (DL) for small base station (SBS) coalition formation (CF) and RA. We address critical challenges in dynamic RA and CF, presenting a comprehensive framework grounded in both theoretical and simulation-based analyses. By leveraging BCT, secure retrieval of reputation data is ensured, thereby establishing trust within the network. Our proposed algorithm dynamically optimizes the reputation-to-demand (R/D) ratios, prioritizing SBSs with higher R/D ratios for RA, thus enhancing network efficiency. Further, notable improvements in network performance metrics, validated through extensive simulations and theoretical calculations. The adaptability of the algorithm allows SBSs to enhance their reputation via increased upload capacity and coalition participation. Utilizing Shapley values and network monitoring, the algorithm optimizes CF and the operational status of SBSs. The stability and reliability of the network are assured through Nash equilibrium checks and reputation integrity verification. This comprehensive implementation illustrates that our proposed methodology offers an efficient solution to dynamic wireless challenges, optimizing RA and CF in evolving environments. The integration of DL and BCT provides a robust framework for efficient wireless network management. Our simulations lay a strong foundation for future research in 6G network optimization, emphasizing the proposed solutions’ practical applicability, scalability, and resilience. Key innovations include dynamic R/D ratio optimization, blockchain-based secure reputation data retrieval, adaptable reputation improvement algorithms, efficient coalition formation using Shapley values, Nash equili

关键词： Resource allocation

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A Survey on Eliminating Botnet and Intrusion Attacks through Machine Learning

A Survey on Eliminating Botnet and Intrusion Attacks through...

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Faseela, M.H. Manjith, B.C. Dept. Computer Science and Engineering Indian Institute of Information Technology Kerala Kottayam India

ISBN: (纸本)9798350325188

With the rising linkage and dependence on computer networks, the threat of intrusions and botnets has become a critical concern for organizations and those involved. Malicious actors are in control of unauthorized access or actions that harm the security and integrity of computer systems and insecure devices. The identification of attacks and botnets is of utmost importance in ensuring the security of networks and safeguarding confidential information. In recent years, there has been a growing interest among researchers and organizations in developing unique evaluation methods that can adapt to emerging threats and apply intelligent algorithms such as sparse modelling, transfer learning, distributed computing, or meta-learning capable of detecting, classifying, and predicting previously unknown attack patterns, which can result in machine learning and deep learning models that are more accurate and efficient. This review paper compares the performance of machine learning and deep learning approaches such as Naïve Bayes, Support Vector Machines (SVM), and Logical Regression for intrusion and botnet attacks in various network platforms. This paper provides a comprehensive comparison of machine-learning approaches used in the cyber realm for botnet and intrusion detection and encompasses an analysis of the datasets utilized, the specific cyber issues addressed, and the resultant outcomes. Furthermore, it offers valuable insights into the challenges and advancements within the realm of cybersecurity, thereby facilitating the development of novel approaches and technologies aimed at enhancing digital safety. © 2023 IEEE.

关键词： Botnet & Intrusion detection Cybersecurity Machine learning & Deep learning Techniques

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