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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Rajbongshi, Linuskrishna Deka, Sanjib K. Tezpur University Department of Computer Science and Engineering Assam Tezpur India

ISBN: (纸本)9798331531195

Spectral efficiency (SE) and energy efficiency (EE) are the two key performance metrics for achieving success in 6G heterogeneous networks (HetNets), especially in environments with high user density and limited spectral resources. The problem of user association and channel allocation while improving network data rate considering varying user density in 6G HetNets is challenging. In this paper, we propose a game-theoretic framework to optimize user association and channel allocation, aiming to improve the overall network data rate while considering direct and relay-assisted communication links. The problem is formulated as a cooperative game where user equipment (UE), relays, and base stations act as players, competing for optimal channel usage to maximize the network data rate. A Vickrey-Clarke-Groves (VCG) auction-based equilibrium is established to ensure stable and efficient resource allocation across the network. The proposed technique consists of two stages: an efficient user association algorithm based on user-relay proximity and link quality, and a channel allocation mechanism using auction theory to optimize spectral usage while minimizing interference. Simulation results show that the proposed method significantly improves spectral and energy efficiency, and demonstrates robust performance under varying user densities and network configurations. © 2025 IEEE.

关键词： Spectrum efficiency

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Distance Metric-Based Classification Comparisons for a Brain-computer Interface Authentication

Distance Metric-Based Classification Comparisons for a Brain...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Lewis, Tyree Agarwal, Rupal Andujar, Marvin University of South Florida Department of Computer Science and Engineering University of South Florida United States Department of Computer Science and Engineering TampaFL33620 United States Faculty of Computer Science and Engineering TampaFL33620 United States

ISBN: (纸本)9798350337020

The rise of security concerns has spurred on-going research into Brain-computer Interfaces (BCI) based authentication. These applications utilize electroencephalogram (EEG) signals, due to their properties that can enhance security systems. In previous studies, EEG data has been incorporated into various authentication systems to compare the performance of new and existing classification methods. However, using EEG data to compare the performance of distance metrics in a P300-based BCI authentication system has not been explored yet. In this study, EEG data is used to determine the most effective distance metric for authenticating users in a closed-loop system. To accomplish this task, we conducted a longitudinal study to evaluate three distance metrics (Cosine, Correlation and Chebyshev) while participants interacted with our BCI authentication system. Our results indicated that the Cosine similarity outperformed all other distance metrics for each user. © 2023 IEEE.

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A NOTE ON THE QUALITATIVE BEHAVIOUR OF AN ODE MODEL RELATED TO ENDOTOXIN TOLERANCE

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2024年第4期86卷 127-130页

作者： Olteanu, Mircea Ştefan, Radu Department of Mathematical Methods and Models Faculty of Applied Sciences University "Politehnica" of Bucharest Romania Department of Automatic Control and Systems Engineering Faculty of Automatic Control and Computer Science University "Politehnica" of Bucharest Romania

关键词： Differential equations

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Deep Learning-based Classification of Infant Cries for Parental Assistance: A Study using MFCC and CNN Technique 3

Deep Learning-based Classification of Infant Cries for Paren...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Mala, B.M. Darandale, Smitha Sandeep Gitam School of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331509675

Worldwide Approximate count of 130 million babies were born per annum. Maintaining newborn babies is a great difficulty, mainly for first-time parents. However, intimations from experienced parents, books, and videos help to a certain extent but not significantly because of difficulty understanding the meaning of Infant cries. Researchers have proposed several techniques using machine learning and artificial intelligence. In this work, we try to leverage deep learning-based techniques such as convolutional neural networks (CNN) to identify the infant crying. Infant crying is further classified into four classes (inclusive of pain, hunger, sleepiness, and wet diaper). The MFCC (Mel-Frequency Cepstral Coefficients) technique is used for feature extraction, as it efficiently captures audio characteristics from crying sounds. In this study, will compare the various existing machine learning models with our proposed model. Among the models evaluated, MFCC combined with Convolutional Neural Networks (CNN) achieves the highest accuracy of 95.5%, for classifying the infant cry and reasoning. © 2025 IEEE.

关键词： Convolutional neural networks

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sEMG-based Sarcopenia risk classification using empirical mode decomposition and machine learning algorithms

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Mathematical Biosciences and engineering 2024年第2期21卷 2901-2921页

作者： Kumar, Konki Sravan Lee, Daehyun Jamsrandoj, Ankhzaya Soylu, Necla Nisa Jung, Dawoon Kim, Jinwook Mun, Kyung Ryoul Center for Artificial Intelligence Korea Institute of Science and Technology Seoul Korea Republic of KHU-KIST Department of Converging Science and Technology Graduate School Kyung Hee University Seoul Korea Republic of Department of Human Computer Interface and Robotics Engineering University of Science and Technology Daejeon Korea Republic of Department of Computer Science Ozyegin University Istanbul Turkey

Early detection of the risk of sarcopenia at younger ages is crucial for implementing preventive strategies, fostering healthy muscle development, and minimizing the negative impact of sarcopenia on health and aging. In this study, we propose a novel sarcopenia risk detection technique that combines surface electromyography (sEMG) signals and empirical mode decomposition (EMD) with machine learning algorithms. First, we recorded and preprocessed sEMG data from both healthy and at-risk individuals during various physical activities, including normal walking, fast walking, performing a standard squat, and performing a wide squat. Next, electromyography (EMG) features were extracted from a normalized EMG and its intrinsic mode functions (IMFs) were obtained through EMD. Subsequently, a minimum redundancy maximum relevance (mRMR) feature selection method was employed to identify the most influential subset of features. Finally, the performances of state-of-the-art machine learning (ML) classifiers were evaluated using a leave-one-subject-out cross-validation technique, and the effectiveness of the classifiers for sarcopenia risk classification was assessed through various performance metrics. The proposed method shows a high accuracy, with accuracy rates of 0.88 for normal walking, 0.89 for fast walking, 0.81 for a standard squat, and 0.80 for a wide squat, providing reliable identification of sarcopenia risk during physical activities. Beyond early sarcopenia risk detection, this sEMG-EMD-ML system offers practical values for assessing muscle function, muscle health monitoring, and managing muscle quality for an improved daily life and well-being. © 2024 American Institute of Mathematical sciences. All rights reserved.

关键词： Empirical mode decomposition

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A novel relative entropy based FCM algorithm using interval type-2 fuzzy set at local membership functions for brain MR image segmentation 3

A novel relative entropy based FCM algorithm using interval ...

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3rd IEEE International Conference on Control, Instrumentation, Energy and Communication, CIEC 2024

作者： Ray, Madhumita Sing, Jamuna Kanta Jadavpur University Computer Science and Engineering Department Kolkata India

ISBN: (纸本)9798350313703

In this proposed article, we develop a fuzzy c-means algorithm based on relative entropy for segmentation of noisy volumetric brain MR images and introduce interval type-2 fuzzy membership functions by using local membership functions as a secondary membership function. The brain magnetic resonance images are influenced by high level of intensity inhomogeneity (IIH) and noise, because of the limitations of machinery devices and cognitive limitations of the operators. As a result it produces blurry images, mostly at the soft tissue boundary regions. This phenomenon increases uncertainty for the image soft tissue classification. The proposed algorithm handle this issue by incorporating interval type-2 fuzzy (IT2F) set at the local membership functions to reduce the underlying uncertainty factors of each voxels more efficiently. It also integrates the total uncertainties for volumetric images by utilizing relative entropy. At first, the proposed algorithm generates the global and local membership functions for each voxel, next, it generates type-2 fuzzy (T2F) set by considering local membership functions as a secondary membership functions and finally, the algorithm generates IT2F set by doing direct defuzzificaton of the T2F set. We conduct qualitative and quantitative experiments on ten different simulated volumes of brain MR images. The results revel the outcome of the experiments and it indicates the supremacy of the proposed algorithm over the state-of-the-art algorithms established in near past. © 2024 IEEE.

关键词： Fuzzy clustering

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Content Filtering Based Navigation Recommendation System Using NLP 9

Content Filtering Based Navigation Recommendation System Usi...

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Roobini, M.S. David, Snowlin Paz Lokeshwaran, S. Vinothini, E. Aishwarya, D. Sathyabama Institute of Science And Technology Department of Computer Science And Engineering Chennai600119 India

ISBN: (纸本)9798350365092

The surge in tourism information necessitates intelligent systems for efficient decision-making during trip planning. This paper introduces a Context-Aware Navigation Recommen- dation System (CANRS) using Natural Language Processing (NLP) and content filtering. The focus is on improving relevance and personalization by extracting insights from textual data. The framework integrates collaborative filtering, Content-Based Filtering, and NLP, analyzing textual data from social media, reviews, and descriptions for nuanced user preferences. The system utilizes TF-IDF, sentiment analysis, and semantic analysis for precise feature extraction, facilitating personalized travel itineraries. The paper introduces an optimization algorithm refining content filtering, considering location, user history, and real-time events for dynamic recommendation scores. Through experiments, the CANRS demonstrates accuracy and contextually relevant navigation recommendations, adapting to changing user preferences and dynamic contexts. This research leverages NLP to enhance content filtering, providing personalized and contextually aware recommendations. Implications include the development of advanced navigation systems prioritizing user preferences and dynamic contextual information in tourism. © 2024 IEEE.

关键词： Decision making

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Deciphering Deception-LSTM Combats Deepfake Audios 4

Deciphering Deception-LSTM Combats Deepfake Audios

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Acharya, Yushsvi Tyagi, Jahanvi Jangid, Mahesh Manipal University Department of Computer Science and Engineering Jaipur India

ISBN: (纸本)9798350387490

It's safe to say that the proliferation of synthetically generated media, particularly Deepfakes, has raised significant concerns regarding privacy, security, and integrity of digital content. There is a lot of research to detect these deep-fakes, however we believe audio deepfake detection is an ever evolving technology which needs to conquer deepfake generation techniques. In this paper, we propose a Long Term Short Memory model to classify real and fake audios and discuss how different hyperparameters affect the results. Four models have been tested with four different optimisers each to conclude the best model and optimiser for deepfake audio detection. © 2024 IEEE.

关键词： Fake detection

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Swar : A Decentralized Approach to Music Streaming with Blockchain and Machine Learning 5

Swar : A Decentralized Approach to Music Streaming with Bloc...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Nagaveni, L.G. Nandyal, Shravya B. Shetty, Skanda S. Raj, Smriti Dayalan, Geetha Pes University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350376685

Swar is a decentralized music platform, using blockchain and machine learning to change music industry fundamentals, where artists and users will reap the most benefits. Unlike other platforms, Swar offers transparent royalty payment system which is based on streaming data and has dynamic adjustments, thus, contributors are assured of fair remunerations. Through its DApp solution, Swar provides the artists with an opportunity to upload songs, get the allocated royalties using smart contracts, and store the content in the IPFS securely. ML-Model of the platform forecasts streaming numbers and notify artists about NFT creation with regard to the expected popularity and distributes royalties that helps artists make decisions on their work with confidence. On the top of that Swar comes up with Timebound NFTs, that empowers artists to provide special content and experiences to the fans which can be sold on the dedicated marketplace. Intuitive personalized song recommendation system also offers user tailored suggestions including the songs they like the most. Swar aspires to have a music ecosystem which is seen as transparent, fair and engaging to both artists and listeners. © 2024 IEEE.

关键词： Decentralized finance

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Review of Machine Learning-Based Distributed Denial-of-Service (DDoS) Detection and Prevention 1

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Fahim, M.D. Shafayat Hossain, Md. Rosni, Tanjela Rahman Khadija, Sihama Ramjana Hasan, Mahamudul Department of Computer Science and Engineering East West University Dhaka Bangladesh

ISBN: (数字)9789819776030

ISBN: (纸本)9789819776023

A distributed denial-of-service (DDoS) attack is a deliberate attempt to stop a target system, service, or network from operating normally by flooding it with an avalanche of unauthorized traffic or requests. In a DDoS assault, numerous hijacked computers or devices—often forming a botnet—are organized to simultaneously send the target an excessive amount of traffic or requests, overwhelming it and making it unable to react to valid user requests. DDoS attacks, such as volumetric attacks, TCP/IP protocol attacks, application layer attacks, and reflective/amplified attacks, can take many forms and target distinct weaknesses in the target infrastructure. DDoS attack prevention might be difficult, but there are different ways of detecting a DDoS attack. This paper focuses on finding and reviewing different kinds of ML-based DDoS detection techniques suggested in different papers and journals and comparing these methods and finding the best alternate solution. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Denial-of-service attack

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