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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： S. Peerbasha Mohammed I. Habelalmateen Ramachandra A C Revathi P T. Saravanan Department of Computer Science Jamal Mohamed College (Autonomous) Affiliated to Bharathidasan University Tiruchirapalli India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electronics and Communication Engineering Dhanalakshmi Srinivasan College of Engineering and Technology Mamallapuram India Department of Electronics and Communication Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

In modern era, the increased growth in social media platforms and technologies such as Artificial Intelligence (AI) have gained interest towards multimodal sentiment analysis that includes text, audio and visual cues for extraction of useful insights. Although these systems are capable of analyzing sentiment, but also faces certain challenges in synchronization of multimodal inputs, data fusion and reliability under various contexts. Hence, this research proposes an effective Multimodal Transformer Fusion (MTF) which combines the strengths of various modalities to recognize human emotions. Initially, the data is collected from CMU-Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) dataset. Further, the data is preprocessed with Empirical Mode Decomposition (EMD) and Min-max normalization, stop words removal, and lemmatization to remove noise and improve the quality of sentiment analysis. Then, the features are extracted for different input modalities by using Mel-Frequency Cepstral Coefficients (MFCC), 3D Residual Network (ResNet 3D) and Distilled Bidirectional Encoder Representations from Transformers (DistilBERT). After that, extracted features are further fed into proposed MTF to classify the sentiment of a customer into positive, negative or neutral. From the results, the proposed MTF achieved outstanding results in F1 Score (80.76%) as well as Mean Absolute Error (0.376) when compared with the existing Tensor Fusion Network (TFN).

关键词： Sentiment analysis Visualization Three-dimensional displays Empirical mode decomposition Tensors Customer services Noise Feature extraction Transformers Mel frequency cepstral coefficient

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A Computational Intelligence Inspired Framework for Intrusion Detection in WSN

A Computational Intelligence Inspired Framework for Intrusio...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Pandey, Vivek Kumar Prakash, Shiv Gupta, Tarun Kumar Yang, Tiansheng Singh, Abhishek Rathore, Rajkumar Singh University of Allahabad Department of Electronics and Communication Prayagraj India University of Delhi Department of Computer Science Miranda House Delhi India University of South Wales Faculty of Business and Creative Industries Pontypridd United Kingdom Institute of Engineering & Technology Department of Computer Science & Engineering Dr. A.P. J. Abdul Kalam Technical University Lucknow India Cardiff School of Technologies Cardiff Metropolitan University Cardiff United Kingdom

ISBN: (纸本)9798350369106

In this growing world of technology, the wide range of uses of wireless sensor networks (WSNs), such as tracking the environment and keeping an eye on the military, makes them indispensable in many applications. However, due to their open nature and limited resources, WSNs have become an easy target for different security threats, like intrusion attempts. Intrusion detection systems (IDSs) protect WSNs from these threats. This research examines the intrusion detection methods for WSNs and focuses on those that use computational intelligence. This review explores the usefulness of different computational intelligence methods in detecting intrusions in WSNs. It also suggests areas where more research is needed and offers possible directions for further exploration. © 2024 IEEE.

关键词： Network intrusion

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Brain Imaging and GRU-CNN Hybrid Framework for Alzheimer's Disease Progression Analysis

Brain Imaging and GRU-CNN Hybrid Framework for Alzheimer's D...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： L. Selvam R. Dhamotharan R. Santhana Krishnan J. Relin Francis Raj G. Ram Sankar G. Yamini Department of Computer Science and Engineering SRM Madurai College for Engineering and Technology Pottapalayam Sivaganga India Department of Computer Science and Information Technology Kalasalingam Academy of Research and Education Krishnankoil India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India Department of English Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Alzheimer's disease detection and classification using deep learning techniques offer significant advancements in early diagnosis and progression analysis of this neurodegenerative disorder. This approach leverages various medical imaging modalities such as MRI, PET, CT, and fMRI, alongside clinical data like cognitive test results and biomarkers, to assess brain changes associated with Alzheimer's. The focus is on classifying the disease into three stages: Mild, Moderate, and Severe, using advanced machine learning models. Deep learning techniques, such as EfficientNet B7 for 2D imaging and 3D CNNs for volumetric data, enable efficient feature extraction from medical scans. Additionally, Recurrent Neural Networks (RNNs) with GRU layers are used to capture temporal patterns from longitudinal data, improving predictive accuracy. Hybrid models, combining CNNs with attention mechanisms, further enhance classification. The model uses a multi-class classification approach with Softmax activation to predict the likelihood of each stage of Alzheimer's disease. Performance evaluation includes key metrics such as accuracy, precision, recall, F1 score, and AUC (Area Under the Curve). AUC can be calculated in two ways: individual AUC scores for each disease stage or a combined AUC for overall classification. This analysis provides a comprehensive understanding of the model's effectiveness in distinguishing between different stages of Alzheimer's. The results can be integrated into clinical workflows to assist healthcare professionals in diagnosing and monitoring Alzheimer's disease progression, offering a potential path toward more efficient and early intervention strategies.

关键词： Deep learning Support vector machines Solid modeling Analytical models Three-dimensional displays Accuracy Attention mechanisms Brain modeling Data models Alzheimer's disease

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Outage Analysis of Multiple IRS-aided Cooperative NOMA System

Outage Analysis of Multiple IRS-aided Cooperative NOMA Syste...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Arthi Murugadass Vinoth Babu Kumaravelu Helen Sheeba John Kennedy B S Anjana R Dipinkrishnan Mehmet Bilim School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Communication Engineering School of Electronics Engineering Vellore Institute of Technology Vellore India Department of Electrical and Electronics Engineering Nuh Naci Yazgan University Kayseri Turkey

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The traditional cooperative non-orthogonal multiple access (CNOMA) technique improves the outage and data rate performance for cell-edge users, albeit at the expense of near users. Due to the ability to control the propagation environment, introducing intelligent reflective surfaces (IRS) into a multi-user network can reduce costs, signal processing complexity, and energy consumption. In this work, a comprehensive downlink framework for a blind-IRS-supported CNOMA system is established to enhance outage performance for both cell-edge and near users. The closed-form analytical expressions for the outage probability of the near and cell-edge users within this proposed system are derived. Furthermore, these analytical expressions are validated through Monte-Carlo simulations. The cell-edge user of our proposed model outperforms traditional NOMA and IRS-assisted NOMA variants. To achieve a target outage probability of $10^{-3}$, our proposed system requires $\sim 4$ dB lower signal-to-noise ratio (SNR) compared to other IRS-assisted NOMA variants.

关键词： NOMA Monte Carlo methods Focusing Probability Signal processing Power system reliability Resource management Surface treatment Signal to noise ratio Testing

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The Techniques used for Sustainable communication System FR 6-G type of N/W

The Techniques used for Sustainable Communication System FR ...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Renu Yadav M.Mohammed Kasim S Srividhya Yogendra Kumar T. Mohanraj S P V Subba Rao Department of Electronics & Communication Engineering IES College Of Technology Bhopal M. P. India Department of Electronics and Communication Engineering Nehru Institute of Technology T.M.Palayam Tamil Nadu India Electronics and Communication Engineering Department Prince Shri Venkateshwara Padmavathy Engineering College Electrical Engineering department GLA University Mathura Department of Computer Science and Engineering Karpagam Academy of Higher Education TN Department of Electronics and Communication Engg Sreenidhi Institute of Science and Technology Hyderabad

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Inthe midst of the global commercialization of 5G technology, research institutions worldwide are turning their attention to the future, anticipating the emergence of 6 G networks that prioritize both high-quality service and energy efficiency. The envisioned 6G landscape foresees a revolutionary integration of traditional terrestrial mobile networks with emerging space, aerial, and underwater networks, ushering in a new era of ubiquitous, anytime-anywhere network access. This paper conducts an exhaustive survey on the wireless evolution towards 6 G networks, focusing on fundamental architectural changes. Key aspects include pervasive 3D coverage, the infusion of artificial intelligence across the network, and an enhanced protocol stack. Additionally, the survey delves into prospective technologies crucial for establishing sustainable and socially seamless networks, encompassing terahertz and visible light communication, novel communication paradigms, blockchain applications, and symbiotic radio technologies. The ultimate objective of this work is to offer illuminating insights that can guide subsequent research endeavors in the development of environmentally conscious 6 G networks.

关键词： Surveys 6G mobile communication Protocols Three-dimensional displays Wireless networks Quality of service Turning

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Swarm-Based LSTM for Temperature Forecasting Using Big Data Analytics

Swarm-Based LSTM for Temperature Forecasting Using Big Data ...

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Smart Technologies for Sustainable Development Goals (ICSTSDG), International Conference on

作者： Mathan Kumar Mounagurusamy A. Pandiaraj N. Ramshankar Naveen Gandhi Anbullam Dhivya V Jeeva K Department of Computing Technologies SRM Institute of Science and Technology Department of Computing Technologies SRM Institute of Science and Technology Chennai Department of Computer Science and Engineering R.M. D. Engineering College Department of Electronics and Communication Madha Institute of Engineering and Technology Department of Computer Science and Business Systems S.A. Engineering College Chennai

ISBN: (数字)9798331516284

ISBN: (纸本)9798331516291

Over the previous few periods, global warming, driven by contamination, the emission of conservatory gases, and the use of non-environmentally friendly dynamism sources, has become a pressing issue. This phenomenon is causing a steady increase in the Earth's overall temperature, which is having profound and detrimental effects on both human populations and ecosystems. Millions of people are at risk of losing their livelihoods due to these changes. One significant consequence of global warming is the escalation of high temperatures during the summer months, resulting in heat-related illnesses and even premature fatalities. To mitigate these risks and better understand climate patterns, researchers have turned to machine learning techniques. Specifically, they have employed algorithms like autoregressive combined poignant averages, collaborative learning, and extensive short-term remembrance networks to forecast temperature trends. This article introduces a novel approach known as CPSO to optimize the hyper parameters within Long Short-Term Remembrance networks. These hyper parameters are fine-tuned by minimizing the mean squared error during the validation of LSTM models. The resulting optimized LSTM hyper parameters are then used to predict temperatures in Chennai, a city heavily impacted by rising temperatures. The CPSO-LSTM model's effectiveness is assessed using a publicly available 25-year dataset of temperatures in Chennai. The evaluation is conducted using MATLABR2020a, with the mean squared error and mean absolute error serving as key performance metrics for the temperature predictions. Significantly, the CPSO-LSTM model outperforms traditional LSTM algorithms in several aspects. Notably, it significantly reduces training time to just 25 minutes while using 200 training epochs and 150 hidden neurons. Moreover, it achieves a lower RMSE of 0.250, as opposed to the outdated LSTM's RMSE of 0.35. This improvement in accuracy is promising, as it can have a positive

关键词： Training Heating systems Temperature distribution Accuracy Biological system modeling Computational modeling Neurons Global warming Long short term memory Meteorology

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Organic-inorganic hybrid piezotronic bipolar junction transistor for pressure sensing

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Microsystems & Nanoengineering 2024年第3期10卷 229-244页

作者： Emad Iranmanesh Zihao Liang Weiwei Li Congwei Liao Shunyu Jin Chuan Liu Kai Wang Shengdong Zhang Charalampos Doumanidis Gehan A.J.Amaratunga Hang Zhou Guangdong Provincial Key Laboratory of In-Memory Computing Chips School of Electronic and Computer EngineeringPeking University Shenzhen Graduate SchoolShenzhen 518055P.R.China School of Mechanical Engineering Guangdong Technion-Israel Institute of TechnologyShantou 515063P.R.China State Key Laboratory of Microelectronics Device and Integrated Technology Institute of MicroelectronicsChinese Academy of SciencesBeijing 100029P.R.China Hefei National Research Center for Physical Sciences at the Microscale University of Science and Technology of ChinaHefei 23000PR China School of Electronics and Information Technology Sun Yat-sen UniversityNo.132 East Waihuan RoadGuangzhou 510006P.R.China Department of Mechanical Aerospaceand Biomedical EngineeringUniversity of South AlabamaShelby Hall3128MobileAL 36688USA Electrical Engineering Division Department of EngineeringUniversity of Cambridge9 JJ Thomson AvenueCambridge CB30FAUnited Kingdom Zhejiang University International CampusHainingChina

With the rapid development of the Internet of Things(IoTs),wearable sensors are playing an increasingly important role in daily monitoring of personal health and *** signal-to-noise-ratio has become the most critical performance factor to *** enhance it,on the one hand,good sensing materials/devices have been employed;on the other hand,signal amplification and noise reduction circuits have been ***,most of these devices and circuits work in an active sampling mode,requiring frequent data acquisition and hence,entailing high-power *** this scenario,a flexible and wearable event-triggered sensor with embedded signal amplification without an external power supply is of great ***,we report a flexible two-terminal piezotronic n-p-n bipolar junction transistor(PBJT)that acts as an autonomous and highly sensitive,current-and/or voltage-mediated pressure *** PBJT is formed by two back-to-back piezotronic diodes which are defined as emitter-base and collectorbase *** force exertion on the emitter side,as a result of the piezoelectric effect,the emitter-base diode is forward biased while the collector-base diode is reverse *** to the inherent BJT amplification effect,the PBJT achieves record-high sensitivities of 139.7 kPa^(-1)(current-based)and 88.66 kPa^(-1)(voltage-based)in sensing *** PBJT also has a fast response time of<110 ms under exertion of dynamic stimuli ranging from a flying butterfly to a gentle finger ***,the PBJT advances the state of the art not only in terms of sensitivity but also in regard to being self-driven and autonomous,making it promising for pressure sensing and other IoT applications.

关键词： bipolar transistor IoT

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A Review on the Advancement of Renewable Natural Fiber Hybrid Composites: Prospects, Challenges, and Industrial Applications

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Journal of Renewable Materials 2024年第7期12卷 1237-1290页

作者： Mohammed Mohammed Jawad K.Oleiwi Aeshah M.Mohammed Anwar Ja’afar Mohamad Jawad Azlin F.Osman Tijjani Adam Bashir O.Betar Subash C.B.Gopinath Center of Excellence Geopolymer&Green Technology(CEGeoGTech) Universiti Malaysia PerlisArau02600Malaysia Faculty of Chemical Engineering Technology Universiti Malaysia Perlis(UniMAP)Arau02600Malaysia Department of Materials Engineering University of TechnologyBaghdad10070Iraq Department of Chemical Materials University of Bagdad College of Education for Pure Science Ibn-AlhaithamBaghdad10001Iraq Department of Computer Techniques Engineering Al-Rafidain University CollegeBaghdad10001Iraq Faculty of Electronics Engineering Technology Universiti Malaysia PerlisKampus Uniciti Alam Sg.ChuchuhPerlis02100Malaysia Research Center(NANOCAT) University of MalayaKuala Lumpur50603Malaysia Institute of Nano Electronic Engineering Universiti Malaysia PerlisPerlis01000Malaysia Center for Global Health Research Saveetha Medical College&HospitalSaveetha Institute of Medical and Technical Sciences(SIMATS)Tamil Nadu602105India

Naturalﬁbre(NFR)reinforced functional polymer composites are quickly becoming an indispensable sustainable material in the transportation industry because of their lightweight,lower cost in manufacture,and adaptability to a wide variety of ***,the major difﬁculties of using theseﬁbres are their existing poor dimensional stability and the extreme *** assessing the mechanical properties(MP)of composites,the interfacial bonding(IB)happening between the NFR and the polymer matrix(PM)plays an incredibly signiﬁcant *** compared to NFR/syntheticﬁbre hybrid composites,hybrid composites(HC)made up of two separate NFR are less prevalent;yet,these hybrid composites also have the potential to be valuable materials in terms of environmental issues.A new dimension to theﬂexibility of composites reinforced with NFR is added by the cost-effective manufacture of hybrid composites utilising *** purpose of this study is to offer an over-view of the keyﬁndings that were presented on hybrid *** emphasis was focused on the factors that inﬂuence the performance of the naturalﬁber composites,diverse approaches to enhancing MP,physical,electri-cal,and thermal characteristics of the *** study in polymer science gains interest for applications in con-struction and automotive industries.

关键词： Renewable naturalﬁber hybrid composites sustainability nanomaterial greener material

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Certain Investigation of Optimization Methods of Sensor Nodes in Biomedical Recording Systems 9

Certain Investigation of Optimization Methods of Sensor Node...

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9th International Conference on Advanced Computing and communication Systems, ICACCS 2023

作者： Ashokkumar, N. Pappa, C. Kanmani Siva Kumar, A. Srinivasan, Dhamodharan Vijay, M.M. Mohan Babu University Department of ECE Tirupati India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of ECE Tamilnadu Chennai India SRM Institute of Science and Technology Department of Data Science and Business Systems Chennai India Sri Eshwar College of Engineering Department of Computer and Communication Engineering Tamilnadu Coimbatore India SCAD College of Engineering and Technology Department of ECE Tirunelveli India

ISBN: (纸本)9798350397376

Biosensors will monitor a patient's physiological signals (ECG, EEG, etc.) and send an alarm as soon as irregularities are discovered. Downsized biomedical sensors are the focus of this article, which explores innovative circuit approaches for miniaturised sensors. In order to demonstrate the proposed biomedical compression engine, ECG compression was applied and assessed. A MIP (mixed-integer programming model) is used to represent and solve the clustering issue based on the power consumption analysis. It follows that an architectural model that may be used to design ECG monitoring systems has been provided, as well as an in-depth study of the ECG monitoring system's value chain and a complete evaluation of relevant literature based on an expert classification. Time synchronisation and signal synthesis are the two stages of the distributed cooperative sensing algorithm's execution of ECG signal gathering. This method uses the mutation probability of each solution to prevent local convergence. © 2023 IEEE.

关键词： Electrocardiograms

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Reprogramming Pretrained Target-Specific Diffusion Models for Dual-Target Drug Design 38

Reprogramming Pretrained Target-Specific Diffusion Models fo...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhou, Xiangxin Guan, Jiaqi Zhang, Yijia Peng, Xingang Wang, Liang Ma, Jianzhu School of Artificial Intelligence University of Chinese Academy of Sciences China China Department of Computer Science University of Illinois Urbana-Champaign United States Department of Electronic Engineering Tsinghua University China Institute for Artificial Intelligence Peking University China Institute for AI Industry Research Tsinghua University China

Dual-target therapeutic strategies have become a compelling approach and attracted significant attention due to various benefits, such as their potential in overcoming drug resistance in cancer therapy. Considering the tremendous success that deep generative models have achieved in structure-based drug design in recent years, we formulate dual-target drug design as a generative task and curate a novel dataset of potential target pairs based on synergistic drug combinations. We propose to design dual-target drugs with diffusion models that are trained on single-target protein-ligand complex pairs. Specifically, we align two pockets in 3D space with protein-ligand binding priors and build two complex graphs with shared ligand nodes for SE(3)-equivariant composed message passing, based on which we derive a composed drift in both 3D and categorical probability space in the generative process. Our algorithm can well transfer the knowledge gained in single-target pretraining to dual-target scenarios in a zero-shot manner. We also repurpose linker design methods as strong baselines for this task. Extensive experiments demonstrate the effectiveness of our method compared with various baselines. © 2024 Neural information processing systems foundation. All rights reserved.

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