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IEEE International Conference on Communications Workshops, ICC

作者： Ding Yao Zheng Chang Geyong Min School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Information Technology University of Jyvaskyla Jyvaskyla Finland Department of Computer Science University of Exeter Exeter U.K.

To satisfy the explosive growing traffic demands for high-speed service, visible light communication (VLC) has become a promising technology these years. This paper presents a novel intelligent reflective surface (IRS)-aided VLC network system, which takes into account the existence of both information users (IUs) for receiving data information and energy harvesting users (EHUs) for capturing light energy. We determine the IRS association, power allocation, as well as direct current (DC) offset distribution to maximize the sum rate at the IUs, subject to the energy constraints at EHUs. As a solution to such a complex non-convex optimization problem, we propose a two-stage alternating optimization algorithm by decomposing it into two subproblems, with each subproblem being solved iteratively. We solve the subproblem of IRS association with a heuristic method and tackle another subproblem of power allocation and DC offset distribution with a successive convex approximation (SCA)-based algorithm. Based on our numerical results, we conclude that the proposed algorithm is convergent, and a significant improvement in sum rate can be achieved by joint resource management while meeting certain requirement of harvesting energy. Moreover, IRS can reduce the sensitivity to obstacles and improve sum rate performance in VLC.

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Estimation of Bandwidth Requirement for Underwater Fiber Optic Networks

Estimation of Bandwidth Requirement for Underwater Fiber Opt...

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Paradigm Shift in information Technologies with Innovative Applications in Global Scenario (ICPSITIAGS), IEEE International Conference on

作者： Sanjay Choudhary Girija Shankar Sahoo A Rajesh Jatin Khurana S. Yuvaraj Chaitali R. Shewale Department of Mechanical Engineering Vivekananda Global University Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science Engineering Faculty of Engineering and Technology Bangalore Karnataka India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Information Technology Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798350329773

ISBN: (纸本)9798350329780

This paper affords an estimation of the bandwidth requirement for underwater fiber optic networks by thinking about transmission c together with signal attenuation, dispersion, and nonlinear effects (NLEs). The authors present an evaluation of the signal power level and the propagation of the signal in an underwater environment. From this evaluation, they obtain empirical expressions for the desired signal-to-noise ratio (SNR) to achieve a given bit-errors charge (BER) for different sorts of fiber optics. The impact of NLEs at the sign excellent is also investigated by means of modeling these effects with polynomial expressions under high electricity excitation of the optical fiber. The authors suggest numerous methods for estimating the bit fee required for a given data rate and BER, inclusive of the p-endurance method, which is based totally on an assessment of the energy of the received optical sign. The results acquired from this observation suggest that NLEs can extensively affect the overall performance of optical communications, leading to extra design concerns and extended machine requirements for underwater fiber optic networks.

关键词： Optical fibers Underwater cables Optical fiber cables Estimation Bandwidth Optical fiber networks Polynomials

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NextGen Robot: An Efficient and Effortless Wall Painting Robot Design Based on Artificial Intelligence (AI) Principles

NextGen Robot: An Efficient and Effortless Wall Painting Rob...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： S Jeya Mary Sajithra S G Divya Lakshmi. N Pallavi Giri K. Al-Qawasmi Department of ECE Einstein College of Engineering Tirunelveli Department of Computer Science and Engineering R.M.K. Engineering College Chennai Department of Networking and Communications Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur TamilNadu India Department of Master of Computer Applications S.A. Engineering College Chennai Department of Civil engineering Laxminarayan Innovation Technological University Nagpur India Department of Cyber Security Faculty of Information Technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

A fully autonomous wall-painting robot is the focus of this paper's development, expansion, and implementation efforts. Research efforts have not communicated much about interior wall painting. Painters’ eyes and respiratory systems are particularly vulnerable to the toxic chemicals used in the industry. Additionally, the monotony, time commitment, and physical exertion inherent in the painting process are hard to resist. The entire construction process may be enhanced and time and labor saved by using robots and human workers appropriately into building jobs. The Dynamic Smart Painting Robo Model (DSPRM) is a novel robot that this study introduces for intense wall painting. To test its efficacy, it is crossvalidated with the usual technique, the usual Painting Bot (CPB). Furthermore, it would provide an opportunity to lessen or eliminate human exposure to challenging and dangerous conditions, which would address most of the issues, provide protection, and make it happen more often. An autonomous robotic painting system is being developed as a result of the wall painting robot.

关键词： Service robots Buildings Toxic chemicals Resists Chatbots Artificial intelligence Respiratory system Protection System analysis and design Painting

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Expression of Concern for: Harnessing Machine Learning for Increased Efficiency in Real-Time Data Analysis

Expression of Concern for: Harnessing Machine Learning for I...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Dhiraj Kumar Singh Umamaheswari. B Aparna A. Junnarkar Vaishali Singh Manpreet Singh Savitha R Department of Electrical Engineering Vivekananda Global University Jaipur India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Information Technology Vishwakarma Institute of Information Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Electrical and Electronics Engineering Faculty of Engineering and Technology Bangalore Kanataka India

ISBN: (数字)9798350319125

ISBN: (纸本)9798350319132

The goal of this research is to analyze the ability of device mastering (ML) to increase the efficiency of real-time records evaluation. Notably, the focus is on leveraging ML techniques to correctly pick out, examine, and categorize information streams as a way to offer more green selection-making possibilities. This study will explore present ML strategies, which include supervised and unsupervised learning, deep studying, and reinforcement learning, and their software for real-time statistics evaluation. Additionally, the studies will remember the feasibility of combining ML strategies with present information science strategies and technology, including streaming data infrastructure and facts engineering. Sooner or later, the research will compare the potential prices and benefits of enforcing the sort of device in phrases of computational requirements, statistics handling, and accuracy. Via presenting insight into the feasibility of device mastering for actual-time records analysis, this research aims to cope with the issue of scalability in present-day techniques and help tell future selections in statistics engineering and analytics.

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Moisture Content Prediction Model for Pharmaceutical Granules Using Machine Learning Techniques

SSRN

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SSRN 2023年

作者： Tekie, Haftom Kahsay Beshah, Tibebe Kidus, Fisha Haileslassie Ghidey, Kibrom Department of Information Technology College of Engineering and Technology Adigrat University Adigrat Ethiopia School Information Science Addis Ababa University Addis Ababa Ethiopia Department of Computer Science Faculty of Technology Debre Tabor University Debre Tabor Ethiopia

The aim of this study was to develop a prediction model and identifying relative important factors in the evaluation of moisture content of pharmaceutical granules using Artificial Neural Network (ANN) and Support Vector Machine (SVM) techniques for datasets of Addis Pharmaceutical Factory (APF). Optimal models of ANN and SVM models were developed and compared, utilizing Matlab16.0 as a software tool. The performance of the models is evaluated using a quantitative error metric;mean squared error (MSE), Regression(R) and Confusion Matrix (CM). This study reveals that the ANN model is an optimal model for predicting moisture content of pharmaceutical granules for datasets of APF. The model of ANN, with MSE of 0.016941 and classification accuracy of 98.7% is built and accepted as optimal model for predicting moisture content of pharmaceutical granules. Temperature, Screw speed, Initial moisture, Inlet airflow rate and Drying time respectively are the most important factors in determining the moisture content of the granules. © 2023, The Authors. All rights reserved.

关键词： Support vector machines

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Investigation of Dynamic Spectrum Allocation Algorithms for Ultra-High Capacity Optical Networks

Investigation of Dynamic Spectrum Allocation Algorithms for ...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Balakumar P Sachin R. Sakhare Mahendra Kumar Singar Shweta Singh Mamatha G N Amritpal Sidhu Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune INDIA Department of Civil Engineering Vivekananda Global University Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Electronics and Communication Engineering Faculty of Engineering and Technology JAIN Bangalore Karnataka India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India

The rapid evolution of generation has enabled the development of extremely-excessive capability optical networks that can aid multi-Gigabit/s records transmissions among nodes. to utilize this increased capability, green spectrum allocation algorithms need to be developed. This summary will discuss the continued studies efforts to develop dynamic spectrum allocation algorithms for ultra-excessive capacity optical networks. studies is performed the use of algorithmic tactics and numerical simulations to decide the most efficient scheme for useful resource allocation inside the network. The algorithms should make sure green usage of the spectrum whilst also accounting for changes in optical sign strength through the years. furthermore, the take a look at evaluates network overall performance, inclusive of capacity, latency and packet loss rate. those overall performance parameters are then used to assess the effectiveness of the chosen algorithms. The goal of these studies is to identify efficient spectrum allocation algorithms that could maximize the usage of available sources and make sure superior network performance.

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Apache Spark Powered: Enhancing Network Intrusion Detection System Using Random Forest

Apache Spark Powered: Enhancing Network Intrusion Detection ...

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Novel Intelligent and Leading Emerging Sciences Conference (NILES)

作者： Hesham Mamdouh Mohamed Tarek Abdelrahman Radwan Adel Saeed Ahmed Abdeen Monier Ashraf Khaled M. Fouad Ibrahim Abdelbaky School of Engineering and Applied Science (EAS) Nile University Giza Egypt CS program School of Information Technology and Computer Science (ITCS) Nile University Giza Egypt Faculty of Computer Science and Engineering New Mansoura University Artificial Intelligence Department Faculty of Computers and Artificial Intelligence Benha University Banha Egypt

ISBN: (数字)9798350378511

ISBN: (纸本)9798350378528

The increasing sophistication of cyber attacks necessitates effective intrusion detection systems. We propose a novel intrusion detection method integrating deep learning with big data management using Apache Spark. Leveraging the comprehensive CSE-CIC-IDS2018 dataset, we apply extensive data preprocessing, including handling missing and unreliable values, duplicates, and redundant columns. In addition, implementation of a Random Forest based feature importance approach is derived to prioritize the most impactful Features. Furthermore, stratified k-fold cross-validation is used for a model selection process on a class-imbalanced dataset. Our weighted Random Forest classifier achieves a remarkable weighted average F1-score of 0.999 and a test inference time of 0.673 seconds using only the top 34 features, outperforming previous studies without sampling techniques. The proposed architecture offers a scalable and accurate solution for intrusion detection in cloud architectures, demonstrating the effectiveness of combining deep learning and big data technologies for cybersecurity.

关键词： Deep learning Cloud computing Data preprocessing Network intrusion detection Cluster computing Forestry Big Data Random forests Cyberattack Testing

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Harnessing Machine Learning for Increased Efficiency in Real-Time Data Analysis

Harnessing Machine Learning for Increased Efficiency in Real...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

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Exploring genetic mechanisms underlying EEG endophenotypes via summary-data-based Mendelian randomization

Exploring genetic mechanisms underlying EEG endophenotypes v...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Chen, Jing Zhu, Pengfei Pan, Jinghui Wang, Tao Xi'an University of Technology School of Computer Science and Engineering Xi'an China Harbin Institute of Technology Faculty of Computing Harbin China Northwestern Polytechnical University School of Computer Science Xi'an China Wuhan University School of Electronic Information Wuhan China Renmin Hospital of Wuhan University Department of Radiation Oncology Wuhan China

ISBN: (纸本)9781665468190

Oscillations in neuronal activity play a crucial role in neural processing. Electroencephalogram (EEG) records the amplitudes of oscillations at different frequencies. The robust interindividual variation and the high intraindividual stability of EEG oscillations can be explained by genetic variants. However, our knowledge of how genetic variants affect EEG endophenotypes is still limited. In this paper, we aim to identify the linkage between genes and EEG oscillations at different frequencies. We integrate the summary-level datasets from the genome-wide association studies (GWASs) of seven EEG endophenotypes and expression quantitative trait loci (eQTL) study of the human brain, to explore the genetic mechanisms underlying EEG using summary-data-based two-sample Mendelian randomization. As a result, twelve candidate causal genes are prioritized across four EEG endophenotypes. These genes offer opportunities for a deeper understanding of the molecular mechanisms whereby genomic variation leads to variable brain activities. © 2022 IEEE.

关键词： Genes

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Semantic and Emotional Dual Channel for Emotion Recognition in Conversation

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Zhenyu Zhang, Zhibo Cheng, Yuhu Zhang, Tong Wang, Xuesong Jinan250353 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China China University of Mining and Technology Engineering Research Center of Intelligent Control for Underground Space Ministry of Education Xuzhou Key Laboratory of Artificial Intelligence and Big Data School of Information and Control Engineering Xuzhou221116 China South China University of Technology Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Emotion recognition in conversation (ERC) aims at accurately identifying emotional states expressed in conversational content. Existing ERC methods, although relying on semantic understanding, often encounter challenges when confronted with incomplete or misleading semantic information. In addition, when dealing with the interaction between emotional and semantic information, existing methods are often difficult to effectively distinguish the complex relationship between the two, which affects the accuracy of emotion recognition. To address the problems of semantic misdirection and emotional cross-talk encountered by traditional models when confronted with complex conversational data, we propose a semantic and emotional dual channel (SEDC) strategy for emotion recognition in conversations to process emotional and semantic information independently. Under this strategy, emotion information provides an auxiliary recognition function when the semantics are unclear or lacking, enhancing the accuracy of the model. Our model consists of two modules: the emotion processing module accurately captures the emotional features of each utterance through contrastive learning, and then constructs a dialogue emotion propagation map to simulate the emotional information conveyed in the dialogue;the semantic processing module combines an external knowledge base to enhance the semantic expression of the dialogue through knowledge enhancement strategies. This divide-and-conquer approach allows us to more deeply analyze the emotional and semantic dimensions of complex dialogues. Experimental results on the IEMOCAP, EmoryNLP, MELD, and DailyDialog datasets show that our approach significantly outperforms existing techniques and effectively improves the accuracy of dialogue emotion recognition. Our code is available at https://***/r/SEDC-FCF1. © 2010-2012 IEEE.

关键词： Contrastive Learning

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