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Advances in Data Engineering and Intelligent Computing systems (ADICS), International Conference on

作者： M. Pradeepa V. Sumathi Sivakumar Ponnusamy G Suneetha Gurjeet Singh Kruthika P School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamil Nadu India Department of Mathematics Sri Sai Ram Engineering College West Tambaram Chennai-44 Tamil Nadu India Department of Computer Science and Engineering KSR College of Engineering Tiruchengode Namakkal TamilNadu India Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Mechanical Engineering IES College of Technology & IES University Bhopal Madhya Pradesh India Department of Information Technology Sona College of Technology Salem Tamil Nadu India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

This study uses machine learning models as potent analytical tools to look into the issue of inventory cost and profit prediction in the automobile industry. Throughout a ten-year dataset from 2012 to 2023, the study carefully analyses several cost components and profit data, illuminating the complex relationships between these elements. To provide insights into inventory dynamics, the Convolutional Neural Network (CNN), k-nearest Neighbours (KNN), and Support Vector Machine (SVM) models are rigorously trained and tested. An impressive feat is achieved by the CNN model, which achieves a phenomenal accuracy of 98.7 % , followed by KNN at 96.7 % and SVM at 93.4%. Additional proof of the models' propensity to make accurate predictions comes from their precision, recall, F1 score, and confusion matrices. The research provides data-driven insights that players in the dynamic vehicle industry can use to improve inventory management procedures and financial results. This work serves as an example of how machine learning may fundamentally alter how people make decisions in this difficult domain.

关键词： Support vector machines Industries Costs Profitability Machine learning Predictive models Inventory management

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A Review of 5G Technology: Architecture and Challenges 8

A Review of 5G Technology: Architecture and Challenges

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8th IEEE International Conference on Computing, Engineering and Design, ICCED 2022

作者： Attarbashi, Zainab S. Altaleb, Abdulazeez E. Mohd Aman, Azana Hafizah Hassan Abdalla Hashim, Aisha Eker, Sebahattin International Islamic University Malaysia Information Communication Technology Kuala Lumpur Malaysia Informatics Institute Istanbul Technical University Communication Systems Department Istanbul Turkey University Kebangsaan Malaysia Faculty of Information Science Technology Bangi Malaysia International Islamic University Malaysia Computer Information Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9781665453899

5G Networks are considered as a new evolution since it aims to increase the wireless capacity 1000 times the previous generations. By 2025, it is expected that 5G will be connecting around 3 billion people and 41.6 billion devices enabling the Internet of Things (IoT) and the Internet of Everything (IoE) while increasing energy efficiency 90% more than 4G and reducing 20% of carbon emissions. In this paper, the deployment requirements and challenges of 5G Networks are reviewed. Starting with the overall network architecture and network slicing framework including the service-type slicing and the infrastructure slicing are investigated. Later, the spectrum and multiple access techniques are explained. Finally, the challenges and the available solutions have been discussed. © 2022 IEEE.

关键词： 5G mobile communication systems

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Cloud Telescope: A distributed architecture for capturing Internet Background Radiation

Cloud Telescope: A distributed architecture for capturing In...

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IEEE International Conference on Cloud Networking (CLOUDNET)

作者： Fabricio Bortoluzzi Barry Irwin Lucas Silvero Beiler Carla Merkle Westphall Cyber Security Programme Noroff University College Kristiansand Norway Postgraduate Programme in Computer Science University of Santa Catarina Florianópolis Brazil Department of Computer Science Rhodes University Grahamstown South Africa Laboratory of Embedded & Distributed Systems University of Vale do Itajai Itajai Brazil

Internet Background Radiation (IBR) comprises a range of unsolicited traffic directed towards Internet hosts. In general, this type of traffic is characterised by high levels of port scanning activity, malware propagation, application exploits, system misconfiguration and denial-of-service attacks. IBR capture is typically undertaken by a system termed a network telescope. This records unfiltered incoming internet traffic for a specific CIDR block in the form of a packet capture (PCAP) file for analysis. This work proposes a novel, cloud-native approach to capturing IBR by the deployment of an ephemeral and reproducible architecture, described as code, distributed across all regions of a cloud service provider. In this paper we discuss the technical and financial viability of using a fleet of small-sized compute instances, in a spot price auction model, to maximise platform collection, capillarity and duration. We also present an overview analysis of the primary characteristics of IBR as collected during a month long proof-of-concept experiment across 26 regions of a cloud service provider in May 2023. Our analysis discusses the aspects of the dataset in quantitative terms: traffic aggregation per protocol, top TCP and UDP ports, top radiation sources and radiation distribution per cloud region. We also provide an overview of the most relevant threats detected. Our results include a formalisation and validation of the cloud telescope, with the corresponding supporting architecture described in Terraform and Ansible. The aggregate dataset amounted to 2.2 GB, and 21.8 million packets. Composition by protocol was 78% TCP, 14% ICMP and 8% UDP.

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Smart Monitoring System for Small-scale Wind Turbine Model

Smart Monitoring System for Small-scale Wind Turbine Model

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2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2022

作者： Sudharsan, G. Srinivasa Vishnupriyan, J. Anand, K. Vijay Chidambarathanu, K. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Chennai Institute of Technology Center for Energy Research Chennai India R.M.K. Engineering College Department of Electronics and Instrumentation Engineering Chennai India R.M.K. Engineering College Department of Computer Science and Business Systems Chennai India

ISBN: (纸本)9781665483162

The most pressing challenges in the development of fossil fuel energy resources are their high value and scarcity. Therefore, renewable energy achieved through the mismanagement of natural resources is one of the most important aspects defining the energy crisis. Solar, wind, geothermal and recurring event energy are examples of assets. The cost of wind turbines is much higher compared to diesel generators. Moreover, regular maintenance costs often account for up to one-twentieth of the total cost of a rotary engine. However, accidental failure of certain elements such as blades, gearboxes, supports, brake systems can lead to high costs. Identifying parts of a system with human resources or intelligent systems helps to stop and reduce serious failures. Therefore, a completely different condition monitoring system has been applied to wind turbines, providing a great opportunity to develop a large space for analysis in the fields of structural turbine condition monitoring and field condition monitoring systems. Assessing the health of critical system components can help you become more aware of failed components and reduce maintenance time and costs. By using a completely different kind of sensor for the rotary engine components and using the right control and data acquisition systems, a complex condition monitoring system is provided. © 2022 IEEE.

关键词： Wind turbines

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RAFA-Net: Region Attention Network For Food Items And Agricultural Stress Recognition

arXiv

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arXiv 2024年

作者： Bera, Asish Krejcar, Ondrej Bhattacharjee, Debotosh Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani Pilani Campus Rajasthan333031 India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic Skoda Auto University Na Karmeli 1457 Mlada Boleslav293 01 Czech Republic Universiti Teknologi Malaysia Kuala Lumpur Malaysia Department of Computer Science and Engineering Jadavpur University Kolkata32 India

Deep Convolutional Neural Networks (CNNs) have facilitated remarkable success in recognizing various food items and agricultural stress. A decent performance boost has been witnessed in solving the agro-food challenges by mining and analyzing of region-based partial feature descriptors. Also, computationally expensive ensemble learning schemes fusing multiple CNNs have been studied in earlier works. This work proposes a region attention scheme for modeling long-range dependencies by building a correlation among different regions within an input image. The attention method enhances feature representation by learning the usefulness of context information from complementary regions. Spatial pyramidal pooling and average pooling pair aggregate partial descriptors into a holistic representation. Both pooling methods establish spatial and channel-wise relationships without incurring extra parameters. A context gating scheme is applied to refine the descriptiveness of weighted attentional features, which is relevant for classification. The proposed Region Attention network for Food items and Agricultural stress recognition method, dubbed RAFA-Net, has been experimented on three public food datasets, and has achieved state-of-the-art performances with distinct margins. The highest top-1 accuracies of RAFA-Net are 91.69%, 91.56%, and 96.97% on the UECFood-100, UECFood-256, and MAFood-121 datasets, respectively. In addition, better accuracies have been achieved on two benchmark agricultural stress datasets. The best top-1 accuracies on the Insect Pest (IP-102) and PlantDoc-27 plant disease datasets are 92.36%, and 85.54%, respectively;implying RAFA-Net’s generalization capability. © 2024, CC BY.

关键词： Plant diseases

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Impact of Gender and Technological Advancement on Employee Productivity in the Post-Pandemic Era

Impact of Gender and Technological Advancement on Employee P...

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

作者： Sarath Kumar. C Krithika. M C. Srinivasan Sunantha Sri B Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Department of Management Studies Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Computer Science and Business Systems S.A. Engineering College (Autonomous) Chennai Tamil Nadu India

ISBN: (数字)9798331516284

ISBN: (纸本)9798331516291

The primary aim of this research is to investigate the impact of technological advancements and gender on employee productivity in the post-pandemic era. This research investigates the relationship between gender inequalities and the implementation of advanced technology in professional settings by analyzing a sample of 294 individuals. Furthermore, the study investigates the impact of various factors, including internet literacy, acceptance of remote work, and perceived advantages of technology, on employee productivity. Moreover, the research investigates the psychological legitimacy of remote work, considering gender-specific psychological differences that could impact productivity results. The research findings will offer significant insights into the influence of gender dynamics and technology utilization on productivity levels in modern work settings. These insights hold significant importance for both employers and employees as they navigate the changing work environment following the pandemic. They assist in the formulation of strategies aimed at maximizing productivity and cultivating a favourable work atmosphere for individuals of all genders.

关键词： Productivity Pandemics Navigation Psychology Atmosphere Remote working Sustainable development Analysis of variance

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IoT Applications in Smart Building Energy Management

IoT Applications in Smart Building Energy Management

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

作者： Thangjam Ravichandra Madhavi Karumudi M. Uma Maheswari S. Leela Thamizhkani. B S.P. Kanniyappan Alliance School of Business Alliance University Bengaluru Karnataka India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Bowrampet Hyderabad Telangana India Department of Mechatronics Rajalakshmi Engineering College Thandalam Chennai Tamil Nadu India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai Tamil Nadu India Department of Civil Engineering R.M.K. Engineering College Kavaraipettai Tamil Nadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The technologies that are a part of the Internet of Things enable smart buildings to use energy more efficiently. The main focus of the research is on the use of internet-connected sensors and devices. Real-time monitoring, analysis, and management of energy consumption are the goals of this. Data is gathered from various sources, such as appliances, lighting, HVAC (heating, ventilation, and air conditioning), and other systems, employing networked systems. This offers the chance for a more effective distribution of energy. The goal of the project is to foresee energy patterns and automatically adjust settings by focussing on the integration of cloud computing, big data analytics, and machine learning algorithms. Furthermore, it highlights how the Internet of Things can help cut down on energy waste and improve sustainability by giving consumers advice and information on how to use less energy. Providing consumers with insights and recommendations helps achieve this. By promoting the adoption of more ecologically friendly energy practices, the implementation of these technologies not only reduces operating costs but also decreases the environmental impact that buildings have. The study's conclusions indicate that the field of smart buildings has advanced significantly, which may help to promote sustainability and higher energy efficiency.

关键词： Heating systems Smart buildings Machine learning algorithms Lighting Ventilation Energy efficiency Real-time systems Internet of Things Sustainable development Intelligent sensors

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A Novel Duo-Stage driven Deep Neural Network Approach for Mitigating Electrode Shift Impact on Myoelectric Pattern Recognition systems

A Novel Duo-Stage driven Deep Neural Network Approach for Mi...

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2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023

作者： Kulwa, Frank Samuel, Oluwarotimi Williams Asogbon, Mojisola Grace Oyemakinde, Tolulope Tofunmi Obe, Olumide Olayinka Li, Guanglin Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems SIAT-CAS China University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Guangdong Shenzhen518055 China University of Derby School of Computing and Engineering DerbyDE22 3AW United Kingdom Federal University of Technology Department of Computer Science Akure Nigeria

ISBN: (纸本)9781665493840

A major barrier to the commercialization of pattern recognition (PR)-based myoelectric prostheses is the lack of robustness to confounding factors such as electrode shift which has been lingering for years. To overcome this challenge, a novel Duo-Stage Convolutional Neural Network (DS-CNN) is proposed. The DS-CNN is comprised of two cascaded stages in which the first stage deciphers the occurrence of a particular kind of shift upon which a requisite CNN model is triggered in the second stage for accurate decoding of individual motion intent, which is necessary for initiating robust control of the prostheses. The proposed scheme works on raw EMG signals as input which reduces the preprocessing time that would be required in conventional machine learning-based PR schemes, to effectively mitigate both transverse and longitudinal shifts using the same network architecture. This approach was validated for four distinct electrode shift conditions (with shifts in the range of 7.50mm-10.05mm) in a dataset obtained from 18 able-bodied subjects that performed 8 classes of targeted hand gestures. The experimental results show that the proposed dual-stage driven deep neural network model can adequately resolve the effects of electrode shift with classification accuracy near the No-shift scenario ( © 2023 IEEE.

关键词： Electrodes

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Radar-Assisted Predictive Beamforming for UAV-Aided Networks: A Deep-Learning Solution

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IEEE Transactions on Vehicular Technology 2025年

作者： Yin, Rui Peng, Jingwei Cai, Yunlong Wu, Celimuge Champagne, Benoit Al-Dhahir, Naofal Hangzhou City University Laboratory for Microwave Spatial Intelligence and Cloud Platform Hangzhou310015 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China University of Electro-Communications Graduate School of Informatics & Engineering Japan McGill University Department of Electrical and Computer Engineering MontrealQCH3A 0E9 Canada The University of Texas at Dallas Department of Electrical and Computer Engineering RichardsonTX75080 United States

In the realm of Sixth-Generation (6G) mobile communication systems, unmanned aerial vehicle (UAV) technologies have gained significant attention, particularly within UAV-assisted wireless networks. This paper explores radar-assisted predictive beamforming to enhance communication in such networks. A mobile UAV equipped with multiple antennas and radar functionality is deployed to serve a group of mobile users on the ground. The primary objective is to optimize communication performance, ensuring stable and efficient data transmission rates within a specified time frame, under practical power and flight range constraints. To address this challenging problem, we introduce a novel solution framework that integrates a deep reinforcement learning (DRL) network and a dual-layer deepunfolding network (DUN). By leveraging a two-timescale processing structure, the new framework enables and facilitates the joint optimization of mobile user tracking, transmit beamforming and UAV trajectory. Simulation results confirm the effectiveness of our proposed scheme, demonstrating improvements in movement tracking accuracy and transmission rates. © 1967-2012 IEEE.

关键词： Deep reinforcement learning

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Federated Learning Methods for Privacy-Preserving Collaborative Machine Learning

Federated Learning Methods for Privacy-Preserving Collaborat...

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IEEE UP Section Conference on Electrical computer and Electronics (UPCON)

作者： Y. Nagender S. Deena Shaik Mohammed Imran Navdeep Singh R Anuradha Dhiraj Kapila School of CS & AI SR University Warangal Telangana India Department of Computer Science and Business Systems RMD Engineering College Kavarapettai CSE AI& ML Mal1a Reddy Engineering College for Women Lovely Professional University Phagwara Institute of Aeronautical Engineering Dundigal Hyderabad Department of Computer Science & Engineering Lovely Professional University Phagwara

Federated learning is a cutting-edge data solution that protects privacy as well as a fresh machine learning model developed on collaborative data sets. A major concern privacy protection aspect of machine learning has proven quite successful. The PFMLP framework, which is based on federated learning as well as collaborative machine learning, is presented in this paper for multi-party safeguarding privacy machine learning. All learning partners ought to send only homomorphic encrypted gradients, according to the basic idea. According to experiments, the PFMLP-trained model almost always achieves the same accuracy, with a variance of less than 1.%. The upcoming privacy-preserving data technology and a fresh category of distributed learning models, federated learning, were both invented by Google. In this research, we examine distributed ML applied to dispersed data sets and federated learning as an option for privacy-preserving data access. Additionally, a federated learning architecture that protects privacy is presented.

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