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RETRACTED ARTICLE: ALBAE feature extraction based lung pneumonia and cancer classification

Soft Computing 2023年第2期28卷 589-589页

作者： M Braveen S Nachiyappan R Seetha K Anusha A Ahilan A Prasanth A Jeyam Assistant professor senior School of Computer Science and Engineering Vellore institute of technology Chennai Tamil Nadu India Associate Professor School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu India Associate Professor School of Information Technology and Engineering Vellore Institute of Technology Vellore Tamil Nadu India Associate Professor Department of Electronics and Communication Engineering PSN College of Engineering and Technology Tirunelveli Tamil Nadu India Assistant Professor Department of Electronics and Communication Engineering Sri Venkateswara College of Engineering Sriperumbudur India Assistant Professor Computer Science and Engineering Lord Jegannath College of Engineering and Technology Kanyakumari Tamil Nadu 629402 India

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Machine learning and role of artificial intelligence in optimizing work performance and employee behavior

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Materials Today: Proceedings 2022年 51卷 2327-2331页

作者： K.K. Ramachandran A. Apsara Saleth Mary Shibani Hawladar D. Asokk Bandi Bhaskar J.R. Pitroda Professor Management Dr G R D College of Science India Faculty Centre for Tourism and Hotel Management Madurai Kamaraj University Tamil Nadu India Research Scholar BS Abdur Rahman Crescent Institute of Science and Technology Chennai India Faculty BS Abdur Rahman Crescent Institute of Science and Technology Chennai India School of Computer Science and Artificial Intelligence SR University Telangana India Civil Engineering Department BVM Engineering College Gujarat Technological University India

In today’s business world, firms are using analytics to diving deeper into their data to improve productivity, acquire a competitive edge, and boost their bottom lines. That is why businesses are eager to integrate machine learning (ML) and artificial intelligence (AI) because they want a faster, more accurate result. In addition, machine learning and artificial intelligence improved productivity by reducing repetitive tasks. As a result, nearly every sector in the world intends to use artificial intelligence and ML. Artificial intelligence and ML have various roles in business, ranging from enhancing employee and customer interactions to discovering patterns in massive amounts of data to automating monotonous activities. The goal is to seize the opportunity that artificial intelligence provides in employee success and productivity. Implementing artificial intelligence and machine learning may be a vital tool for any company seeking quantitative help in their decision-making since it can analyze vast quantities of data and spit forth trend directions and actionable suggestions. AI helps employees and businesses to set and achieve better goals results. This paper highlights the impact of using artificial intelligence and ML to enhance employee behaviour and work outcome. This research also provides insights into artificial intelligence, which will usher in a new era in industry.

关键词： Artificial intelligence Machine learning Productivity Employee behaviour Automation

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Secuyolo: Secure Object Detection Framework With Darknet-53 and CBAM in Edge Devices

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Concurrency and Computation: Practice and Experience 2025年第15-17期37卷

作者： P. Lakshmiramana A. Vadivel Computer Science & Engineering GITAM School of Technology (Research Scholar) & Madanapalle Institute of Technology & Science (Assistant Professor) GITAM (Deemed to Be University) Dodballapura Karnataka India Madanapalle Institute of Technology & Science Angallu India Artificial Intelligence and Data Science GITAM School of Technology GITAM (Deemed to Be University) Dodballapura Karnataka India

Object detection in edge computing has emerged as a pivotal solution for processing sensor data locally, thereby minimizing latency and bandwidth consumption. However, this introduces significant privacy concerns, as edge servers may exploit sensitive sensor data. Traditional frameworks often overlook data security and privacy protection, presenting challenges in balancing privacy with computational efficiency in resource-constrained environments. To address these issues and improve performance, we propose SecuYOLO CBAMNet53, a novel framework combining Secure YOLO V3 with Darknet-53 and the CBAM. This model enhances feature extraction capabilities, ensuring privacy protection and accurate real-time object detection on edge devices. Darknet-53 serves as a deep feature extractor, while CBAM boosts discriminative power through channel-wise and spatial attention, improving detection accuracy and handling complex scenes. It integrates YOLOv3 with security measures like data encryption, secure deployment, and differential privacy, safeguarding sensitive information during inference. By classifying data as public or private for smart IoT applications, SecuYOLO CBAMNet53 optimizes performance and prevents privacy breaches. The proposed SecuYOLO, employing Darknet-53 with CBAM as its backbone, achieves outstanding performance in both an average recall of 67.62 and an average precision of 51.2. The model's dynamic recalibration of feature maps and robust security mechanisms ensure reliable object detection and privacy protection, making it suitable for real-world applications.

关键词： attention module data privacy deep learning edge computing object detection YOLOv3

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A comprehensive study of space debris & its removal using nanobots

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AIP Conference Proceedings 2023年第1期2728卷

作者： Amrutha Obili Swetha Ramachandran Lalitha Pattabiraman Undergraduate Department of Computer Science SRM Institute of Science and Technology Ramapuram Chennai Tamilnadu India Assistant Professor Department of Mathematics SRM Institute of Science and Technology Ramapuram Chennai Tamilnadu India

The increasing amount of space debris poses a risk to every space-vehicle and it turns out to be very deadly if it is a spacecraft carrying humans. Furthermore, with the burgeoning rate of space junk, there might soon come a point where this debris interferes with the capacity to utilise weather satellites and consequently reduce the efficiency of weather predictions. Hence space debris has become a serious menace. Significant damage to already working satellites is caused due to collisions at orbital velocities which also results in an increase in future space debris. To combat this threat, the International Space Station and certain other spacecrafts have been armoured. However, owing to the addition of armour and mitigation devices, satellites and human spaceflight vehicles like shuttles become exceedingly expensive and heavy. This research paper is a semi-technical overview that discusses space junk’s growing literature and investigates the many origins, methods of measurement, impacts, and mitigation approaches. It also suggests several approaches for dealing with this issue more effectively and efficiently. This paper explains the many approaches for capturing space debris, including the utilisation of a nanobot and nanotube mesh system for recycling and energy generation.

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Designing of certain graphs by applying Unisum labeling

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AIP Conference Proceedings 2023年第1期2728卷

作者： Lalitha Pattabiraman Madhumitha Mohan Assistant Professor Department of Mathematics SRM Institute of Science and Technology Ramapuram Chennai Tamilnadu India Undergraduate Department of Computer Science SRM Institute of Science and Technology Ramapuram Chennai Tamilnadu India

A Graph G is said to be an Unisum Graph if the nodes of G are numbered with distinct integers ranging from one to m+1 inclusive, where m is the number of edges of the graph G and the edges of G are uniquely represented by adding unity to the absolute difference between its corresponding end vertices, without repetition of any labels. In this paper various special graphs are designed and Unisum labeling is applied to these graphs. It is found that these special graphs satisfy the condition of Unisum labeling by carrying unique labels to their nodes and edges.

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Machine learning models for evaluating the benefits of business intelligence systems

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Journal of High technology Management Research 2023年第2期34卷

作者： Tripathi, Mano Ashish Madhavi, Kilaru Kandi, V.S. Prasad Nassa, Vinay Kumar Mallik, Banitamani Chakravarthi, M. Kalyan Department of Humanities and Social Sciences Motilal Nehru National Institute of Technology Allahabad Uttar Pradesh Prayagraj India Department of Business Management Velagapudi Ramakrishna Siddhartha Engineering College Kanuru Andhra Pradesh Vijayawada India Greenfields Andhra Pradesh Vaddeswaram India Department of Computer Science Engineering Rajarambapu Institute of Technology Rajaramnagar Maharashtra Sangli India Department of Mathematics Centurion University of Technology and Management Paralakhemundi Odisha Gajapati India Senior Assistant Professor School of Electronics Engineering VIT-AP University Andhra Pradesh Amaravathi India

Due to the uncertainty of the market and the intensity of rivalry, business owners and managers are often compelled to experiment with a wide variety of strategies for enhancing their company's performance. By enhancing the timeliness and quality of inputs to the decision-making process, Business Intelligence (BI) is one such idea and tool that combines operational data with analytical tools to show complex and competitive information to planners and decision-makers. Business intelligence (BI) tools help companies rapidly generate insights that guide managers toward operational efficiencies, lead them to new opportunities, and set them apart from the competition. The literature study shows that there is a debate about whether BI tools have an effect on the quality of decisions and the development of businesses. The present research explores the varied empirical facets of BI application through ML models. This study concluded with a discussion of how Machine Learning models can be used to assess the value of BI tools. Machine learning models, fed with historical data and a wealth of input features, can foresee the effect of new systems on metrics like revenue development, customer behavior, and inventory management. Using these models, businesses will be able to better evaluate potential investments in new tools and systems. © 2023 Elsevier Inc.

关键词： Decision making

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Human-computer Interaction: Innovations and Challenges in Virtual Reality

Human-Computer Interaction: Innovations and Challenges in Vi...

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International Conference on science technology Engineering and Management (ICONSTEM)

作者： Cuddapah Anitha Prem latha Soundarraj N. Geethanjali C. Boopathy A. Narasima Venkatesh I. Mohana Krishna Department of Computer Science and Engineering School of Computing Mohan Babu University Erstwhile SreeVidyanikethan Engineering College Tirupati School of Business and Management Christ University Pune Lavasa Maharashtra Department of Management Studies PSNA College of Engineering and Technology Dindigul Tamil Nadu School of Management Studies Sri Krishna College of Engineering and Technology Kuniyamuthur Coimbatore Professor and Head of The Department HRM and General Management ISBR Business School Bangalore Karnataka India K L Business School KLEF Vaddeswaram Andhra Pradesh

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

In an effort to shed light on the advances and difficulties that are shaping the area of Virtual Reality (VR), this research paper digs into the ever-evolving world of Human-computer Interaction (HCI) within the context of VR. We have found important insights with theoretical and practical applications via a careful research technique comprising mathematical modelling, data collecting, and empirical analysis. Through our investigation of new technologies, we have shown the revolutionary potential of haptic feedback systems in VR settings. Our results, backed by a solid mathematical model, provide light on the measurable effect of haptic feedback and suggest it has the potential to radically alter user experiences in fields as diverse as gaming, instruction, and treatment. At the same time, we have overcome a number of obstacles inherent to virtual reality human-computer interaction, including motion sickness. Our mathematical model of motion sickness and its treatment lays the groundwork for creating VR experiences that are both enjoyable and safe for a wider range of users. This study highlights the ethical implications of VR HCI, highlighting the importance of responsible development and deployment in addition to advances and problems. To make sure that the advantages of this gamechanging technology are used in a responsible manner, we discuss issues like privacy, informed consent, and the possibility for addiction in VR. Our results, as we reach the end of our trip, are both a celebration of potential and a guide for where VR HCI is headed. They motivate more research into inclusive and human-centered design, personalized motion sickness prevention, and cutting-edge haptic feedback technologies. To further lead the development of VR HCI, we advocate for continuous multidisciplinary cooperation and the adoption of thorough ethical rules and regulations.

关键词： Human computer interaction Ethics Technological innovation Solid modeling Virtual reality Motion sickness Solids

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Investigating Brain Tumor Segmentation and Detection Techniques

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Procedia computer science 2020年 167卷 121-130页

作者： Mansi Lather Parvinder Singh Research Scholar Department of Computer Science and Engineering Deenbandhu Chhotu Ram University of Science and Technology Murthal Sonipat 131039 India Professor and Dean Department of Computer Science and Engineering Deenbandhu Chhotu Ram University of Science and Technology Murthal Sonipat 131039 India

Brain tumor is a life-threatening problem and hampers the normal functioning of the human body. For proper diagnosis and efficient treatment planning, it is necessary to detect the brain tumor in early stages. Digital image processing plays a vital role in analysis of medical images. Segmentation of brain tumor involves separation of abnormal brain tissues from normal tissues of brain. In the past, various researchers have proposed the semi and fully automatic methods for detection and segmentation of brain tumor. In this article, the different techniques available for segmentation have been presented. This article focuses on the work done by many researchers in the past to partially or fully automate the job of segmenting the brain tumor. The consolidated details of the reviewed literature have been tabulated. Simplicity and degree of human supervision decides the clinical acceptance of a particular segmentation technique.

关键词： Brain Brain Tumor Classification Digital Image Processing Medical Image Analysis Segmentation

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Multi-class Cancer Classification in Microarray Datasets Using MI-Based Feature Selection and Artificial Neural Network 1

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4th International Conference on Nanoelectronics, Circuits and Communication Systems, NCCS 2018

作者： Jansi Rani, M. Devaraj, D. Assistant Professor Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil Virudhunagar India Dean School of Electronics & Electrical Technology Kalasalingam Academy of Research and Education Krishnankoil Virudhunagar India

ISBN: (数字)9789811528545

ISBN: (纸本)9789811528538

Recent developments in microarray data analysis and improvements in soft computing techniques have played a major role in the field of cancer classification. Classification and identification of cancer are very important as early cancer identification is highly useful for its treatment. Cancer diagnosis is challenging as microarray data has large number of genes. Many gene selection algorithms have been applied to effectively select the most promising gene subsets that can accurately identify cancer samples. In this paper, Mutual Information is used to select the informative genes and Feed Forward Neural Network is used to classify the genes. The proposed MI-ANN based gene selection and ANN classification are applied to classify multi-class microarray datasets. The obtained results show that the proposed method provides higher classification accuracy compared to existing methods. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Soft computing

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Design, Modelling, and Analysis of Novel Solar PV System using MATLAB

Design, Modelling, and Analysis of Novel Solar PV System usi...

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International Conference on Computations in Materials and Applied Engineering, CMAE 2021

作者： Banik, Ayan Shrivastava, Anurag Manohar Potdar, Ravindra Kumar Jain, Sanjiv Gopal Nagpure, Santosh Soni, Mukesh National Institute of Technical Teachers' Training and Research Kolkata India Principal and Professor ECE Lakshmi Narain College of Technology and Science Indore India Department of EandTC Engineering Bhilai Institute of Technology Chhattisgarh India Department of Electrical Engineering Medi-Caps University Indore India Department of EandTC Engineering D. Y. Patil School of Engineering Academy Pune India Department of Computer Science and Engineering Jagran Lakecity University Bhopal India

In order to meet the world's future energy demand, alternative renewable green energy supplies must be widely promoted. Among all DERs, the most prevalent is Solar PV for a number of important reasons. In this study, the authors analyze several elements of the Single-Phase Solar Photovoltaic Rooftop System with MATLAB. In order to analyze the characteristics of the output current, a new grid synchronization methodology has been devised. For maximum power tracking, the INC control technique has been adopted. The output of the solar DC power system has been synchronized with the grid through hysteresis current loop control. Extensive testing verifies the practicality of the proposed power grid model and promising outcomes and results. © 2021 Elsevier Ltd. All rights reserved.

关键词： Electric inverters

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