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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Saadaldeen Rashid Ahmed Ali Jabbar Hussein Lubna Qassim ALhashmi Bourair Al-Attar Shaymaa Dheeb Duaa A. Majeed Abadal-Salam T. Hussain Jamal Fadhil Tawfeq Computer Science Department Bayan University Erbil Kurdistan Iraq Al Hikma University College Baghdad Iraq Department of Information and Knowledge Technologies College of Arts Basrah University Basrah Iraq College of Medicin University of Al-Ameed Karbala Iraq Department of Cyber Security and Cloud Computing Al-Ma’moon University College Baghdad Iraq Aeronautical Engineering Department Baghdad University Baghdad Iraq Department of Medical Instrumentation Techniques Engineering Technical Engineering College Al-Kitab University Kirkuk Iraq Department of Medical Instrumentation Technical Engineering Medical Technical College Al-Farahidi University Baghdad Iraq

ISBN: (数字)9798350394634

ISBN: (纸本)9798350394641

In today's competitive corporate landscape, the successful integration of computational intelligence approaches provides a key edge for decision-making processes. Despite developments, there is a gap in understanding the subtle application of such methodologies in corporate management, particularly within retail environments. To address this gap, our study combines decision trees and artificial neural networks to evaluate retail transaction data, concentrating on customer behavior and purchase trends. Leveraging a comprehensive dataset spanning client demographics, transaction details, and product information, our study provides substantial insights. Results suggest an 87% accuracy rate reached by artificial neural networks in forecasting client preferences. This study contributes to bridging the research gap by giving actionable insights into the practical application of computational intelligence in boosting company strategies and decision-making processes within the retail industry.

关键词： Ethics Decision making Companies Artificial neural networks Market research Turning Internet of Things

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Group Spam Identification in IOT Home Automation Using Tracking Time Metadata

Group Spam Identification in IOT Home Automation Using Track...

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2024 International Conference on Communication, computing and Energy Efficient Technologies, I3CEET 2024

作者： Docent, Achilov Dhanik, Saurabh Jindal, Pardeep Kumar Husain, Saif O. Bhardwaj, Diwakar Mittal, Varsha National Research University Tashkent Institute of Irrigation and Agricultural Mechanization Engineers Department of Physics and Chemistry Tashkent Uzbekistan Western Caspian University Baku Azerbaijan School of Computing Graphic Era Hill University Bhimtal India Chandigarh Group of Colleges Chandigarh Engineering College Department of Electrical and Communication Engineering Jhanjeri Punjab Mohali140307 India The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Gla University Department of Computer Engineering and Applications Mathura India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798331541583

With the stark increase due to smarter homes using online of Things, or IoT devices exchanging info far more frequently than we used to - all wirelessly. IoT devices are affected by several issues like possible cyberattacks, the unpredictability of network connectivity and data leakage since there are many types of IoT devices being used today. OBJECTIVES: To improve the privacy of data provided from web of Things (IoT) smart home devices by using machine training and computational modeling to detect anomalies. At its core the experiment contribution is how reliable Web of Things (WoT), i.e. IoT technologies transferring values from or to home things, reasons are taking different features such as average sq rate error origin and feature value in account of evaluations about internet-connected device efficacy. It is a metric that seems so important when we consider our dependency on every attached Internet of Things (IoT) device, in the network's architecture. The proposed method is applied and validated using a dataset of smart homes along with real temperature information collected for public access. Using Time series evidence collected by iot devices, the suggested method works extremely well as it can detect any anomaly or spam. This technique helps with the general health and safety of your entire structure by creating a solid base for evaluating the acceptability of Sea-of-things (SoT) gadgets in just a smart home network.. © 2024 IEEE.

关键词： Smart homes

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Explainable decision making with lean and argumentative explanations

arXiv

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arXiv 2022年

作者： Fan, Xiuyi Toni, Francesca School Of Computer Science And Engineering Nanyang Technological University Singapore Singapore Department Of Computing Imperial College London United Kingdom

It is widely acknowledged that transparency of automated decision making is crucial for deployability of intelligent systems, and explaining the reasons why some decisions are "good" and some are not is a way to achieving this transparency. We consider two variants of decision making, where "good" decisions amount to alternatives (i) meeting "most" goals, and (ii) meeting "most preferred" goals. We then define, for each variant and notion of "goodness" (corresponding to a number of existing notions in the literature), explanations in two formats, for justifying the selection of an alternative to audiences with differing needs and competences: lean explanations, in terms of goals satisfied and, for some notions of "goodness", alternative decisions, and argumentative explanations, reflecting the decision process leading to the selection, while corresponding to the lean explanations. To define argumentative explanations, we use assumption-based argumentation (ABA), a wellknown form of structured argumentation. Specifically, we define ABA frameworks such that "good" decisions are admissible ABA arguments and draw argumentative explanations from dispute trees sanctioning this admissibility. Finally, we instantiate our overall framework for explainable decision-making to accommodate connections between goals and decisions in terms of decision graphs incorporating defeasible and non-defeasible information. Copyright © 2022, The Authors. All rights reserved.

关键词： Decision making

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A Classification of Face Recognition System Using Faster Region-Based Convolutional Neural Network

A Classification of Face Recognition System Using Faster Reg...

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Data Science and Network Security (ICDSNS), International Conference on

作者： M. Maheswari Haider Alabdeli Aruna T M R. Archana Reddy Revathi. R Department of Computational Intelligence School of Computing SRM Institue of Science and Technology Kattankulathur India Department of Computers Techniques Engineering College of Technical Engineering The Islamic university Najaf Iraq Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India School of Computer Science and Engineering SR University Warangal India Department of Computer Science Karpagam Academy of Higher Education Echanari Coimbatore India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

In recent times, face recognition is a kind of biometric authentication which is widely used for security purpose in many areas like image processing, pattern recognition. However, recognizing the human face was very difficult as the poses in the input images vary, various conditions of light, brightness, expressions, ages. To overcome from it, a proposed method was introduced in this paper known as Faster Region-based Convolutional Neural Network (RCNN). The main advantage of using the proposed method is it will minimize the training process time as it consisting of two fully connected layers namely classifier and regression layer. Initially, the proposed method was implemented on ORL database consisting of 40 classes, then the images in the database undergoes image resizing and enhancing in the preprocessing stage. Where, Image resizing id done to reduce images size so that every image is same in size that will minimize the processing time and image enhancing was done to adjust the brightness of the image. In further, for extracting the features from preprocessed images, CNN is used, so that extracted features are given to the classification stage to detect the face of human or not. In detecting the faces, proposed Faster RCNN achieved high accuracy of 98.64% when compared with other existing methods like IFC, EFRA and VGG16.

关键词： Training Accuracy Image recognition Databases Face recognition Brightness Network security Feature extraction Classification algorithms Convolutional neural networks

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Review on Learning-based Methods for shop Scheduling problems

Review on Learning-based Methods for shop Scheduling problem...

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IEEE International Conference on e-Business engineering (ICEBE)

作者： Xiaoxia Li Xin Lu Wei Wang Yanguo Jing College of Informatics Huazhong Agricultural University Wuhan China Department of Computing and Informatics Bournemouth University Bournemouth UK School of Engineering Science University of Skovde Skovde Sweden Faculty of Business Computing and Digital Industries Leeds Trinity University Leeds UK

ISBN: (纸本)9781665492454

Shop scheduling is an effective way for manufacturers to improve their manufacturing performances. However, due to its complexity, it is difficult to deal with shop scheduling problems (SSP). Thus, SSP has received a lot of attention from industry and academia. Various kinds of methods have been proposed to solve SSP. Learning-based method is just one of the most representative methods for SSP. This paper focuses on reviewing the learning-based methods for SSP. Firstly, the methods for SSP are briefly introduced. Then, its description and model are provided and its classification is discussed. Next, the learning-based methods for SSP are classified according to the machine learning technique used in the methods. Based on the classification, the related work on each type of learning-based methods for SSP is summarized and further analyzed and compared with other traditional methods. Finally, the future research opportunities and challenges of the learning-based methods for SSP are summarized.

关键词： Learning systems Industries Job shop scheduling Machine learning Manufacturing Complexity theory

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Industrial Internet of Things Intrusion Detection Method Using Machine Learning and Optimization Techniques

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Wireless Communications and Mobile computing 2023年第1期2023卷

作者： Gaber, Tarek Awotunde, Joseph B. Folorunso, Sakinat O. Ajagbe, Sunday A. Eldesouky, Esraa Faculty of Computers and Informatics Suez Canal University Ismailia41522 Egypt School of Science Engineering and Environment University of Salford ManchesterM5 4WT United Kingdom Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Ilorin240003 Nigeria Department of Mathematical Science Olabisi Onabanjo University Ago-Iwoye120107 Nigeria Department of Computer and Industrial Production Engineering First Technical University Ibadan200255 Nigeria Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj11942 Saudi Arabia Department of Computer Science Faculty of Computers and Informatics Suez Canal University Ismailia41522 Egypt

The emergence of the Internet of Things (IoT) has witnessed immense growth globally with the use of various devices found in home, transportation, healthcare, and industry. The deployment and implementation of the IoT paradigm in industrial settings lead to the architectural changes of Industrial Automation and Control Systems (IACS) plus the countless connectivity of industrial systems. This resulted in what is referred to as the Industrial Internet of Things (IIoT), which removes the barrier of connecting IACS to isolated conventional ICT platforms. In recent times, the IoT has started hacking our personal lives and not only our world, thus creating a platform for impending IoT cyberattacks. The widespread use of the IoT has created a rich platform for possible IoT cyberattacks. Machine learning (ML) algorithms have been driven solutions to secure wireless communication in IIoT-based systems, and their use in solving various cybersecurity challenges. Therefore, this paper proposes a novel intrusion detection model based on the Particle Swarm Optimization (PSO) and Bat algorithm (BA) for feature selection, and the Random Forest (RF) classifier for the classification of malicious behaviors in IIoT-based network traffic. An IIoT-based cybersecurity dataset, WUSTL-IIOT-2021 Dataset, was used to evaluate the performance of the proposed model using accuracy, recall, precision, and F1-score. The results of the two feature selection were compared to identify the most promising one. The results were compared with other recent state-of-the-art ML and multiobjective algorithms, and the results showed better performance. The RF along with BA classifier had proved to be the best classifier. © 2023 Tarek Gaber et al.

关键词： Internet of things

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Naive Bayes-based Autonomous Illumination System for Urban Green Spaces with Cloud Assistance

Naive Bayes-based Autonomous Illumination System for Urban G...

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Sustainable Expert Systems (ICSES), International Conference on

作者： M Tamilselvi A. Neelima J. Jegan Amarnath K. Lalitha E. N. Ganesh Thamizhamuthu R Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science and Engineering Sagi Rama Krishnam Raju Engineering College (A) Bhimavaram Andhra Pradesh India Department of Computer Science And Engineering Sri Sairam Engineering College Chennai Tamil Nadu India Department of Information Technology Panimalar Engineering College Chennai Tamil Nadu India Director R&D Department of Electronics and Communication Engineering St. Peters Institute of Higher Education and Research (SPIHER) Avadi Chennai Tamil Nadu India Department of Computing Technologies Kattankulathur Campus-Chengalpattu SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Improving urban cities' quality of life is one of the primary functions of urban green spaces. Efficient lighting solutions in these areas enhance safety and ambiance. This research introduces a new method for autonomously controlling the lighting in urban parks with the help of a Naive Bayes classifier and cloud computing. The system uses information gathered from several sensors to adapt the brightness of the lights in response to changes in the surrounding environment and the presence or absence of people. The system can generate accurate choices in real-time using machine learning methods, including the Naive Bayes algorithm. With the help of the cloud, remote monitoring and administration are made easier, which improves efficiency and scalability. The experimental findings prove that the proposed approach is reliable and efficient in enhancing urban green areas' energy efficiency and user enjoyment. Intelligent algorithms combined with cloud computing provide a viable option for smart city lighting control, opening the door to more responsive and environmentally friendly cityscapes.

关键词： Cloud computing Smart cities Scalability Green products Lighting Aerospace electronics Energy efficiency Naive Bayes methods Sensors Safety

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Sending Query Data to Mobile Sinks at High Speed in Wireless Sensor Networks

Sending Query Data to Mobile Sinks at High Speed in Wireless...

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International Conference on Science Technology engineering and Management (ICONSTEM)

作者： Charanjeet Singh Subba Reddy V Narayan Krishan Vyas Mridul Gupta Sumit Kushwaha N.M.Sri Prasanna Department of Electronics and Communication Engineering DeenbandhuChhotu Ram University of Science and Technology Murthal Haryana India Department of Electronics and Communication Engineering KoneruLakshmaiah Education Foundation Guntur Andhra Pradesh India Department of Electronics and Communication Engineering Government Engineering College Jhalawar Rajasthan India Department of Electronics & Communication Engineering Graphic Era Deemed to be University Dehradun Uttarakhand Department of Computer Applications University Institute of Computing Chandigarh University Punjab India Department of English PSNA College of Engineering and Technology (Autonomous) Dindigul Tamil Nadu India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

Recently, many strategies to extend the lifespan of wireless sensor networks have made use of sink flexibility (WSNs). Mobile sink-based systems function with different power utilization between hubs, leading to power porting, in contrast to classic WSNs where haptic data from sensor arrays is ultimately communicated by static sinks. decrease the issue of The hub should record the most recent location of mobile sinks for data transfer if flexibility is needed. Containment detection and organizational networks are two of the most crucial elements for ensuring powerful natural detection and high-performance data communication in WSN (Wireless Sensor Network) applications. This project aims to set up, test, and enhance a data collecting framework that solves the issue of disconnected hubs, enhances the network between sensor hubs and base stations, and uses less power. Also, this paradigm aids in addressing some problems including unpredictable behavior and conflicts of interest. This study focuses on the potential applications for the suggested approach. Exploratory work that is truly ongoing will aim to provide a detailed analysis of cuttingedge applications, such as transport frameworks for common uses.

关键词： Wireless sensor networks Base stations Data transfer Mathematics Haptic interfaces Sensor arrays STEM

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Efficiency Improvement of Panels Using Thermoelectric Cooling Technique

Efficiency Improvement of Panels Using Thermoelectric Coolin...

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Innovation in Technology (ASIANCON), Asian Conference on

作者： Ashish Sharma Rinku Garg Anshul Sarawagi Krishna Kant Dixit Amandeep Singh Ahmed Alkhayyat Electrical Engineering Department Chandigarh University Mohali India University Institute of Computing Chandigarh University Mojhali India Department of CSE IES College of technolgy Bhopal India Department of Electrical Engineering GLA University Mathura India Department of Computer Applications Gulzar Group of Institutions Khanna Punjab College of Technical Engineering The Islamic University Najaf Iraq

Solar energy, with its vast potential as a sustainable energy source, is quickly rising to prominence. As the temperature of a PV panel rises, its efficiency drops. Therefore, when the temperature rises, the power output of the PV module decreases unless the heat is removed. The efficiency of PV panels could be improved with the help of module cooling. This thermoelectric system is utilized for cooling purposes. Combining photovoltaic (PV) and thermoelectric (Thermoelectric) modules can improve the overall efficiency of the solar energy conversion system by maintaining a consistent temperature. There has been research into thermoelectricity and a model of PV-Thermoelectric combo produced to show how effective these two types of modules may be when combined. The research demonstrates the effectiveness of a thermoelectric cooling system in improving the electric conversion efficiency of a PV array. If the heat is not dissipated, a PV module's output power decreases as its temperature rises

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Anomaly Detection in Pedestrian Walkways using Imperialist Competitive Algorithm with Deep Learning on Remote Sensing Images

Anomaly Detection in Pedestrian Walkways using Imperialist C...

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Innovations in Communications, Electrical and computer engineering (ICICEC), International Conference on

作者： Ch. Gangadhar K.Swathine E.Kamalanaban J. Senthil Murugan Swagata Sarkar Sangeethaa SN Department of Electronics & Communication Engineering Prasad V. Potluri Siddhartha Institute of Technology Vijayawada Andhra Pradesh India Computing - Artificial Intelligence Coimbatore Institute of Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Avadi India Artificial Intelligence and Data Science Sri Sairam Engineering College Chennai India Computer Science & Engineering Bannari Amman Institute of Technology Erode India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376524

Remote sensing images (RSI) describe images captured by sensors aboard satellites, drones, aircraft, or alternative platforms from a large distance above the Earth's surface. These images are useful for numerous applications, comprising agriculture, ecological monitoring, urban development, disaster management, and land use planning. Anomaly Detection (AD) in pedestrian walkways is a significant research domain, generally employed to increase the security of pedestrians. The conventional manual analysis of labeling abnormal actions is a tedious task owing to the extensive application of video surveillance systems and the increased quantity of captured videos. Currently, the expansion of deep learning (DL) methods are gained great interest in various methods of CV such as object classification and object detection, and such applications are dependent upon supervised learning that needs labels. This manuscript devises an Anomaly Detection in Pedestrian Walkways using Imperialist Competitive Algorithm with Deep Learning (ADPW-ICADL) technique on Remote Sensing Images. The ADPW-ICADL technique starts with Wiener filter (WF) scheme to develop the quality of the images by decreasing the noises. To derive features, the ADPW-ICADL technique applies a DenseNet100 architecture for capturing complicated patterns in pedestrian walkways. Also, the Imperialist Competitive Algorithm (ICA) is utilized for the hyperparameter tuning process to assure the optimal parameter selection of the DenseNet model. At last, Gradient Boosting Machine (GBM) is applied for proper identification of the anomalies in the walkway patterns. By wide-ranging experimental results on RSIs, the ADPW-ICADL technique demonstrates higher AD performance, emphasizing its impending for improving the dependability and productivity of pedestrian walkway investigation in several geographic settings.

关键词： Deep learning Productivity Pedestrians Noise computer architecture Classification algorithms Sensors Tuning Remote sensing Anomaly detection

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