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2nd IEEE International Conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Kanniga, Devi Kumari, Neeraj Department of Computer Science and Engineering Bangalore India Teerthanker Mahaveer University College of Computing Science & It Department of Computing Science & It Moradabad India

ISBN: (纸本)9798350347456

Reliable routing systems for industrial conveyor weighing are essential for ensuring accurate weight readings and efficient operation. Conveyor weighing systems are used in a variety of industrial contexts, including food production and packaging, chemical processing, and the manufacturing of heavy equipment. The reliability of these systems is critical for the successful operation of these industries. The primary components of a reliable routing system for industrial conveyor weighing are the conveyor belt, the load sensors, and the control system software. The conveyor belt is the most essential part of the system, as it carries the material being weighed. The load sensors measure the weight of the material as it moves along the conveyor belt and sends this information to the control system. The software then processes the weight data and sends commands to the conveyor belt, adjusting its speed and direction to ensure accurate weight readings. A reliable routing system should also include safety features to protect operators and equipment. Sensors should be placed near the conveyor belt to detect any potential hazards and notify the operator accordingly. The system should also feature emergency stop buttons to quickly shut down the conveyor belt in the event of an emergency. Additionally, the system should be equipped with alarms and other warning systems to alert operators of any potential issues. © 2023 IEEE.

关键词： Chemical industry

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An Innovation of Energy Harvesting for Small Scale Robotics in Automation Industry 2

An Innovation of Energy Harvesting for Small Scale Robotics ...

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2nd IEEE International Conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Mehrotra, Tushar Shetty, Sahana Teerthanker Mahaveer University College of Computing Science & It Department of Computing Science & It Moradabad India Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

Robotics is a rapidly emerging technology in the automation industry, and small-scale robotics is becoming increasingly prevalent in automation applications. Small-scale robotics can be used to automate a variety of manufacturing processes, such as assembly, welding, and painting. Small-scale robotics can be used to improve efficiency and reduce labor costs, allowing companies to produce products faster and more reliably with fewer resources. Small-scale robots are typically designed with a smaller size and weight than traditional industrial robots, making them suitable for applications with limited space and weight requirements. Small-scale robots are capable of performing a wide range of tasks, including pick-and- place operations, vision-guided navigation, logistics, and even complex manipulation tasks. Small-scale robots can provide a number of benefits to automation operations. For example, they can reduce labor costs by automating manual tasks. Small-scale robots are also capable of performing highly accurate and repeatable tasks, which can improve the quality of products. Additionally, small-scale robots can be used to increase throughput and reduce cycle times, enabling companies to produce more products in less time. © 2023 IEEE.

关键词： Wages

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Reinforcing Artificial Neural Networks through Traditional Machine Learning Algorithms for Robust Classification of Cancer

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computers, Materials & Continua 2023年第5期75卷 4293-4315页

作者： Muhammad Hammad Waseem Malik Sajjad Ahmed Nadeem Ishtiaq Rasool Khan Seong-O-Shim Wajid Aziz Usman Habib Department of Computer Sciences and Information Technology University of Azad Jammu and KashmirMuzaffarabad13100Pakistan Department of Computer Science and AI College of Computer Science and EngineeringUniversity of JeddahJeddah23890Saudi Arabia Department of Computer and Network Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah23890Saudi Arabia Department of AI&DS School of ComputingNational University of Computer and Emerging SciencesIslamabad44000Pakistan

Machine Learning(ML)-based prediction and classification systems employ data and learning algorithms to forecast target ***,improving predictive accuracy is a crucial step for informed *** the healthcare domain,data are available in the form of genetic profiles and clinical characteristics to build prediction models for complex tasks like cancer detection or *** ML algorithms,Artificial Neural Networks(ANNs)are considered the most suitable framework for many classification *** network weights and the activation functions are the two crucial elements in the learning process of an *** weights affect the prediction ability and the convergence efficiency of the *** traditional settings,ANNs assign random weights to the *** research aims to develop a learning system for reliable cancer prediction by initializing more realistic weights computed using a supervised setting instead of random *** proposed learning system uses hybrid and traditional machine learning techniques such as Support Vector Machine(SVM),Linear Discriminant Analysis(LDA),Random Forest(RF),k-Nearest Neighbour(kNN),and ANN to achieve better accuracy in colon and breast cancer *** system computes the confusion matrix-based metrics for traditional and proposed *** proposed framework attains the highest accuracy of 89.24 percent using the colon cancer dataset and 72.20 percent using the breast cancer dataset,which outperforms the other *** results show that the proposed learning system has higher predictive accuracies than conventional classifiers for each dataset,overcoming previous research ***,the proposed framework is of use to predict and classify cancer patients ***,this will facilitate the effective management of cancer patients.

关键词： ANN decision support systems gene-expression data hybrid classification machine learning predictive analytics

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APDO: A Hybrid Aquila Optimizer and Prairie Dog Optimization Metaheuristic Algorithm for Global, Optimization

APDO: A Hybrid Aquila Optimizer and Prairie Dog Optimization...

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2023 computer Applications and Technological Solutions, CATS 2023

作者： Zitouni, Farouq Harous, Saad Lagrini, Samira Cheradid, Abdellatif Guerfi, Sahla Frihi, Ferdousse Saida Kasdi Merbah University Department of Computer Science and Information Technology Ouargla Algeria University of Sharjah College of Computing and Informatics Department of Computer Science Sharjah United Arab Emirates Badji Mokhtar University Computer Science Department Annaba Algeria

ISBN: (纸本)9798350383881

We propose a hybrid metaheuristic algorithm that combines the strengths of the Aquila Optimizer (AO) and the Prairie Dog Optimization (PDO) algorithms. The proposed algorithm is named the Aquila Prairie Dog Optimization (APDO) algorithm. During the initialization phase of the APDO algorithm, chaotic maps are employed to enhance the exploration capabilities, as they introduce randomness into the initialization process. In addition, opposition-based learning is incorporated during the swarming process, wherein the objective is to consider the opposite values of the current solutions, to expand the diversity of the swarm and to avoid local optimums. Moreover, to assess and evaluate the performance of the APDO algorithm, a comprehensive comparative analysis was conducted by benchmarking it against five widely recognized metaheuristics, across a diverse set of challenging optimization problems, encompassing: unimodal, multimodal, hybrid and composition functions. Finally, the performance evaluation of the APDO algorithm was conducted using the Friedman post hoc Dunn's test, which revealed compelling results indicating that the APDO algorithm demonstrated superior performance in the majority of cases, when compared to the other benchmarked algorithms, thereby showcasing its competitiveness and efficacy in tackling diverse optimization problems. © 2023 IEEE.

关键词： Global optimization

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Enhanced Energy Optimization of Structured Random Matrix Model in Industrial Automation 2

Enhanced Energy Optimization of Structured Random Matrix Mod...

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2nd IEEE International Conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Bishnoi, Amit Kumar Vivek, V. Teerthanker Mahaveer University College of Computing Science & It Department of Computing Science & It Moradabad India Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

The Structured Random Matrix (SRM) model is an increasingly popular approach to industrial automation. This model is based on the idea that the control systems of industrial machines are "structured"in a certain way, with certain properties that can be predicted and used to model the behavior of the system. SRM incorporates the principles of random matrix theory in order to provide an efficient, reliable, and realistic model for industrial automation. The SRM model works by taking a large set of variables, such as the type of machine, the speed of the machine, the number of required parts, and the number of different components to be used in the system. These variables are then used to generate a random matrix, which can then be analyzed to identify patterns and correlations between the different variables. This allows engineers to develop more accurate models of the system, as well as to identify potential problems and solutions. In addition to providing a more accurate model, the SRM model also allows engineers to simplify the design process by reducing the number of variables used in the system. This simplification means that fewer parts are needed in the system, which reduces the cost and complexity of the system. Furthermore, by reducing the complexity of the system, engineers can reduce the amount of time spent on maintenance and repairs, which saves money and increases efficiency. © 2023 IEEE.

关键词： Automation

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An Smart Analysis of Energy Efficiency Issues in Industrial Automation 2

An Smart Analysis of Energy Efficiency Issues in Industrial ...

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2nd IEEE International Conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Singhal, Priyank Mahesh, T.R. Teerthanker Mahaveer University College of Computing Science & It Department of Computing Science & It Moradabad India Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

Industrial automation has become increasingly important in recent years. It has allowed for increased efficiency, productivity and safety in the workplace, as well as improved quality control of products. However, with the increased use of automation, comes the need to be mindful of energy efficiency issues. Energy use in industrial automation is primarily driven by the use of electric motors, pumps and other devices. These devices consume a large amount of electricity, especially when they are running continuously or at high speeds. This can result in significant energy losses and increased costs. Additionally, when the motors are operating at different speeds, they can draw more power than they need and this can also cause energy losses. To reduce energy losses, it is important to ensure that the motors and pumps are properly sized for the application and that they are running at the correct speed. Additionally, it is important to take into account the efficiency of the motors and pumps. For example, variable speed drives can introduce energy savings in certain applications. Additionally, energy-efficient motors and pumps can reduce energy losses significantly. © 2023 IEEE.

关键词： Automation

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The Smart Application Development in Real Time Parallel Applications with Industrial Automation 2

The Smart Application Development in Real Time Parallel Appl...

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2nd IEEE International Conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Khan, Gulista Zabeeulla, Zabeeulla Teerthanker Mahaveer University College of Computing Science & It Department of Computing Science & It Moradabad India Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

Real-time parallel applications with industrial automation are applications that are designed to run in parallel, in order to accomplish a task in a shorter amount of time. These applications are used in industrial environments to control machines and other processes in order to maximize efficiency and minimize waste. Real-time parallel applications allow machines and processes to be managed simultaneously. This means that instead of needing to wait for one process to finish before starting the next, multiple processes can be run at the same time. This increases the speed at which tasks can be completed, allowing for greater efficiency and productivity. Real-time parallel applications can be used to automate processes and machines. This means that instead of needing to manually manage and monitor a machine, the software can handle all of the work. This can help to reduce errors and improve the overall quality of the product being produced. © 2023 IEEE.

关键词： Efficiency

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Modeling Hybrid Feature-Based Phishing Websites Detection Using Machine Learning Techniques

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Annals of Data science 2024年第1期11卷 217-242页

作者： Das Guptta, Sumitra Shahriar, Khandaker Tayef Alqahtani, Hamed Alsalman, Dheyaaldin Sarker, Iqbal H. Department of Computer Science and Engineering Chittagong University of Engineering & ampTechnology Chittagong4349 Bangladesh Unit of Cybersecurity Department of Computer Science Center of Artificial Intelligence King Khalid University Abha Saudi Arabia School of Engineering Computing and Informatics Dar Al-Hekma University Jeddah Saudi Arabia

In this paper, we mainly present a machine learning based approach to detect real-time phishing websites by taking into account URL and hyperlink based hybrid features to achieve high accuracy without relying on any third-party systems. In phishing, the attackers typically try to deceive internet users by masking a webpage as an official genuine webpage to steal sensitive information such as usernames, passwords, social security numbers, credit card information, etc. Anti-phishing solutions like blacklist or whitelist, heuristic, and visual similarity based methods cannot detect zero-hour phishing attacks or brand-new websites. Moreover, earlier approaches are complex and unsuitable for real-time environments due to the dependency on third-party sources, such as a search engine. Hence, detecting recently developed phishing websites in a real-time environment is a great challenge in the domain of cybersecurity. To overcome these problems, this paper proposes a hybrid feature based anti-phishing strategy that extracts features from URL and hyperlink information of client-side only. We also develop a new dataset for the purpose of conducting experiments using popular machine learning classification techniques. Our experimental result shows that the proposed phishing detection approach is more effective having higher detection accuracy of 99.17% with the XG Boost technique than traditional approaches. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Search engines

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Using Machine Learning to Identify and Categorize Personally Identifiable Information and Payment Card Industry Data in Textual Content 4

Using Machine Learning to Identify and Categorize Personally...

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4th International Conference on Advanced Research in computing, ICARC 2024

作者： Arambawela, Milinda Aponso, Achala University of Westminster Computer Science and Engineering London United Kingdom Informatics Institute of Technology Department of Computing Colombo Sri Lanka

ISBN: (纸本)9798350384864

The advent of the Internet has significantly stream-lined daily tasks through the rapid increase of online services. Everyday activities, such as purchasing goods and scheduling appointments with healthcare professionals, have become more speedy, efficient and user-friendly with the integration of the Internet. The continuous improvement of online services has led to many people moving towards digital activities. As a result, it has heightened the recording of personal and payment transaction data across various storage mediums, including databases and log files. The protection and regulation of this sensitive data are imperative, aligning with the guidelines outlined in GDPR and PCI-DSS compliances. Recognizing exposed personal data poses a considerable challenge. This research introduces a novel approach to identifying payment card industry data (PCI) and personally identifiable information (PII). The research project proposes a machine learning-based text classification model utilizing the Convolutional Neural Network (CNN) model to discern PII and PCI data within a given text. The CNN model has been constructed and compared against Naive Bayes, Gradient Boost, Random Forest, and Support Vector Machine (SVM) models. The CNN model achieved the highest accuracy at 0.96 (96%). Additionally, the F1 scores for each class were significant, with PII scoring 0.94, PCI scoring 0.95, and Normal scoring 0.99. Following the model's construction and training, it was employed with the saved tokenizer's word indexes and label encoders in the developed classification tool. This tool successfully delivered the promised results, identifying exposed PII and PCI data. © 2024 IEEE.

关键词： Neural network models

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Survey on Video Security:Examining Threats,Challenges,and Future Trends

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computers, Materials & Continua 2024年第9期80卷 3591-3635页

作者： Ali Asghar Amna Shifa Mamoona Naveed Asghar Department of Artificial Intelligence Faculty of ComputingThe Islamia University of BahawalpurBahawalpur63100Pakistan School of Computer Science College of Science and EngineeringUniversity of GalwayGalwayH91 TK33Ireland

Videos represent the most prevailing form of digital media for communication,information dissemination,and ***,theirwidespread use has increased the risks of unauthorised access andmanipulation,posing significant *** response,various protection approaches have been developed to secure,authenticate,and ensure the integrity of digital *** study provides a comprehensive survey of the challenges associated with maintaining the confidentiality,integrity,and availability of video content,and examining how it can be *** then investigates current developments in the field of video security by exploring two critical research ***,it examine the techniques used by adversaries to compromise video data and evaluate their *** these attack methodologies is crucial for developing effective defense ***,it explores the various security approaches that can be employed to protect video data,enhancing its transparency,integrity,and *** compares the effectiveness of these approaches across different use cases,including surveillance,video on demand(VoD),and medical videos related to disease ***,it identifies potential research opportunities to enhance video data protection in response to the evolving threat *** this investigation,this study aims to contribute to the ongoing efforts in securing video data,providing insights that are vital for researchers,practitioners,and policymakers dedicated to enhancing the safety and reliability of video content in our digital world.

关键词： Attacks threats security services video manipulation video security

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