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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Mishra, Abhinav Singh, Akhilendra Pratap Shankar, Amar Kaushik, Harshita Divya, K. Disha, S. Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Food Technology Faculty of Engineering and Technology Karnataka Ramnagar India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Wankhede Department of Computer Engineering Pune India

ISBN: (纸本)9798350319125

Huge facts and AI technologies have superior speedy to end up the primary drivers of innovation and progress in lots of one-of-a-kind areas, along with statistics retrieval. through the evaluation of big datasets of formerly retrieved documents and question logs, AI algorithms are capable of expect and perceive the maximum applicable files and also are used to higher recognize and respond to consumer queries. additionally, AI technology which include natural language processing are already beginning to shape the manner human beings engage with engines like Google, permitting extra conversational, person-friendly seek experience. eventually, massive information and AI have the capability to help form semantically based collections of documents with keywords and entities robotically extracted from content, making it easier for a search engine to analyze and connect files higher. This may in the end result in better retrieval satisfactory and greater dynamic records exploration studies for users of search engines. © 2023 IEEE.

关键词： analyze dynamic exploration records search

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A New Metaheuristic Approach to Solving Benchmark Problems: Hybrid Salp Swarm Jaya Algorithm

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computers, Materials & Continua 2022年第5期71卷 2923-2941页

作者： Erkan Erdemir Adem Alpaslan Altun Department of Information Technologies Tokat Vocational and Technical Anatolian High SchoolMerkez/Tokat60030Turkey Department of Computer Engineering Faculty of TechnologyKonya Selcuk UniversitySelcuklu/Konya42130Turkey

Metaheuristic algorithms are one of the methods used to solve optimization problems and find global or close to optimal solutions at a reasonable computational *** with other types of algorithms,in metaheuristic algorithms,one of the methods used to improve performance and achieve results closer to the target result is the hybridization of *** this study,a hybrid algorithm(HSSJAYA)consisting of salp swarm algorithm(SSA)and jaya algorithm(JAYA)is *** speed of achieving the global optimum of SSA,its simplicity,easy hybridization and JAYA’s success in achieving the best solution have given us the idea of creating a powerful hybrid algorithm from these two *** hybrid algorithm is based on SSA’s leader and follower salp system and JAYA’s best and worst solution *** works according to the best and worst food source *** this way,it is thought that the leader-follower salps will find the best solution to reach the food *** hybrid algorithm has been tested in 14 unimodal and 21 multimodal benchmark *** results were compared with SSA,JAYA,cuckoo search algorithm(CS),firefly algorithm(FFA)and genetic algorithm(GA).As a result,a hybrid algorithm that provided results closer to the desired fitness value in benchmark functions was *** addition,these results were statistically compared using wilcoxon rank sum test with other *** to the statistical results obtained from the results of the benchmark functions,it was determined that HSSJAYA creates a statistically significant difference in most of the problems compared to other algorithms.

关键词： Metaheuristic optimization benchmark algorithm swarm hybrid

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Determination of Compression Response for Various Dried Vegetables 1

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International Conference of Experimental and Numerical Investigations and New Technologies, CNNTech 2023

作者： Milošević, Miloš Jevtić, Ivana Trajković, Isaak Zlatanović, Ivan Mladenović, Goran Korolija, Nenad Innovation Centre of the Faculty of Mechanical Engineering Kraljice Marije 16 Street Belgrade11120 Serbia Faculty of Mechanical Engineering University of Belgrade Kraljice Marije 16 Street Belgrade11120 Serbia School of Electrical Engineering The Department of Computer Science and Information Technology University of Belgrade Belgrade11000 Serbia

ISBN: (数字)9783031464324

ISBN: (纸本)9783031464317

The texture of food plays a vital role in its overall quality, affecting both sensory experience and functional aspects, such as processing and preservation. To study texture in the field of food science and technology, various analytical methods are used to measure mechanical properties such as hardness, cohesiveness, and viscosity. One widely utilized technique for evaluating the texture of solid foods, particularly those with high firmness or brittleness such as dried vegetables, is compression testing. Here, a force is applied to a food sample until it deforms or fractures. In this particular study, dried onions, carrots, and peppers were chosen as test samples to investigate their texture and compression properties. To perform the tests, the researchers employed a Shimadzu AGS-X universal testing machine equipped with a 1 kN load cell capacity. This machine allowed them to compress the samples and measure the force required to achieve a specific deformation. The obtained results revealed that the texture and compression properties of the dried vegetables were significantly influenced by the specific part of the vegetable being tested. For instance, when examining the dried onions, a notable disparity was observed between the inner and outer parts. The basal plate of the onion, in particular, exhibited much greater hardness compared to the middle portion of the dried onion. Consequently, breaking the sample required a higher compression force. This discovery implies that the texture and compression properties of dried vegetables are closely tied to their inherent characteristics, internal structure, and composition. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Compression testing

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Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

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computer Systems Science & engineering 2023年第9期46卷 3121-3139页

作者： Khaled M.Alalayah Fatma S.Alrayes Jaber S.Alzahrani Khadija M.Alaidarous Ibrahim M.Alwayle Heba Mohsen Ibrahim Abdulrab Ahmed Mesfer Al Duhayyim Department of Computer Science College of Science and ArtsSharurahNajran UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

With the increased advancements of smart industries,cybersecurity has become a vital growth factor in the success of industrial *** Industrial Internet of Things(IIoT)or Industry 4.0 has revolutionized the concepts of manufacturing and production *** industry 4.0,powerful IntrusionDetection Systems(IDS)play a significant role in ensuring network *** various intrusion detection techniques have been developed so far,it is challenging to protect the intricate data of *** is because conventional Machine Learning(ML)approaches are inadequate and insufficient to address the demands of dynamic IIoT ***,the existing Deep Learning(DL)can be employed to identify anonymous ***,the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection(HGSODLID)model for the IIoT *** presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful *** HGSO algorithm is employed for Feature Selection(HGSO-FS)to reduce the curse of ***,Sparrow Search Optimization(SSO)is utilized with a Graph Convolutional Network(GCN)to classify and identify intrusions in the ***,the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN *** proposed HGSODL-ID model was experimentally validated using a benchmark dataset,and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.

关键词： Industrial IoT deep learning network security intrusion detection system attribute selection smart factory

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Anomaly-Based Intrusion DetectionModel Using Deep Learning for IoT Networks

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computer Modeling in engineering & Sciences 2024年第10期141卷 823-845页

作者： Muaadh A.Alsoufi Maheyzah Md Siraj Fuad A.Ghaleb Muna Al-Razgan Mahfoudh Saeed Al-Asaly Taha Alfakih Faisal Saeed Faculty of Computing Universiti Teknologi Malaysia(UTM)Johor Bahru81310Malaysia DAAI Research Group College of Computing and Digital TechnologyBirmingham City UniversityBirminghamB47XGUK Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11345Saudi Arabia

The rapid growth of Internet of Things(IoT)devices has brought numerous benefits to the interconnected ***,the ubiquitous nature of IoT networks exposes them to various security threats,including anomaly intrusion *** addition,IoT devices generate a high volume of unstructured *** intrusion detection systems often struggle to cope with the unique characteristics of IoT networks,such as resource constraints and heterogeneous data *** the unpredictable nature of network technologies and diverse intrusion methods,conventional machine-learning approaches seem to lack *** numerous research domains,deep learning techniques have demonstrated their capability to precisely detect *** study designs and enhances a novel anomaly-based intrusion detection system(AIDS)for IoT ***,a Sparse Autoencoder(SAE)is applied to reduce the high dimension and get a significant data representation by calculating the reconstructed ***,the Convolutional Neural Network(CNN)technique is employed to create a binary classification *** proposed SAE-CNN approach is validated using the Bot-IoT *** proposed models exceed the performance of the existing deep learning approach in the literature with an accuracy of 99.9%,precision of 99.9%,recall of 100%,F1 of 99.9%,False Positive Rate(FPR)of 0.0003,and True Positive Rate(TPR)of *** addition,alternative metrics,such as training and testing durations,indicated that SAE-CNN performs better.

关键词： IoT anomaly intrusion detection deep learning sparse autoencoder convolutional neural network

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Developing a Framework for Utilizing AI for Data Access Optimization 3

Developing a Framework for Utilizing AI for Data Access Opti...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Bogiri, Nagaraju Sinha, Sanjay Kumar Kaur, Ashmeet Singh, Vaishali Bahri, Dimple Raajini, X.Mercilin Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Civil Engineering Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India

ISBN: (纸本)9798350319125

The use of artificial intelligence for data access optimization is becoming increasingly important in the field of data science. AI algorithms can be used to create complex data structures that can be used to facilitate faster and more efficient access to data. Additionally, AI can be used to create frameworks capable of handling large amounts of data and optimizing the speed and accuracy of query results. This paper presents a framework for utilizing AI for data access optimization. In this framework, AI algorithms are used to create a structure for data storage and user interfaces that allow users to interact with the data more efficiently. Data mining techniques are used to identify patterns in the data that can be used to generate optimized queries. Additionally, the framework includes tools and libraries to automate processes and enable easy implementation of the developed framework. The proposed framework has been evaluated using a case study, and results show that the AI-based query optimization proposed in this framework can significantly reduce the query result time compared to traditional methods. © 2023 IEEE.

关键词： compare framework optimization techniques traditional

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Prediction of Chaotic Time Series Based on LSTM, Autoencoder and Chaos Theory 11th

Prediction of Chaotic Time Series Based on LSTM, Autoencoder...

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11th EAI International Conference on Context-Aware Systems and Applications, ICCASA 2022

作者： Huy, Nguyen Duc Anh, Duong Tuan Department of Information Technology HCMC University of Foreign Languages and Information Technology Ho Chi Minh City Viet Nam Faculty of Computer Science and Engineering Ho Chi Minh City University of Technology Ho Chi Minh City Viet Nam

ISBN: (纸本)9783031288159

Time-series forecasting, especially in a chaotic system, is a critical problem because its application is ubiquitous in several real-world fields, namely finance, environment, traffic, meteorology, industry, etc. In literature, there are many proposed methods for chaotic time series forecasting, but it is still challenging to yield a high predictive accuracy due to the chaotic characteristic which is very sensitive on the initial condition. In this work, we propose a fusion approach that takes advantage of chaos theory to represent time series data into phase space and combines autoencoder (AE) with Long Short-Terms Memory (LSTM) networks. First of all, the task of phase-space reconstruction starts with determining appropriate time lag and embedding dimension for the input time series. Next, autoencoder, which is constructed by LSTM cells, takes responsibility for latent-feature extraction through an unsupervised learning task and feeds the extracted data into LSTM-based forecaster. The experimental results on seven datasets including both synthetic and real-world chaotic time series reveal that our proposed method outperforms other forecasting methods using only stacked autoencoder, LSTM with or without chaos theory. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Long short-term memory

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An upgraded variant of backtracking search algorithm and its application to COVID-19 CT images segmentation

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Multimedia Tools and Applications 2025年 1-57页

作者： Debnath, Sanjib Nama, Sukanta Chakraborty, Sanjoy Saha, Apu Kumar Mirjalili, Seyedali Department of Computer Science & Engineering National Institute of Technology Agartala Tripura Agartala799046 India Department of Science & Humanities Gomati District Polytechnic Fulkumari Tripura Udaipur799013 India Department of Information Technology Bir Bikram Memorial College Tripura Agartala India Department of Mathematics National Institute of Technology Agartala Tripura Agartala799046 India Center for Artificial Intelligence Research and Optimization Torrens University Australia Brisbane Australia Faculty of Electrical Engineering and Computer Science VŠB-TU Ostrava Ostrava Czech Republic University Research and Innovation Center Obuda University Budapest Hungary Department of Computer Science & Engineering The ICFAI University Tripura Tripura 799210 India

To improve the performance of the original backtracking search algorithm (BSA), this work combines BSA with a centralized population initialization and an elitism-based local escape operator and proposes a new, improved variant of BSA called CiBSA. BSA's performance is improved by centralized population initialization regarding search capability, convergence speed, and capacity to jump out of the local optimum. The suggested algorithm's elitism-based local escape operator method prevents BSA searching slowness and enhances convergence rates and local search efficiency. The performance of the proposed algorithm has been tested on a set of 54 test suites. An extensive comparison with other recent meta-heuristics algorithms (MA) demonstrates that both methodologies substantially assist BSA in boosting the quality of its solutions and speeding up the convergence rate. The proposed CiBSA is then applied to a real-world problem: image segmentation using Kapur's entropy function. The proposed CiBSA is successfully applied to the segmentation of COVID-19 CT images using Kapur's entropy for grey-scale images. Considering the performance matrices Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Feature Similarity Index (FSIM), and Intersection over Union (IoU), the suggested CiBSA outperformed conventional MA algorithms. In COVID-19 CT image segmentation trials, CiBSA has superior segmentation capability and adaptability at varied threshold levels than other approaches. As a result, CiBSA has a lot of potential for enhancing COVID-19 diagnosis accuracy. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Signal to noise ratio

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Designing Optimal Parameters for Single-Axis Solar-Tracker through Analysis of Sun-Path and Tracking Vector Dynamics 6

Designing Optimal Parameters for Single-Axis Solar-Tracker t...

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6th International Conference on Electrical engineering and information and Communication technology, ICEEICT 2024

作者： Akhtaruzzaman, M. Islam, Md Shofiqul Shahryer, Fahim Arif, Muammer Din Mollik, Md. Sazib Rahman, Muhammad Towfiqur Rahman, Md. Mahbubur Hasan, Mohammad Kamrul Ahmed, Shugata Ashrafuzzaman, Md. Department of Computer Science and Engineering Dhaka1216 Bangladesh Department of Mechanical Engineering Dhaka1216 Bangladesh Department of Electrical and Electronic Engineering Dhaka Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh Faculty of Information Science and Technology Bangi Malaysia Department of Robotics and Mechatronics Engineering Bangladesh Sdn. Bhd. Cyberjaya Malaysia

ISBN: (纸本)9798350385779

The persistent challenges faced by sun-tracking systems include inefficient power production, wastage of energy, ineffective control, and high costs. Most of the existing systems are either static or dual-axis tracking which lead to inefficient output or increasing energy wastage and cost overheads. Though, single-axis tracking system has some benefits over the static or dual-axis tracking, most of the systems do not analyze the selection of optimal sun-path which again leads to inefficient and inadequate absorption of sunlight. Addressing the issues, this article presents a design of optimal parameters for single-axis solar-tracking by analyzing the sun-path and tracking vector dynamics. The study mainly focuses on analyzing solar-tracking dynamics, variations in sunray angles, Azimuth and Elevation dynamics throughout the day for the twelve months, Sun-vector calculation for local coordinates, joint rotational-axis, and joint motion control using a Proportional-Derivative controller. Additionally, the article outlines a conceptual architecture for automated control, monitoring, and power distribution for the potential development of a sustainable smart city. The proposed strategy addresses the issues of the optimal design of a single-axis solar-tracker, thus ensuring the reduction of cost overhead and energy wastage. © 2024 IEEE.

关键词： Dynamics

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Arabic Sentiment Analysis of Food Delivery Services Reviews

Arabic Sentiment Analysis of Food Delivery Services Reviews

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2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Mustafa, Dheya Khabour, Safaa M. Shatnawi, Ahmed S. Taqieddin, Eyad The Hashemite University Faculty of Engineering Dept. of Computer Engineering Zarqa13133 Jordan Yarmouk University Faculty of Information Technology and Computer Sciences Dept. of Information Systems Irbid21163 Jordan Jordan University of Science & Technology Dept. of Software Engineering Irbid21110 Jordan School of Computing and Informatics Dept. of Cyber Security Al-Hussein Technical University Amman Jordan

ISBN: (纸本)9798350335590

Customer reviews on online platforms have grown to become an important source of insight into a company's performance. Food delivery services (FDS) companies aim to effectively use customers' feedback to identify areas for improvement of customer satisfaction. Although Arabic is becoming one of the most widely used languages on the Internet, only a few studies have focused on Arabic sentiment analysis to date. The present study conducts an extensive emotion mining and sentiment analysis on FDS-related reviews in Arabic, exploiting natural language processing, and machine learning techniques to extract information that reflects customers' subjective viewpoints, recognize their feelings, and determine polarity in the FDS domain. This work begins with collecting the FDS dataset (Talabat), and then extracting and creating a dialects lexicon for Arabic dialects, followed by walking the reader through detailed steps of cleaning and pre-processing a manually annotated dataset. Finally, we examined classification algorithms including Decision Tree (DT), and Support Vector Machine (SVM). We achieved a maximum accuracy of about 82% using the SVM classifier. © 2023 IEEE.

关键词： Sentiment analysis

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