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Arithmetic Optimization with Deep Learning Enabled Anomaly Detection in Smart City

Computers, Materials & Continua 2022年第10期73卷 381-395页

作者： Mahmoud Ragab Maha Farouk S.Sabir Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Information Systems Department Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia

In recent years,Smart City Infrastructures(SCI)have become familiar whereas intelligent models have been designed to improve the quality of living in smart ***,anomaly detection in SCI has become a hot research topic and is widely explored to enhance the safety of *** increasing popularity of video surveillance system and drastic increase in the amount of collected videos make the conventional physical investigation method to identify abnormal actions,a laborious *** this background,Deep Learning(DL)models can be used in the detection of anomalies found through video surveillance *** current research paper develops an Internet of Things Assisted Deep Learning Enabled Anomaly Detection Technique for Smart City Infrastructures,named(IoTAD-SCI)*** aim of the proposed IoTAD-SCI technique is to mainly identify the existence of anomalies in smart city ***,IoTAD-SCI technique involves Deep Consensus Network(DCN)model design to detect the anomalies in input video *** addition,Arithmetic Optimization Algorithm(AOA)is executed to tune the hyperparameters of the DCN ***,ID3 classifier is also utilized to classify the identified objects in different *** experimental analysis was conducted for the proposed IoTADSCI technique upon benchmark UCSD anomaly detection dataset and the results were inspected under different *** simulation results infer the superiority of the proposed IoTAD-SCI technique under different metrics.

关键词： Object detection anomaly detection smart city infrastructure deep learning parameter tuning

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Analyzing video footage from security cameras using the Amped FIVE professional tool specifically for the identification of vehicle license plates 4

Analyzing video footage from security cameras using the Ampe...

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4th International Conference on computing, Networking, Telecommunications and Engineering Sciences Applications, CoNTESA 2023

作者： Gvozdanovic, Misel Delija, Damir Tulicic, Domagoj Sirovatka, Goran Zagar, Marinko Bilic, Krunoslav Zagreb University of Applied Science Department of Informatics and Computing Zagreb Croatia University of Applied Sciences in Information Technology Department of Computer Systems and Networks Zagreb Croatia

ISBN: (纸本)9798350382754

This paper describes problems of improving security cameras' video footage for forensic investigation. When processing records, one encounters problems with low resolution, poor lighting, or excessive distance of a key object in an image or video. Using the Amped FIVE Professional tool, forensic scientists create videos and analyze and process the record itself using various filters. Record quality demands a choice of video filters. The use and selection of filters show the improvement of the record itself to the level to which useful information can be obtained in this case of vehicle license plates. The presented roadmap can be used as the base for further video forensic research. © 2023 IEEE.

关键词： Image enhancement

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Enhanced Artificial Intelligence-based Cybersecurity Intrusion Detection for Higher Education Institutions

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Computers, Materials & Continua 2022年第8期72卷 2895-2907页

作者： Abdullah S.AL-Malaise AL-Ghamdi Mahmoud Ragab Maha Farouk S.Sabir Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt

As higher education institutions(HEIs)go online,several benefits are attained,and also it is vulnerable to several kinds of *** accomplish security,this paper presents artificial intelligence based cybersecurity intrusion detection models to accomplish *** incorporation of the strategies into business is a tendency among several distinct industries,comprising education,have recognized as game ***,the HEIs are highly related to the requirement and knowledge of the learner,making the education procedure highly ***,artificial intelligence(AI)and machine learning(ML)models have shown significant interest in *** study designs a novel Artificial Intelligence based Cybersecurity Intrusion Detection Model for Higher Education Institutions named AICIDHEI *** goal of the AICID-HEI technique is to determine the occurrence of distinct kinds of intrusions in higher education *** AICID-HEI technique encompassesmin-max normalization approach to preprocess the ***,the AICID-HEI technique involves the design of improved differential evolution algorithm based feature selection(IDEA-FS)technique is applied to choose the feature ***,the bidirectional long short-term memory(BiLSTM)model is utilized for the detection and classification of intrusions in the ***,the Adam optimizer is applied for hyperparameter tuning to properly adjust the hyperparameters in higher educational *** order to validate the experimental results of the proposed AICID-HEI technique,the simulation results of the AICIDHEI technique take place by the use of benchmark *** experimental results reported the betterment of the AICID-HEI technique over the other methods interms of different measures.

关键词： Higher education institutions deep learning machine learning cybersecurity intrusion detection

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Evaluating Satisfaction and Intention of Online Enrollment: A Case of a University in the Philippines

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Procedia Computer Science 2025年 257卷 1190-1196页

作者： Nathan Kit A. Berro Saimon L. Mag-aso Rhymes Polinar Jocelyn D. Rodrigo Melbert R. Bonotan Department of Information Systems College of Computing and Information Sciences Caraga State University Butuan City 8600 Philippines Department of Information Technology College of Computing and Information Sciences Caraga State University Butuan City 8600 Philippines

Online enrollment system is crucial to the expansion of the school population. During the pandemic, online systems became essential to ensure students’ education continued safely, prioritizing online enrollment and e-learning for institutions. This study investigates students’ satisfaction and intention to use Caraga State University’s online enrollment. A quantitative research approach was employed, utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM) for data analysis. The study evaluated perceived usefulness (PU), perceived ease of use (PEOU), and satisfaction (S) to assess intention to use (IU). The findings revealed that PU significantly influences both S and IU, while PEOU significantly impacts PU and S. However, PEOU does not directly affect IU. Additionally, S was found to substantially impact IU. The study underscores the pivotal role of usefulness in shaping satisfaction and intention to use, highlighting the importance of usability in enhancing both usefulness and satisfaction.

关键词： Online Enrollment System Intention to Use IU Satisfaction Perceived Usefulness PU Perceived Ease of Use PEOU

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Improved Harmony Search with Optimal Deep Learning Enabled Classification Model

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Computers, Materials & Continua 2022年第10期73卷 1783-1797页

作者： Mahmoud Ragab Adel A.Bahaddad Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre for Artificial Intelligence in Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

Due to drastic increase in the generation of data,it is tedious to examine and derive high level knowledge from the *** rising trends of high dimension data gathering and problem representation necessitates feature selection process in several machine learning *** feature selection procedure establishes a generally encountered issue of global combinatorial *** FS process can lessen the number of features by the removal of unwanted and repetitive *** this aspect,this article introduces an improved harmony search based global optimization for feature selection with optimal deep learning(IHSFS-ODL)enabled classification *** proposed IHSFS-ODL technique intends to reduce the curse of dimensionality and enhance classification *** addition,the IHSFSODL technique derives an IHSFS technique by the use of local search method with traditional harmony search algorithm(HSA)for global ***,ODL based classifier including quantum behaved particle swarm optimization(QPSO)with gated recurrent unit(GRU)is applied for data classification *** utilization of HSA for the choice of features and QPSO algorithm for hyper parameter tuning processes helps to accomplish maximum classification *** order to demonstrate the enhanced outcomes of the IHSFS-ODL technique,a series of simulations were carried out and the results reported the betterment over its recent state of art approaches.

关键词： Data classification feature selection global optimization deep learning metaheuristics

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Deep Learning-based Paddy Doctor for Sustainable Agriculture 7

Deep Learning-based Paddy Doctor for Sustainable Agriculture

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7th International Conference on Image information Processing, ICIIP 2023

作者： Garg, Vijay Agarwal, Stuti Sharma, Sahil International Institute of Information Technology Bangalore India Panjab University Chandigarh India Ulster University School of Computing Engineering And Intelligent Systems Londonderry United Kingdom

ISBN: (纸本)9798350371406

In promoting sustainable agriculture, the prompt and precise identification of plant diseases and pests becomes critical in mitigating economic setbacks via timely interventions. Utilizing EfficientNet, a deep learning-based convolutional neural network (CNN), we categorized images using open-source datasets on various rice strains. Our empirical findings confirm that our suggested model architecture attains a commendable accuracy of 91% with imbalanced classes. Discarding blurry images and employing data augmentation techniques enhanced the model's precision in rice disease categorization by an additional 8.4%. The corresponding code will be publicly accessible here. © 2023 IEEE.

关键词： Convolutional neural networks

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Ontology-Based Development of Domain-Specific Languages via Customizing Base Language 16

Ontology-Based Development of Domain-Specific Languages via ...

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16th IEEE International Conference on Application of information and Communication Technologies, AICT 2022

作者： Kulagin, Grigory Ermakov, Ivan Lyadova, Lyudmila Hse University Department of Information Technologies in Business Perm Russia Perm State University Department of Computing Systems Software Perm Russia

ISBN: (纸本)9781665451628

The quality of the systems depends on compliance to the domain requirements. High quality is achieved only with involving experts in the relevant fields to the system design as experts. Modern design methods are based on using professional tools and modeling languages. Using these tools are difficult for domain experts. Domain-Specific Languages (DSLs) can be considered as "user interfaces"for experts because they bridge the gap between the domain experts and the software development tools via customizing modeling languages. Usability of DSLs by domain experts is a key factor for their successful adoption. But DSL creation is challenging task. An approach to DSL customization based on using multifaceted ontology is proposed. General scheme of DSL metamodel generation based on multifaceted ontology is described. Examples of created DSLs and models illustrating the applicability of the proposed method are shown. The DSL metamodels were developed and tested in several domains. The results of experiments confirmed practical significance of the ontology-based approach to DSL creation. © 2022 IEEE.

关键词： Ontology

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Exploring the Role of Generative AI in Medical Microbiology Education: Enhancing Bacterial Identification Skills in Laboratory Students 1st

Exploring the Role of Generative AI in Medical Microbiology ...

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1st International Workshop on Breaking Barriers with Generative Intelligence, BBGI 2024

作者： Al-Barazie, Ray Mohamed, Azza Lin, Fuhua Faculty of Medical and Health Sciences Liwa College Al-Ain United Arab Emirates Faculty of Information Technology Liwa College Al-Ain United Arab Emirates School of Computing and Information Systems Faculty of Science and Technology Athabasca University AthabascaAB Canada

ISBN: (纸本)9783031659959

The precise identification of pathogens in biological material is critical for appropriate medical diagnosis and therapy. Medical laboratory students must be proficient in laboratory skills since they play a critical part in the diagnostic process. Using appropriate microscope techniques, one must be able to identify a wide range of pathogens, including bacteria, viruses, parasites, and fungi. Traditional methods of skill development include on-campus practical lessons and field training in hospital microbiology departments. However, the emergence of generative artificial intelligence (AI) opens new avenues for educational enhancement. This study investigates the feasibility of using generative AI, specifically Gemini, to train medical laboratory students in bacterial identification using morphological traits seen in micrographs. The study assessed student learning results using Gemini-generated case studies and quizzes. The results showed that Gemini-generated quizzes helped pupils identify different bacteria based on the micrographs supplied. However, limitations were identified, such as the requirement for teachers to manually add photographs. Overall, the study highlights the potential of generative AI tools in educational contexts, arguing that they could supplement traditional teaching techniques and improve the learning experience for medical laboratory students. More research into generative AI's educational applications is needed to fully realize its potential in medical education. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Students

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Coati Optimization-Based Energy Efficient Routing Protocol for Unmanned Aerial Vehicle Communication

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Computers, Materials & Continua 2023年第6期75卷 4805-4820页

作者： Hanan Abdullah Mengash Hamed Alqahtani Mohammed Maray Mohamed K.Nour Radwa Marzouk Mohammed Abdullah Al-Hagery Heba Mohsen Mesfer Al Duhayyim Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial Intelligence and Unit of CybersecurityKing Khalid UniversityAbha62529Saudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbha62529Saudi Arabia Department of Computer Sciences College of Computing and Information SystemsUmm Al-Qura UniversityMecca24382Saudi Arabia Department of Computer Science College of ComputerQassim UniversityBuraydah52571Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

With the flexible deployment and high mobility of Unmanned Aerial Vehicles(UAVs)in an open environment,they have generated con-siderable attention in military and civil applications intending to enable ubiquitous connectivity and foster agile *** difficulty stems from features other than mobile ad-hoc network(MANET),namely aerial mobility in three-dimensional space and often changing *** the UAV network,a single node serves as a forwarding,transmitting,and receiving node at the same ***,the communication path is multi-hop,and routing significantly affects the network’s performance.A lot of effort should be invested in performance analysis for selecting the optimum routing *** this motivation,this study modelled a new Coati Optimization Algorithm-based Energy-Efficient Routing Process for Unmanned Aerial Vehicle Communication(COAER-UAVC)*** presented COAER-UAVC technique establishes effective routes for communication between the *** is primarily based on the coati characteristics in nature:if attacking and hunting iguanas and escaping from ***,the presented COAER-UAVC technique concentrates on the design of fitness functions to minimize energy utilization and communication delay.A varied group of simulations was performed to depict the optimum performance of the COAER-UAVC *** experimental results verified that the COAER-UAVC technique had assured improved performance over other approaches.

关键词： Artificial intelligence unmanned aerial vehicle data communication routing protocol energy efficiency

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Improving Data Transmission Efficiency in UAV Networks Through Adaptive Coding

Improving Data Transmission Efficiency in UAV Networks Throu...

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International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Majd M. Safadi Vladislav B. Amoursky Mathematical and Software of Computing Systems Complexes and Computer Networks Faculty of Information Science and Control Systems Bauman Moscow State Technical University Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

In recent years, unmanned aerial vehicles (UAV) have become widely used in civil and military applications such as surveillance, data collection, and cargo delivery. With the increasing use of this technology, there is a need for improved communication systems to ensure reliable and efficient data transmission. UAV networks face unique challenges due to dynamic motion, environmental interference, and energy and bandwidth limitations. The objective of this study is to propose an adaptive coding scheme using Rate-Compatible Punctured Coding (RCPC) technology, which assigns different coding rates to different data depending on their importance and channel conditions. The proposed scheme includes a mechanism to dynamically adjust the coding rates based on the change in link quality. This study demonstrates that adaptive coding can improve resource efficiency, reduce latency, and enhance communication reliability.

关键词： Surveillance Interference Autonomous aerial vehicles Encoding Reliability Data communication Time factors Vehicle dynamics Adaptive coding Signal to noise ratio

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