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2nd International Conference on Electronics and Renewable systems, ICEARS 2023

作者： Nayak, Chhaya Rao, Chintala Lakshmana Alam, Tanweer Singh, Shalini Islam, Shaziya Maginmani, Umme Habiba Pimpri Chinchwad College of Engineering Pune India School of Law Visakhapatnam India Islamic University of Madinah Faculty of Computer and Information Systems Department of Computer Science Madinah Saudi Arabia Graphic Era Deemed to Be University Department of Ece Uttarakhand Dehradun248002 India Rungta College of Engineering and Technology Bhilai India Secab Institute of Engineering and Technology Department of Computer Science and Engineering Karnataka Vijayapur India

ISBN: (纸本)9798350346640

Artificial intelligence (AI) technologies have been recently developed, and their advantages can be observed in a wide range of domains, from image processing to face detection. AI-based approaches can assist opponents to enhance their attack strategies while also improving cyber protection technologies. The efficiency of AI techniques against cyber security issues are evaluated. Here, the primary data and a quantitative study design approach is used. The information is then obtained from software industry professionals. The sample size for this study is 549 people, and it included confirmatory factor analysis, discriminant validity testing, model fundamental analysis, and hypothesis evaluation. All variables' P-values were found to be statistical, with the exception of intelligent agents, which had no meaningful relationship to AI or cyber security. The main issues were availability, geographical locations, study population, and other related variables. © 2023 IEEE.

关键词： Factor analysis

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Determining the Critical Factors of Antenatal Care Follow-Up Using Two-Phase Machine Learning

Determining the Critical Factors of Antenatal Care Follow-Up...

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International Conference on Soft Computing and Measurements (SCM)

作者： Igor A. Bessmertny Bidru C. Enkomaryam Adane L. Mamuye Faculty of Software Engineering and Computer Systems ITMO University Saint Petersburg Russia Department of Computer Science University of Gondar Gondar Ethiopia

ISBN: (数字)9798350363708

ISBN: (纸本)9798350363715

Determining the critical factors affecting antenatal visits will greatly contribute to reducing maternal and infant mortality. This study, thus attempted to construct a cluster-based predictive model to determine the critical factors of antenatal care follow-up among women in Ethiopia using two-step machine learning techniques. The 5,990 preprocessed obstetric dataset with 13 selected attributes were used that was collected from the Dabat Research Center at the University of Gondar, Ethiopia. To solve the study problem, six cluster models were developed using K-means, Expectation Maximization and Make Density clustering algorithms, which are well-suited for analyzing health datasets, providing scalability, flexibility, robustness to noise, and the ability to handle uneven cluster sizes. K-means out-formers Density based and Expectation Maximization clustering algorithms with minimum number of sum of square errors (17094) and number of iteration (3). Then, output of the K-means cluster model was used as input to segment the dataset and build a total of 21 predictive models using the tree-based J48 classifier, PART and JRip rule generators while adjusting their parameters. The model for predicting antenatal care follow-up is constructed by induction of the truncated PART rule (accuracy of 99.93%) and extracts attributes of interest such as antenatal care attendance, antenatal care location, antenatal care frequency, current vaccination, woman’s age, and HIV testing to predict antenatal care follow-up in the study area.

关键词： Machine learning algorithms Accuracy Scalability Clustering algorithms Machine learning Predictive models Prediction algorithms

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Synthesis, Analysis and Implementation of Partial Counters Using Integrated Circuits 32

Synthesis, Analysis and Implementation of Partial Counters U...

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32nd International Scientific Conference Electronics, ET 2023

作者： Borodzhieva, Adriana N. Pandzhar, Atila G. Nedelchev, Martin N. Genov, Nikolay G. University of Ruse "Angel Kanchev" Faculty of Electrical Engineering Electronics and Automation Department of Telecommunications 8 Studentska Street Ruse7017 Bulgaria University of Ruse "Angel Kanchev" Faculty of Electrical Engineering Electronics and Automation Department of Computer Systems and Technologies 8 Studentska Street Ruse7017 Bulgaria

ISBN: (纸本)9798350302004

The paper presents the processes of synthesis, analysis, and implementation of counters and frequency dividers using integrated circuits from the 74xx library. All the synthesized circuits are built and tested in Logisim. The work is related to the Teamwork Project in the course "Digital Circuits"for students-bachelors of the "computer systems and Technologies"specialty at the University of Ruse. © 2023 IEEE.

关键词： Flip flop circuits

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Machine Learning Approaches in Cybersecurity to Enhance Security in Future Network Technologies

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SN computer Science 2025年第4期6卷 1-18页

作者： Ahmad, Sultan Haque, Md. Alimul Abdeljaber, Hikmat A. M. Eljialy, A.E.M. Nazeer, Jabeen Mishra, B.K. Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Mohali Punjab Gharuan 140413 India Department of Computer Science Veer Kunwar Singh University Bihar Ara India Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Information Systems College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia Department of Physics Veer Kunwar Singh University Ara 802301 India

As technology continues to evolve rapidly, cybersecurity has become a critical global concern. The increasing sophistication of cyber threats poses significant risks to individuals, businesses, and governments. To combat these threats, cybersecurity tools play a crucial role in monitoring, detecting, and mitigating security risks in digital environments. These tools ensure data protection, prevent unauthorized access, and safeguard sensitive information. However, traditional cybersecurity approaches are struggling to keep pace with emerging cyber-attacks. In response, machine learning (ML) has emerged as a powerful solution for enhancing cybersecurity strategies. ML algorithms enable organizations to analyze large datasets, identify anomalies, and predict potential threats with greater accuracy. Cybersecurity tools, integrated with ML, act as the final line of defense against attacks such as data breaches, identity theft, and system intrusions. This research explores the application of ML in selecting and optimizing cybersecurity models for enterprise ICT systems. It also emphasizes the growing demand for skilled professionals who can develop and implement ML-based security solutions. By examining current trends and future possibilities, this study provides valuable insights into the role of ML in strengthening cybersecurity measures and enhancing overall digital protection for organizations worldwide. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cyber security Cybercrime Data analysis ML algorithms Network security Threats

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Energy-Saving VM in SDN-Driven Cloud Data Center with the Operational Vacation Policy 3

Energy-Saving VM in SDN-Driven Cloud Data Center with the Op...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar Barik, Lalbihari Ghosh, Akash Pandey, Trilok Nath Gourisaria, Mahendra Kumar KIIT Deemed to Be University School of Computer Applications Bhubaneswar India King Abdul Aziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Saudi Arabia Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343632

The increasing use of the Internet of Things (IoT), which has resulted in an exponential growth in network traffic, has serious implications on energy consumption and network performance. To reduce power consumption in cloud data centers, a server vacation-based energy-efficient traffic management solution is recommended. The primary idea is that using software-defined network (SDN) technologies, cloud system virtual machines (VMs) can take vacations. The goal of the energy-saving optimization task is to reduce the number of virtual machines (VMs) in use while maintaining service quality standards. This research study focuses on impatient client behaviour, several vacations, limited buffer and queuing system with many servers. Impatient customers may be kept in the cloud system by implementing certain customer retention strategies. The steady-state probability for the proposed SDN-assisted cloud system have been determined using recurrences. To determine the efficiency of the proposed system, several performance studies are conducted. © 2023 IEEE.

关键词： Cloud data center Energy optimization Performance measurement Queueing model Software Defined Network Vacation policy

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Performance Evaluation of Popular Deep Neural Networks for Neural Machine Translation 20

Performance Evaluation of Popular Deep Neural Networks for N...

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20th International Conference on Frontiers of Information Technology, FIT 2023

作者： Naeem, Muhammad Siddique, Abu Bakar Ali, Raja Hashim Arshad, Usama Abideen, Zain Ul Ali Khan, Talha Shah, Muhammad Huzaifa Zeeshan Ijaz, Ali Ali, Nisar Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350395785

The field of Neural Machine Translation (NMT) has shown impressive performance for quick and easy communication in various languages spoken all over the world. NMT helps us by improving communication between different languages. For this purpose, different sequential models are used such as Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Units (GRU). Analysis among these different models are important for making language translation better and choose the best model for the right job. This research investigates the performance of these models on two distinct language datasets, English-to-German and English-to-Urdu. Based on accuracy metrics, the findings reveal that GRU having test accuracy (88.22% ) outperforms RNN (87.21% ), and LSTM (85.70% )demonstrating the highest translation accuracy, followed by RNN and LSTM exhibiting comparatively lower accuracy levels. © 2023 IEEE.

关键词： Long short-term memory

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Mitigating Crop Losses: AI-enabled Disease Detection in Tomato Plants 20

Mitigating Crop Losses: AI-enabled Disease Detection in Toma...

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20th International Conference on Frontiers of Information Technology, FIT 2023

作者： Ashfaq, Hassan Arshad, Usama Ali, Raja Hashim Ul Abideen, Zain Shah, Muhammad Huzaifa Ali Khan, Talha Ijaz, Ali Zeeshan Ali, Nisar Siddique, Abu Bakar Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350395785

Fostering crop health is vital for global food security, underscoring the need for effective disease detection. This research introduces an innovative artificial intelligence (AI) model designed to enhance the detection and diagnosis of diseases in tomato plants, particularly focusing on Early Blight and Late Blight. Significantly, our model leverages cutting-edge image processing techniques to improve disease detection efficiency, outperforming traditional methods in terms of speed and accuracy. Our results demonstrate an impressive model accuracy of 92.58% on training data and 86.83% on validation data, showing the effectiveness of AI in diagnosing plant diseases. These high accuracy rates underline the potential of our model for timely disease classification, allowing for immediate and appropriate interventions. However, our research also identified a potential overfitting problem in the model's performance. To address this, we propose using regularization and data augmentation techniques to enhance the model's generalizability on unseen data. Additionally, we delve into inherent challenges that plague AI-based plant disease detection, such as the scarcity of diverse datasets and the difficulty of achieving broad generalizability across different plant species. In identifying potential solutions for these issues, our research lays the groundwork for the wider and more practical implementation of AI technologies in agriculture. © 2023 IEEE.

关键词： Crops

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Biomedical Osteosarcoma Image Classification Using Elephant Herd Optimization and Deep Learning

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computers, Materials & Continua 2022年第12期73卷 6443-6459页

作者： Areej A.Malibari Jaber S.Alzahrani Marwa Obayya Noha Negm Mohammed Abdullah Al-Hagery Ahmed S.Salama Anwer Mustafa Hilal Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Science Mathematics and Computer Science DepartmentMenoufia UniversityEgypt Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

Osteosarcoma is a type of malignant bone tumor that is reported across the *** advancements in Machine Learning(ML)and Deep Learning(DL)models enable the detection and classification of malignancies in biomedical *** this regard,the current study introduces a new Biomedical Osteosarcoma Image Classification using Elephant Herd Optimization and Deep Transfer Learning(BOIC-EHODTL)*** presented BOIC-EHODTL model examines the biomedical images to diagnose distinct kinds of *** the initial stage,Gabor Filter(GF)is applied as a pre-processing technique to get rid of the noise from *** addition,Adam optimizer with MixNet model is also employed as a feature extraction technique to generate feature ***,EHOalgorithm is utilized along with Adaptive Neuro-Fuzzy Classifier(ANFC)model for recognition and categorization of *** algorithm is utilized to fine-tune the parameters involved in ANFC model which in turn helps in accomplishing improved classification *** design of EHO with ANFC model for classification of osteosarcoma is the novelty of current *** order to demonstrate the improved performance of BOIC-EHODTL model,a comprehensive comparison was conducted between the proposed and existing models upon benchmark dataset and the results confirmed the better performance of BOIC-EHODTL model over recent methodologies.

关键词： Biomedical imaging osteosarcoma classification deep transfer learning parameter tuning fuzzy logic

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Automated Wood Surface Defects Recognition System Using Yolov4-tiny Model

Automated Wood Surface Defects Recognition System Using Yolo...

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Musa, Ahmad Muaz bin Momin, Md. Abdul Khairuddin, Anis Salwa Mohd Khairuddin, Uswah Ahmad, Azlin Rosli, Nenny Ruthfalydia Department of Electrical Engineering Faculty of Engineering Universiti Malaya Kuala Lumpur Malaysia Department of Mechanical Precision Engineering MJIIT Universiti Teknologi Malaysia Kuala Lumpur Malaysia Centre of Information Systems Studies Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Selangor Malaysia Universiti Teknologi Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9798350325188

Wood surface detection is a process of identifying and locating wooden surfaces in an image or video using computer vision techniques. This technique can be used in various applications such as furniture manufacturing, construction, and lumber mills. Wood defect detection is an important task in the wood industry as it ensures the quality of wood products. In this study, YOLOv4-tiny algorithm is proposed to detect seven types of wood defects from wood texture images. Analysis was performed by applying several detection models to detect the wood defects. The performance of the proposed model is evaluated in terms of precision, recall, F1-score, and mAP. The algorithm has the potential to improve the efficiency and quality of the wood industry and could be applied to other similar tasks, such as identifying defects in other materials and products. © 2023 IEEE.

关键词： CNN deep learning defects wood images

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Online DVFS Scheduling Algorithm for Sporadic Tasks with Energy-Conscious in Hard Real-Time systems 16

Online DVFS Scheduling Algorithm for Sporadic Tasks with Ene...

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16th International Conference on computer engineering and systems, ICCES 2021

作者： El Sayed, Manal A. El Sayed, M. Saad Habashy, Shahira M. Aly, Rasha F. Helwan University Faculty of Engineering Department of Computer and Systems Cairo Egypt

ISBN: (纸本)9781665408677

Dynamic voltage frequency scaling (DVFS) has been an efficient technology in minimizing the energy consumption of real-time embedded systems. Many prior studies have attempted to balance performance and energy consumption through energy-efficient task scheduling using the DVFS approach. This paper presents a novel DVFS algorithm, named Energy Conscious-DVFS (EC-DVFS) that can be used with sporadic constrained-deadline hard real-time tasks implemented upon multicore platforms, where the scheduler is the Earliest Deadline First (EDF) algorithm. The EC-DVFS technique is an adaptive online speed determination mechanism that determines the safe speed for each released task firstly. That speed ensures all deadlines are achieved when the jobs are scheduled using EDF. Then, when the task is assigned to a core, the EC-DVFS adjusts the core speed online according to its actual workload by considering both current and future task load using runtime queues. We demonstrate the efficacy of the suggested approach through an extensive set of experiments. The experimental simulation results demonstrate that the suggested algorithm saves a significant amount of energy when the system is running across a variety of applications. © 2021 IEEE.

关键词： Scheduling

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