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2024 IEEE Global Conference on Artificial Intelligence and Internet of Things, GCAIoT 2024

作者： Salem, Sameh A. Said, Samar A. Nour, Samar M. Department of Computer and Systems Engineering Faculty of Engineering Helwan University Cairo Egypt Department of Computer and Systems Engineering Faculty of Engineering Helwan University Cairo Egypt Department of Computer and Systems Engineering Faculty of Engineering Badr University Cairo Egypt

ISBN: (纸本)9798331529413

Nowadays, Internet of Things (IoT) become progressively a fundamental part of our life. It revolutionizes various industries by enabling seamless connectivity between devices as well as it increases automation and efficiency. However, the reliability of IoT systems is often compromised due to the complexity and scale of these networks. It makes them vulnerable to failures and security breaches. To mitigate this problem, anomaly detection using artificial Intelligence (AI) in IoT can be a promising candidate to help data identifies unusual patterns that could indicate system faults or threats. In this paper, AI-Driven Anomaly Detection Framework for Enhancing IoT Security is proposed. The proposed framework enhances IoT system reliability by reducing downtime, improving security,and ensuring the consistent performance of connected devices. The experimental results demonstrate the efficiency of machine learning techniques and their capabilities with anomalies to enhance detection capability. Finally, the empirical results show that DT and RF outperform the other competitive models for network intrusion detection in the face of anomaly detection in front of the ongoing. ©2024 IEEE.

关键词： Data reliability

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Early Detection of Colletotrichum Kahawae Disease in Coffee Cherry Based on computer Vision Techniques

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computer Modeling in engineering & Sciences 2024年第4期139卷 759-782页

作者： Raveena Selvanarayanan Surendran Rajendran Youseef Alotaibi Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical ScienceChennai602105India Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia

Colletotrichum kahawae(Coffee Berry Disease)spreads through spores that can be carried by wind,rain,and insects affecting coffee plantations,and causes 80%yield losses and poor-quality coffee *** deadly disease is hard to control because wind,rain,and insects carry *** researchers utilized a deep learning system to identify CBD in coffee cherries at three growth stages and classify photographs of infected and uninfected cherries with 93%accuracy using a random forest *** the dataset is too small and noisy,the algorithm may not learn data patterns and generate accurate *** overcome the existing challenge,early detection of Colletotrichum Kahawae disease in coffee cherries requires automated processes,prompt recognition,and accurate *** proposed methodology selects CBD image datasets through four different stages for training and *** to train a model on datasets of coffee berries,with each image labeled as healthy or *** themodel is trained,SHAP algorithmto figure out which features were essential formaking predictions with the proposed *** of these characteristics were the cherry’s colour,whether it had spots or other damage,and how big the Lesions *** inception is important for classification to virtualize the relationship between the colour of the berry is correlated with the presence of *** evaluate themodel’s performance andmitigate excess fitting,a 10-fold cross-validation approach is *** involves partitioning the dataset into ten subsets,training the model on each subset,and evaluating its *** comparison to other contemporary methodologies,the model put forth achieved an accuracy of 98.56%.

关键词： computer vision coffee berry disease colletotrichum kahawae XG boost shapley additive explanations

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State-of-the-art in human activity recognition based on inertial measurement unit sensors: survey and applications

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International Journal of computers and Applications 2025年第1期47卷 1-16页

作者： Sepahvand, Majid Meqdad, Maytham N. Abdali-Mohammadi, Fardin Department of Computer Engineering Arak University Markazi Iran Intelligent Medical Systems Department Al-Mustaqbal University Babil Hillah Iraq Department of Computer Engineering and Information Technology Razi University Kermanshah Iran

Human activity recognition systems using wearable sensors is an important issue in pervasive computing, which applies to various domains related to healthcare, context aware and pervasive computing, sports, surveillance and monitoring, and the military. Three approaches can be considered for activity recognition: video sensor-based, physical sensor-based, and environmental sensor-based. This paper investigates the related work regarding the physical sensor-based approaches to motion processing. In this paper, a wide range of artificial intelligence models, from single classifications to methods based on deep learning, have been reviewed. The human activity detection accuracy of different methods, under natural and experimental conditions poses several challenges. These challenges cause problems regarding the accuracy and applicability of the proposed methods. This paper analyzes the methods, challenges, approaches, and future work. The goal of this paper is to establish a clear distinction in the field of motion detection using inertial sensors. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Active learning

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A generic optimization and learning framework for Parkinson disease via speech and handwritten records

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Journal of Ambient Intelligence and Humanized Computing 2023年第8期14卷 10673-10693页

作者： Yousif, Nada R. Balaha, Hossam Magdy Haikal, Amira Y. El-Gendy, Eman M. Computer and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt

Parkinson’s disease (PD) is a neurodegenerative disorder with slow progression whose symptoms can be identified at late stages. Early diagnosis and treatment of PD can help to relieve the symptoms and delay progression. However, this is very challenging due to the similarities between the symptoms of PD and other diseases. The current study proposes a generic framework for the diagnosis of PD using handwritten images and (or) speech signals. For the handwriting images, 8 pre-trained convolutional neural networks (CNN) via transfer learning tuned by Aquila Optimizer were trained on the NewHandPD dataset to diagnose PD. For the speech signals, features from the MDVR-KCL dataset are extracted numerically using 16 feature extraction algorithms and fed to 4 different machine learning algorithms tuned by Grid Search algorithm, and graphically using 5 different techniques and fed to the 8 pretrained CNN structures. The authors propose a new technique in extracting the features from the voice dataset based on the segmentation of variable speech-signal-segment-durations, i.e., the use of different durations in the segmentation phase. Using the proposed technique, 5 datasets with 281 numerical features are generated. Results from different experiments are collected and recorded. For the NewHandPD dataset, the best-reported metric is 99.75% using the VGG19 structure. For the MDVR-KCL dataset, the best-reported metrics are 99.94% using the KNN and SVM ML algorithms and the combined numerical features;and 100% using the combined the mel-specgram graphical features and VGG19 structure. These results are better than other state-of-the-art researches. © 2022, The Author(s).

关键词： Feature extraction

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Acoustic modelling using deep learning for Quran recitation assistance

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International Journal of Speech Technology 2023年第1期26卷 113-121页

作者： Ghori, Ammar Farid Waheed, Aisha Waqas, Maria Mehmood, Aqsa Ali, Syed Abbas Department of Computer and Information Systems Engineering NED University of Engineering and Technology Karachi Pakistan

Recent advancements in computer science and engineering have significantly boosted interest in and success rate of speech recognition systems. Sophisticated speech recognition systems are being developed for a variety of languages having wide-spread applications. Arabic being one of the most widely used languages in the world, augmented by religious importance, is a potential candidate in this domain, however rich vocabulary and multiple dialects open up new challenges for development of Arabic speech recognition processes. This paper focuses on development of an Arabic isolated speech recognition system for the vocabulary of Holy Quran using Mel frequency cepstral coefficients and deep neural networks. The proposed system is capable of identifying individual words from a recited verse with reasonable accuracy. It targets the 362 unique words of the first and the last 19 chapters of the Holy Quran and uses 14 hours of audio data to demonstrate the working prototype of the system. A user-friendly, web-based application has also been developed for transcription of recitations. The software is specified to make an effort in spreading the right way of recitation of the Holy Quran. The presented architecture can be extended to a complete automatic speech recognition system to help people practice recitations, mapping speech to transcription and then matching the transcribed utterance with standard templates of correct recitation to identify reciter’s short comings. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Deep neural networks

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Securing Cloud-Encrypted Data:Detecting Ransomware-as-a-Service(RaaS)Attacks through Deep Learning Ensemble

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computers, Materials & Continua 2024年第4期79卷 857-873页

作者： Amardeep Singh Hamad Ali Abosaq Saad Arif Zohaib Mushtaq Muhammad Irfan Ghulam Abbas Arshad Ali Alanoud Al Mazroa School of Computer Information Sciences University of CumberlandsWilliamsburgKentucky40769USA Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Electrical Engineering The University of LahoreLahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware *** study provides a new approach for RaaS attack detection which uses an ensemble of deep learning *** this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is *** the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are ***,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested *** proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%*** empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual *** expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.

关键词： Cloud encryption RaaS ensemble threat detection deep learning cybersecurity

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Smoke Removal Method of Industrial Images Based on Dark Channel Prior Approach and Second-Generation Wavelets

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SN computer Science 2024年第7期5卷 843页

作者： Khmag, Asem Department of Computer Systems Engineering Faculty of Engineering University of Zawia Zawia Libya

In the field of digital image and computer vision, haze and smoke removal (dehazing) is one pf a popular scientific arena where it is being studied by an ample number of computer scientists. However, conventional joint dehazing and noise removal techniques use traditional hand-crafted approaches in order to achieve clean image by apply suitable restoration processes. In this study, a smoke removal technique, which designed according to dark channel, prior DCP is proposed. Theoretically, traditional DCP utilized the atmospheric light in the entire image as a global constant and as a result, in our study, the suggested DCP is further enhanced by using filter of second-generation wavelets (SGWs) and therefore, more powerful model is achieved. Consequently, the SGWs filtering is utilized due to its strength in custom designing for complex domains and irregular sampling in order to improve the solution of atmospheric light. The proposed algorithm is evaluated against several state of the art smoke removal methods. Extensive experimentation on multiple datasets demonstrates that our method exhibits better dehazing performance and generalizability than other image dehazing approaches in terms of peak signal-to-noise ratio PSNR, the structural similarity index SSIM and the average gradient results. Moreover, the execution efficiency of the proposed algorithm can reduce considerably the processing time in comparison with the state-of-the-art- smoke removal techniques. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Atmospheric light Dark channel prior Dehazing Patch size Second-generation wavelet Smoke removal

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Synthetic photoplethysmogram (PPG) signal generation using a genetic programming-based generative model

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Journal of Medical engineering and Technology 2024年第6期48卷 223-235页

作者： Ghasemi, Fatemeh Sepahvand, Majid Meqdad, Maytham N. Abdali Mohammadi, Fardin Department of Computer Engineering and Information Technology Razi University Kermanshah Iran Department of Computer Engineering Arak University Markazi Iran Intelligent Medical Systems Department Al-Mustaqbal University Babil Iraq

Nowadays, photoplethysmograph (PPG) technology is being used more often in smart devices and mobile phones due to advancements in information and communication technology in the health field, particularly in monitoring cardiac activities. Developing generative models to generate synthetic PPG signals requires overcoming challenges like data diversity and limited data available for training deep learning models. This paper proposes a generative model by adopting a genetic programming (GP) approach to generate increasingly diversified and accurate data using an initial PPG signal sample. Unlike conventional regression, the GP approach automatically determines the structure and combinations of a mathematical model. Given that mean square error (MSE) of 0.0001, root mean square error (RMSE) of 0.01, and correlation coefficient of 0.999, the proposed approach outperformed other approaches and proved effective in terms of efficiency and applicability in resource-constrained environments. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Generative adversarial networks

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Deep Learning Performance Evaluation Model for Enhancing Network Intrusion Detection systems 6

Deep Learning Performance Evaluation Model for Enhancing Net...

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6th IEEE Novel Intelligent and Leading Emerging Sciences Conference, NILES 2024

作者： Nour, Samar M. Said, Samar A. Badr University Faculty of Engineering Department of Computer and Systems Engineering Cairo Egypt Helwan University Faculty of Engineering Department of Computer and Systems Engineering Cairo Egypt

ISBN: (纸本)9798350378511

Network intrusion detection systems (NIDSs) play an important role in protecting network infrastructure from cyber threats. Traditional NIDS often rely on signature-based or rule-based methods, which can contention to detect incoming strong attacks. Deep learning techniques have emerged as a promising method for improving NIDS due to their ability to learn complex structures and features from raw network traffic data. In this paper, a performance evaluation model for enhancing network intrusion detection is proposed. Deep learning methods including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Attention Mechanisms, and Deep Neural Networks are investigated in the proposed model. Furthermore, the proposed model discusses the challenges and opportunities associated with the implementation of deep learning-based NIDS. These challenges include scalability, interpretability, and adversarial attacks. The performance of the proposed model is examined across numerous datasets that are real-world. The experimental results prove the efficiency to deep learning techniques and their capabilities with NIDS for enhancing the detection capability. Finally, the empirical results show that CNN outperforms the other competitive models for network intrusion detection in the face of cyber threats and in front of the ongoing. © 2024 IEEE.

关键词： Network intrusion

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A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2220-2235页

作者： Jiufang Chen Kechen Liu Xin Luo Ye Yuan Khaled Sedraoui Yusuf Al-Turki MengChu Zhou IEEE the College of Computer Science and Technology Chongqing University of Posts and Telecommunications the Department of Computer Science Columbia University the College of Computer and Information Science Southwest University the Department of Electrical and Computer Engineering Faculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Center of Research Excellence in Renewable Energy and Power Systems the Department of Electrical and Computer EngineeringFaculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Department of Electrical and Computer Engineering New Jersey Institute of Technology

High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation learning to an HDI matrix,whose hyper-parameter adaptation can be implemented through a particle swarm optimizer(PSO) to meet scalable ***, conventional PSO is limited by its premature issues,which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer(SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational ***, SPSO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.

关键词： Data science generalized momentum high-dimensional and incomplete(HDI)data hyper-parameter adaptation latent factor analysis(LFA) particle swarm optimization(PSO)

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