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166 results for “Network Layer”
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Research ArticleOpen accessIn the abstractEvaluation of Surface Drag Interval in the Variable SituationsParisa Ashrafi, Amir Hossein Ashrafi, Hossein Gholami et al.Tools applied in different fields of engineering are artificial neural networks to model non-linear systems which are efficient and useful. Artificial neural networks consist of input and output layers with one or more hidden layers between them. In fact, one or more processing elements or neurons are used in each layer. Input layer neurons are the independent variables studied and … …American Journal of Engineering and Applied Sciences, Vol. 15, Issue 1 · 12 Mar 2022 · 6,001 accessesView PDFCite
Research ArticleOpen accessIn the abstractOptimization of Multi-Layer Perceptron Deep Neural Networks using Genetic Algorithms for Hand Gesture RecognitionKhanh Nguyen-Trong, Thi-Thanh-Tan NguyenApplications of wearable sensors for Hand Gesture Recognition (HGR) have been gaining popularity in recent years. Among the proposed methods, deep neural networks with many hidden layers are promising to address the requirements of this wearable activity recognition. They can directly uncover features tied to the dynamics of HGR, from simple motion encoding in lower layers to more complex motion … …Journal of Computer Science, Vol. 18, Issue 2 · 8 Feb 2022 · 7,180 accessesView PDFCite
Research ArticleOpen accessIn the abstractEnhancing Kidney Stone Detection Accuracy Using Image Processing and a Hybrid Convolutional Deep Belief NetworkGeetha M. N, Barkathulla A, S. Nandhini Devi et al.Automated CKD is a rapidly expanding non-communicable ailment that significantly contributes to global mortality and morbidity. CKD impacts more than 10% of the worldwide population, leading to millions of deaths annually. This paper proposes an enhanced deep learning model for renal disease detection that integrates convolutional and involution layers to enhance classification accuracy. CKD is a worldwide health issue characterised … …Journal of Computer Science, Vol. 22, Issue 10 · 9 Oct 2026 · 749 accessesView PDFCite
Research ArticleOpen accessIn the abstractCardiovascular Disease Prediction using Various Machine Learning AlgorithmsDebabrata Swain, Badal Parmar, Hansal Shah et al.Almostone-third of all deaths caused around the world were caused due tocardiovascular diseases. Even if death was not the result, much cost isincurred during the treatment of such diseases. But much of these deaths andtreatments could have been prevented with prior action. Advance knowledge ofthe symptoms and consequently proper care can lead us to avoid such diseases.Thus, current research proposes … …Journal of Computer Science, Vol. 18, Issue 10 · 13 Oct 2022 · 9,394 accessesView PDFCite
Research ArticleOpen accessIn the abstractSmart and Secure Anomaly Detection in IoT Using Quantum Inspired Kernel Ensemble LearningAbrar M. AlajlanThe exponential growth of the Internet of Things networks has greatly increased the attack surface for cyber threats, necessitating the use of strong and perceptive anomaly detection systems. Traditional intrusion detection systems often struggle with high false alarm rates, limited generalization, and inefficiency in processing high-dimensional heterogeneous IoT data. To address this issue, a novel Hybrid Quantum-Enhanced Kernel Ensemble Learning … …Journal of Computer Science, Vol. 22, Issue 3 · 12 Mar 2026 · 1,762 accessesView PDFCite
Research ArticleOpen accessIn the abstractFault-Tolerant Control of a Nonlinear Uncertain System: A Neural Network-Based Passive Approaches and Comparative Study with State-of-the-Art Control ApproachesSejal K. Raval, Himanshukumar Rajendrabhai Patel, Vipul A. ShahThis article suggests passive methods for designing Fault-Tolerant Control (FTC) for nonlinear uncertain systems with actuator and leak faults. To anticipate the Fault-Tolerant Control (FTC) action to overcome the actuator and leak faults, two-layer Feed-Forward Back-Propagation Neural Network (FFBPNN) and two-layer Cascade Forward Neural Network (CFNN) have been used, it will also tolerate external process additive disturbances. We employ the … …Journal of Computer Science, Vol. 17, Issue 7 · 3 Aug 2021 · 6,736 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Content Filtering from Spam Posts on Social Media using Weighted Multimodal ApproachChastine Fatichah, Wildan F. Lazuardi, Dini A. Navastara et al.The system for filtering spam posts on social media is preferred to obtain the relevant content and expected by users. The previous works on spam detection have been done to filter irrelevant content on email and social media based on text or image separately. Due to the social media posts are commonly in the form of image, text, or both, … …Journal of Computer Science, Vol. 17, Issue 1 · 21 Jan 2021 · 7,904 accessesView PDFCite
Research ArticleOpen accessIn the abstractFine-Tuned MobileNet Classifier for Classification of Strawberry and Cherry Fruit TypesVenkatesh, Nagaraju Yallappa, Siddhanth Udayashankar Hegde, Sangeetha Raj StalinThis paper proposed an accurate, fast and reliable strawberry, cherry fruit detection and classification system for the automated strawberry cherry yield estimation. State-of-the-art deep learning-based fine-tuned MobileNet Convolutional Neural Network is developed to detect and classify strawberry and cherry fruit types in the outdoor field. The proposed CNN model is trained on 4250 strawberry fruit images, 3878 Cherry fruit images … …Journal of Computer Science, Vol. 17, Issue 1 · 20 Jan 2021 · 7,953 accessesView PDFCite
Research ArticleOpen accessIn the abstractAdvanced Hierarchical Deep Learning Approach for Intrusion Detection: Hyperparameter Tuning and Performance Evaluation on the CICIDS DatasetAjeet Singh, Azath M, Shahazad Niwazi Qurashi et al.Intrusion Detection Systems (IDS) are essential in the communication security and integrity of contemporary network architectures. This study introduces a novel advanced hierarchical deep learning model for network intrusion detection using the CICIDS dataset-2019. To improve the detection accuracy, the proposed model architecture builds several deep learning layers embedded with hyperparameters' optimization. Additional preprocessing steps, including over-sampling and min-max scaling, … …Journal of Computer Science, Vol. 21, Issue 11 · 26 Dec 2025 · 2,148 accessesView PDFCite
Research ArticleOpen accessIn the abstractEnhancing Fleet Management with GPS Data in Port Terminals: A Deep Learning Architecture for Truck Position PredictionMohammed El Karkraoui, Hicham AttariuasEffective truck fleet management in port terminals is crucial for improving operational efficiency and reducing delays. However, GPS-based positioning systems often face limitations in port environments due to signal obstructions and network issues. This study explores the utilization of advanced methods for deep learning, particularly Recurrent Neural Networks (RNNs) for predicting truck positions when GPS signals are unavailable or unreliable. … …Journal of Computer Science, Vol. 21, Issue 10 · 16 Dec 2025 · 2,271 accessesView PDFCite