00373nas a2200133   4500000000100000008004100001100001800042700001700060700002000077700001900097700001900116245009700135490000700232       2024                            d1 aMohamed Yamni1 aAchraf Daoui1 aPaweł Pławiak1 aOsama Alfarraj1 aAhmed El-Latif00aA Hardware-Accelerated Approach to Chaotic Image Encryption: LTB Map and FPGA Implementation0 v1200376nas a2200121   4500000000100000008004100001100001500042700001500057700002700072700002000099245012800119490000700247       2024                            d1 aMengfan Li1 aJian Zhang1 aRoohallah Alizadehsani1 aPaweł Pławiak00aA Multi-Channel Advertising Budget Allocation Using Reinforcement Learning and an Improved Differential Evolution Algorithm0 v1200379nas a2200133   4500000000100000008004100001100001700042700001800059700002000077700001900097700001900116245010300135490000700238       2024                            d1 aAchraf Daoui1 aMohamed Yamni1 aPaweł Pławiak1 aOsama Alfarraj1 aAhmed El-Latif00aA New Chaotic Memristor-Based Cryptosystem for Secure Bio-Signal Transmission on Low-Cost Hardware0 v1200438nas a2200157   4500000000100000008004100001100001300042700001700055700001600072700001500088700001500103700002700118700002000145245010800165490000700273       2024                            d1 aLiu Yang1 aJiang Guofan1 aZhang Yixin1 aWei Qianze1 aZhang Jian1 aRoohallah Alizadehsani1 aPaweł Pławiak00aA Reinforcement Learning Approach Combined With Scope Loss Function for Crime Prediction on Twitter (X)0 v1200417nas a2200145   4500000000100000008004100001100002000042700001900062700002000081700001800101700002000119700001900139245010600158490000700264       2024                            d1 aIsmail Lamaakal1 aIbrahim Ouahbi1 aKhalid Makkaoui1 aYassine Maleh1 aPaweł Pławiak1 aFahad Alblehai00aA TinyDL Model for Gesture-Based Air Handwriting Arabic Numbers and Simple Arabic Letters Recognition0 v1200500nas a2200169   4500000000100000008004100001100002100042700001400063700002400077700003000101700002000131700002000151700002700171700002000198245010500218490000700323       2024                            d1 aMahmood Sotoodeh1 aAli Kohan1 aMohamad Roshanzamir1 aSenthil Jagatheesaperumal1 aMohammad Qazani1 aJavad Joloudari1 aRoohallah Alizadehsani1 aPaweł Pławiak00aComparison Between State-of-the-Art Color Local Binary Pattern-Based Descriptors for Image Retrieval0 v1202471nas a2200169   4500000000100000008004100001100002000042700001400062700001900076700002500095700002000120245013900140856007200279490000700351520192900358022001402287       2024                            d1 aSoner Kiziloluk1 aEser Sert1 aMohamed Hammad1 aRyszard Tadeusiewicz1 aPaweł Pławiak00aEO-CNN: Equilibrium Optimization-Based hyperparameter tuning for enhanced pneumonia and COVID-19 detection using AlexNet and DarkNet19  uhttps://www.sciencedirect.com/science/article/pii/S02085216240004700 v443 aConvolutional neural networks (CNN) have been increasingly popular in image categorization in recent years. Hyperparameter optimization is a critical stage in enhancing the effectiveness of CNNs and achieving better results. Properly tuning hyperparameters allows the model to exhibit improved performance and facilitates faster learning. Misconfigured hyperparameters can prolong the training time or lead to the model not learning at all. Manually tuning hyperparameters is a time-consuming and challenging process. Automatically adjusting hyperparameters helps save time and resources. This study aims to propose an approach that shows higher classification performance than unoptimized convolutional neural network models, even at low epoch values, by automatically optimizing the hyperparameters of AlexNet and DarkNet19 with equilibrium optimization, the newest metaheuristic algorithm. In this respect, the proposed approach optimizes the number and size of filters in the first five convolutional layers in AlexNet and DarkNet19 using an equilibrium optimization algorithm. To evaluate the efficacy of the suggested method, experimental analyses were conducted on the pneumonia and COVID-19 datasets. An important advantage of this approach is its ability to accurately classify medical images. The testing process suggests that utilizing the proposed approach to optimize hyperparameters for AlexNet and DarkNet19 led to a 7% and 4.07% improvement, respectively, in image classification accuracy compared to non-optimized versions of the same networks. Furthermore, the approach displayed superior classification performance even in a few epochs compared to AlexNet, ShuffleNet, DarkNet19, GoogleNet, MobileNet-V2, VGG-16, VGG-19, ResNet18, and Inceptionv3. As a result, automatic tuning of the hyperparameters of AlexNet and DarkNet-19 with EO enabled the performance of these two models to increase significantly.  a0208-521600484nas a2200181   4500000000100000008004100001100001900042700001600061700001600077700001600093700001800109700001800127700001800145700002000163700001900183245009400202490000600296       2024                            d1 aFarhad Banoori1 aNouman Ijaz1 aJinglun Shi1 aKhalid Khan1 aXiongying Liu1 aSadique Ahmad1 aAllam Prakash1 aPaweł Pławiak1 aMohamed Hammad00aFew-Shot Bioacoustics Event Detection Using Transductive Inference With Data Augmentation0 v800420nas a2200133   4500000000100000008004100001100002100042700001900063700002000082700002000102700002700122245013000149490000700279       2024                            d1 aMahdieh Moghadam1 aZabih Moghadam1 aMohammad Qazani1 aPaweł Pławiak1 aRoohallah Alizadehsani00aImpact of Artificial Intelligence in Nursing for Geriatric Clinical Care for Chronic Diseases: A Systematic Literature Review0 v1201710nas a2200205   4500000000100000008004100001100002300042700002000065700001800085700002000103700001900123700002000142700001900162700001900181245008900200856004600289490000700335520114800342022001401490       2024                            d1 aMohamed Fartitchou1 aIsmail Lamaakal1 aYassine Maleh1 aKhalid Makkaoui1 aZakaria Allali1 aPaweł Pławiak1 aFahad Alblehai1 aAhmed El-Latif00aIOTASDN: IOTA 2.0 Smart Contracts for Securing Software-Defined Networking Ecosystem  uhttps://www.mdpi.com/1424-8220/24/17/57160 v243 aSoftware-Defined Networking (SDN) has revolutionized network management by providing unprecedented flexibility, control, and efficiency. However, its centralized architecture introduces critical security vulnerabilities. This paper introduces a novel approach to securing SDN environments using IOTA 2.0 smart contracts. The proposed system utilizes the IOTA Tangle, a directed acyclic graph (DAG) structure, to improve scalability and efficiency while eliminating transaction fees and reducing energy consumption. We introduce three smart contracts: Authority, Access Control, and DoS Detector, to ensure trusted and secure network operations, prevent unauthorized access, maintain the integrity of control data, and mitigate denial-of-service attacks. Through comprehensive simulations using Mininet and the ShimmerEVM IOTA Test Network, we demonstrate the efficacy of our approach in enhancing SDN security. Our findings highlight the potential of IOTA 2.0 smart contracts to provide a robust, decentralized solution for securing SDN environments, paving the way for the further integration of blockchain technologies in network management.  a1424-822000412nas a2200145   4500000000100000008004100001100001600042700002000058700002200078700002000100700001800120700002100138245010000159490000700259       2024                            d1 aFurqan Alam1 aPaweł Pławiak1 aAhmed Almaghthawi1 aMohammad Qazani1 aSanat Mohanty1 aand Alizadehsani00aNeuroHAR: A Neuroevolutionary Method for Human Activity Recognition (HAR) for Health Monitoring0 v1200401nas a2200133   4500000000100000008004100001100001700042700001800059700002000077700001900097700001900116245012500135490000700260       2024                            d1 aAchraf Daoui1 aMohamed Yamni1 aPaweł Pławiak1 aFahad Alblehai1 aAhmed El-Latif00aNovel Integer Hahn Transform for Real-Time Lossless Image Authentication in IoT: A 32-Bit Microcontroller Implementation0 v1200369nas a2200133   4500000000100000008004100001100001800042700001700060700002000077700001900097700001900116245009300135490000700228       2024                            d1 aMohamed Yamni1 aAchraf Daoui1 aPaweł Pławiak1 aOsama Alfarraj1 aAhmed El-Latif00aNovel Integer Reversible Charlier Transform for Image Reversible Data Hiding Application0 v1202119nas a2200241   4500000000100000008004100001100002000042700002000062700001800082700001600100700001600116700001700132700001700149700001700166700002500183700002400208700002000232245012800252856007200380490000800452520140300460022001401863       2024                            d1 aPradipta Sasmal1 aVanshali Sharma1 aAllam Prakash1 aM.K. Bhuyan1 aKiran Patro1 aNagwan Samee1 aHayam Alamro1 aYuji Iwahori1 aRyszard Tadeusiewicz1 aU. Rajendra Acharya1 aPaweł Pławiak00aSemi-supervised generative adversarial networks for improved colorectal polyp classification using histopathological images  uhttps://www.sciencedirect.com/science/article/pii/S00200255230161950 v6583 aEarly and accurate detection of dysplasia in colorectal polyps can improve prognosis and increase survival chances. Recently, automated learning-based approaches using histopathological images have been adopted for improved classification of polyps. The supervised learning approaches do not provide a reliable classification performance due to limited annotated samples. But, in unsupervised learning, some hidden features are extracted from the unlabeled data which may not be effective in discriminating the complex patterns of the dataset. A generative adversarial network (GAN) is proposed in this work based on a semi-supervised framework for colorectal polyp classification using histopathological images. Our framework learns the discriminating features in an adversarial manner from the limited labeled and huge unlabeled data. In the supervised mode, the discriminator of the proposed model is trained to classify the real histopathological images, whereas, in the unsupervised mode, it tries to discriminate between real and fake images, similar to the classical GAN network. By training in unsupervised mode, the discriminator can identify and extract the subtle features from unlabeled images, to develop a generalized robust model. Our technique yielded classification accuracies of 87.50% and 76.25% using 25% and 50% majority voting schemes, respectively, on the UniToPatho dataset.  a0020-025500447nas a2200157   4500000000100000008004100001100002200042700002000064700001800084700001800102700002100120700002500141700001900166245009700185490000700282       2024                            d1 aGhazal Rahimzadeh1 aPaweł Pławiak1 aShady Mohamed1 aKathleen Lacy1 aDarius Nahavandi1 aRyszard Tadeusiewicz1 aHoushyar Asadi00aSignificance of Physiological Signal Thresholds in the Early Diagnosis of Simulator Sickness0 v1202084nas a2200181   4500000000100000008004100001100002300042700001600065700002000081700002200101700002700123700002000150245011300170856004600283490000700329520155200336022001401888       2024                            d1 aHamidreza Ashayeri1 aNavid Sobhi1 aPaweł Pławiak1 aSiamak Pedrammehr1 aRoohallah Alizadehsani1 aAli Jafarizadeh00aTransfer Learning in Cancer Genetics, Mutation Detection, Gene Expression Analysis, and Syndrome Recognition  uhttps://www.mdpi.com/2072-6694/16/11/21380 v163 aArtificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), has revolutionized medical research, facilitating advancements in drug discovery and cancer diagnosis. ML identifies patterns in data, while DL employs neural networks for intricate processing. Predictive modeling challenges, such as data labeling, are addressed by transfer learning (TL), leveraging pre-existing models for faster training. TL shows potential in genetic research, improving tasks like gene expression analysis, mutation detection, genetic syndrome recognition, and genotype–phenotype association. This review explores the role of TL in overcoming challenges in mutation detection, genetic syndrome detection, gene expression, or phenotype–genotype association. TL has shown effectiveness in various aspects of genetic research. TL enhances the accuracy and efficiency of mutation detection, aiding in the identification of genetic abnormalities. TL can improve the diagnostic accuracy of syndrome-related genetic patterns. Moreover, TL plays a crucial role in gene expression analysis in order to accurately predict gene expression levels and their interactions. Additionally, TL enhances phenotype–genotype association studies by leveraging pre-trained models. In conclusion, TL enhances AI efficiency by improving mutation prediction, gene expression analysis, and genetic syndrome detection. Future studies should focus on increasing domain similarities, expanding databases, and incorporating clinical data for better predictions.  a2072-6694