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Secure Transmission of Compressed Medical Image Sequences on Communication Networks Using Motion Vector Watermarking

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Computers, Materials & Continua 2024年第3期78卷 3283-3301页

作者： Rafi Ullah Mohd Hilmi bin Hasan sultan Daud Khan Mussadiq Abdul Rahim Computer and Information Sciences Department Universiti Teknologi PETRONASBandar Seri Iskandar Perak32610Malaysia Department of Computer Science National University of TechnologyIslamabad46000Pakistan Interdisciplinary Research Center for Intelligent Secure Systems King Fahd University of Petroleum and MineralsDammam31261Saudi Arabia

Medical imaging plays a key role within modern hospital management systems for diagnostic *** methodologies are extensively employed to mitigate storage demands and enhance transmission speed,all while upholding image ***,an increasing number of hospitals are embracing cloud computing for patient data storage,necessitating meticulous scrutiny of server security and privacy ***,considering the widespread availability of multimedia tools,the preservation of digital data integrity surpasses the significance of compression *** response to this concern,we propose a secure storage and transmission solution for compressed medical image sequences,such as ultrasound images,utilizing a motion vector watermarking *** watermark is generated employing an error-correcting code known as Bose-Chaudhuri-Hocquenghem(BCH)and is subsequently embedded into the compressed sequence via block-based motion *** the process of watermark embedding,motion vectors are selected based on their magnitude and phase *** embedding watermarks,no specific spatial area,such as a region of interest(ROI),is used in the *** embedding of watermark bits is dependent on motion *** reversible watermarking allows the restoration of the original image sequences,we use the irreversible watermarking *** reason for this is that the use of reversible watermarks may impede the claims of ownership and legal *** restoration of original data or images may call into question ownership or other legal *** peak signal-to-noise ratio(PSNR)and structural similarity index(SSIM)serve as metrics for evaluating the watermarked image *** all images,the PSNR value exceeds 46 dB,and the SSIM value exceeds *** results substantiate the efficacy of the proposed technique in preserving data integrity.

关键词： Block matching algorithm(BMA) compression full-search algorithm motion vectors ultrasound image sequence watermarking

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Ensemble Approach Combining Deep Residual Networks and BiGRU with Attention Mechanism for Classification of Heart Arrhythmias

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Computers, Materials & Continua 2024年第7期80卷 341-359页

作者： Batyrkhan Omarov Meirzhan Baikuvekov Daniyar sultan Nurzhan Mukazhanov Madina Suleimenova Maigul Zhekambayeva Department of Information Systems Al-Farabi Kazakh National UniversityAlmaty050040Kazakhstan Department of Mathematical and Computer Modeling International Information Technology UniversityAlmaty050040Kazakhstan Department of Software Engineering Satbayev UniversityAlmaty050013Kazakhstan

This research introduces an innovative ensemble approach,combining Deep Residual Networks(ResNets)and Bidirectional Gated Recurrent Units(BiGRU),augmented with an Attention Mechanism,for the classification of heart *** escalating prevalence of cardiovascular diseases necessitates advanced diagnostic tools to enhance accuracy and *** model leverages the deep hierarchical feature extraction capabilities of ResNets,which are adept at identifying intricate patterns within electrocardiogram(ECG)data,while BiGRU layers capture the temporal dynamics essential for understanding the sequential nature of ECG *** integration of an Attention Mechanism refines the model’s focus on critical segments of ECG data,ensuring a nuanced analysis that highlights the most informative features for arrhythmia *** on a comprehensive dataset of 12-lead ECG recordings,our ensemble model demonstrates superior performance in distinguishing between various types of arrhythmias,with an accuracy of 98.4%,a precision of 98.1%,a recall of 98%,and an F-score of 98%.This novel combination of convolutional and recurrent neural networks,supplemented by attention-driven mechanisms,advances automated ECG analysis,contributing significantly to healthcare’s machine learning applications and presenting a step forward in developing non-invasive,efficient,and reliable tools for early diagnosis and management of heart diseases.

关键词： CNN BiGRU ensemble deep learning ECG arrhythmia heart disease

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Assessing forest cover changes and fragmentation in the Himalayan temperate region: implications for forest conservation and management

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Journal of Forestry Research 2024年第4期35卷 128-141页

作者： Kaleem Mehmood Shoaib Ahmad Anees Akhtar Rehman Aqil Tariq Qijing Liu sultan Muhammad Fazli Rabbi Shao’an Pan Wesam Atef Hatamleh College of Forestry Beijing Forestry UniversityBeijing 100083People’s Republic of China Institute of Agriculture Sciences and Forestry University of SwatSwatPakistan Department of Forestry The University of AgricultureDera Ismail Khan 29050Pakistan Key Laboratory for Silviculture and Conservation of Ministry of Education Beijing Forestry UniversityBeijing 100083People’s Republic of China Beijing Key Laboratory of Precision Forestry Forestry CollegeBeijing Forestry UniversityBeijing 100083People’s Republic of China School of Environmental Studies China University of GeosciencesWuhan 430074People’s Republic of China Department of Wildlife Fisheries and AquacultureCollege of Forest ResourcesMississippi State UniversityStarkvilleMS 39762‑9690USA School of Management Tianjin University of CommerceTianjin 300134People’s Republic of China Department of Computer Science College of Computer and Information SciencesKing Saud UniversityP.O.Box 5117811543 RiyadhSaudi Arabia

This study comprehensively assessed long-term vegetation changes and forest fragmentation dynamics in the Himalayan temperate region of Pakistan from 1989 to *** satellite images,including Landsat-5 TM and Landsat-8 Operational Land Imager(OLI),were chosen for subsequent assessments in October 1989,2001,2011 and *** classified maps of 1989,2001,2011 and 2019 were created using the maximum likelihood ***-classification comparison showed an overall accuracy of 82.5%and a Kappa coefficient of 0.79 for the 2019 *** revealed a drastic decrease in closed-canopy and open-canopy forests by 117.4 and 271.6 km^(2),respectively,and an increase in agriculture/farm cultivation by 1512.8 km^(2).The two-way ANOVA test showed statistically significant differences in the area of various cover *** fragmentation was evaluated using the Landscape Fragmentation Tool(LFT v2.0)between 1989 and *** large forest core(>2.00 km^(2))decreased from 149.4 to 296.7 km^(2),and a similar pattern was observed in medium forest core(1.00-2.00 km^(2))*** the contrary,the small core(<1.00 km^(2))forest increased from 124.8 to 145.3 km^(2) in *** perforation area increased by 296.9 km^(2),and the edge effect decreased from 458.9 to 431.7 km^(2).The frequency of patches also increased by 119.1 km^(2).The closed and open canopy classes showed a decreasing trend with an annual rate of 0.58%and 1.35%,*** broad implications of these findings can be seen in the studied region as well as other global ecological *** serve as an imperative baseline for afforestation and reforestation operations,highlighting the urgent need for efficient management,conservation,and restoration *** on these findings,sustainable land-use policies may be put into place that support local livelihoods,protect ecosystem services,and conserve biodiversity.

关键词： Natural catastrophes Landsat Change detection Forest fragmentation Landscape fragmentation tool(LFT) Afforestation Reforestation

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Adaptive Cloud Intrusion Detection System Based on Pruned Exact Linear Time Technique

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Computers, Materials & Continua 2024年第6期79卷 3725-3756页

作者： Widad Elbakri Maheyzah Md.Siraj Bander Ali Saleh Al-rimy sultan Noman Qasem Tawfik Al-Hadhrami Faculty of Computing Universiti Teknologi MalaysiaJohor BahruJohor81310Malaysia Computer Science Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia School of Science and Technology Nottingham Trent UniversityNottinghamNG118NSUK

Cloud computing environments,characterized by dynamic scaling,distributed architectures,and complex work-loads,are increasingly targeted by malicious *** threats encompass unauthorized access,data breaches,denial-of-service attacks,and evolving malware *** security solutions often struggle with the dynamic nature of cloud environments,highlighting the need for robust Adaptive Cloud Intrusion Detection Systems(CIDS).Existing adaptive CIDS solutions,while offering improved detection capabilities,often face limitations such as reliance on approximations for change point detection,hindering their precision in identifying *** can lead to missed attacks or an abundance of false alarms,impacting overall security *** address these challenges,we propose ACIDS(Adaptive Cloud Intrusion Detection System)-*** novel Adaptive CIDS framework leverages the Pruned Exact Linear Time(PELT)algorithm and a Support Vector Machine(SVM)for enhanced accuracy and ***-PELT comprises four key components:(1)Feature Selection:Utilizing a hybrid harmony search algorithm and the symmetrical uncertainty filter(HSO-SU)to identify the most relevant features that effectively differentiate between normal and anomalous network traffic in the cloud environment.(2)Surveillance:Employing the PELT algorithm to detect change points within the network traffic data,enabling the identification of anomalies and potential security threats with improved precision compared to existing approaches.(3)Training Set:Labeled network traffic data forms the training set used to train the SVM classifier to distinguish between normal and anomalous behaviour patterns.(4)Testing Set:The testing set evaluates ACIDS-PELT’s performance by measuring its accuracy,precision,and recall in detecting security threats within the cloud *** evaluate the performance of ACIDS-PELT using the NSL-KDD benchmark *** results demonstrate that ACIDS-PELT outperf

关键词： Adaptive cloud IDS harmony search distributed denial of service(DDoS) PELT machine learning SVM ISOTCID NSL-KDD

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GliomaCNN: An Effective Lightweight CNN Model in Assessment of Classifying Brain Tumor from Magnetic Resonance Images Using Explainable AI

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Computer Modeling in Engineering & Sciences 2024年第9期140卷 2425-2448页

作者： Md.Atiqur Rahman Mustavi Ibne Masum Khan Md Hasib M.F.Mridha sultan Alfarhood Mejdl Safran Dunren Che Department of Computer Science and Engineering Ahsanullah University of Science and TechnologyDhaka1208Bangladesh Department of Computer Science and Software Engineering The University of Western AustraliaPerthWA 6009Australia Department of Computer Science American International University-BangladeshDhaka1229Bangladesh Department of Computer Science College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia School of Computing Southern Illinois UniversityCarbondale62901USA

Brain tumors pose a significant threat to human lives and have gained increasing attention as the tenth leading cause of global *** study addresses the pressing issue of brain tumor classification using Magnetic resonance imaging(MRI).It focuses on distinguishing between Low-Grade Gliomas(LGG)and High-Grade Gliomas(HGG).LGGs are benign and typically manageable with surgical resection,while HGGs are malignant and more *** research introduces an innovative custom convolutional neural network(CNN)model,*** stands out as a lightweight CNN model compared to its *** research utilized the BraTS 2020 dataset for its *** with the gradient-boosting algorithm,GliomaCNN has achieved an impressive accuracy of 99.1569%.The model’s interpretability is ensured through SHapley Additive exPlanations(SHAP)and Gradient-weighted Class Activation Mapping(Grad-CAM++).They provide insights into critical decision-making regions for classification *** challenges in identifying tumors in images without visible signs,the model demonstrates remarkable performance in this critical medical application,offering a promising tool for accurate brain tumor diagnosis which paves the way for enhanced early detection and treatment of brain tumors.

关键词： Deep learning magnetic resonance imaging convolutional neural networks explainable AI boosting algorithm ablation

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Design of a Lightweight Compressed Video Stream-Based Patient Activity Monitoring System

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Computers, Materials & Continua 2024年第1期78卷 1253-1274页

作者： Sangeeta Yadav Preeti Gulia Nasib Singh Gill Piyush Kumar Shukla Arfat Ahmad Khan sultan Alharby Ahmed Alhussen Mohd Anul Haq Department of Computer Science&Applications Maharshi Dayanand UniversityRohtak124001India University Institute of Technology Rajiv Gandhi Proudyogiki VishwavidyalayaBhopal462033India Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl Majmaah11952Kingdom of Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl Majmaah11952Kingdom of Saudi Arabia

Inpatient falls from beds in hospitals are a common *** falls may result in severe *** problem can be addressed by continuous monitoring of patients using *** advancements in deep learning-based video analytics have made this task of fall detection more effective and *** with fall detection,monitoring of different activities of the patients is also of significant concern to assess the improvement in their *** computation-intensive models are required to monitor every action of the patient *** requirement limits the applicability of such ***,to keep the model lightweight,the already designed fall detection networks can be extended to monitor the general activities of the patients along with the fall *** by the same notion,we propose a novel,lightweight,and efficient patient activity monitoring system that broadly classifies the patients’activities into fall,activity,and rest classes based on their *** whole network comprises three sub-networks,namely a Convolutional Neural Networks(CNN)based video compression network,a Lightweight Pose Network(LPN)and a Residual Network(ResNet)Mixer block-based activity recognition *** compression network compresses the video streams using deep learning networks for efficient storage and retrieval;after that,LPN estimates human ***,the activity recognition network classifies the patients’activities based on their *** proposed system shows an overall accuracy of approx.99.7% over a standard dataset with 99.63% fall detection accuracy and efficiently monitors different events,which may help monitor the falls and improve the inpatients’health.

关键词： Fall detection activity recognition human pose estimation accuracy

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An Innovative Approach Using TKN-Cryptology for Identifying the Replay Assault

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Computers, Materials & Continua 2024年第1期78卷 589-616页

作者： Syeda Wajiha Zahra Muhammad Nadeem Ali Arshad Saman Riaz Muhammad Abu Bakr Ashit Kumar Dutta Zaid Alzaid Badr Almutairi sultan Almotairi Department of Computer Science Alhamd Islamic UniversityIslamabad44000Pakistan Department of Computer Science and Technology University of Science and Technology BeijingBeijing100083China Department of Computer Science National University of TechnologyIslamabad44000Pakistan Department of Electrical Engineering National University of TechnologyIslamabad44000Pakistan Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of Computer Science Faculty of Computer and Information SystemsIslamic University of MadinahMedinah42351Saudi Arabia Department of Information Technology College of Computer Sciences and Information Technology CollegeMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Natural and Applied Sciences Faculty of Community CollegeMajmaah UniversityMajmaah11952Saudi Arabia

Various organizations store data online rather than on physical *** the number of user’s data stored in cloud servers increases,the attack rate to access data from cloud servers also *** researchers worked on different algorithms to protect cloud data from replay *** of the papers used a technique that simultaneously detects a full-message and partial-message replay *** study presents the development of a TKN(Text,Key and Name)cryptographic algorithm aimed at protecting data from replay *** program employs distinct ways to encrypt plain text[P],a user-defined Key[K],and a Secret Code[N].The novelty of the TKN cryptographic algorithm is that the bit value of each text is linked to another value with the help of the proposed algorithm,and the length of the cipher text obtained is twice the length of the original *** the scenario that an attacker executes a replay attack on the cloud server,engages in cryptanalysis,or manipulates any data,it will result in automated modification of all associated values inside the *** mechanism has the benefit of enhancing the detectability of replay ***,the attacker cannot access data not included in any of the papers,regardless of how effective the attack strategy *** the end of paper,the proposed algorithm’s novelty will be compared with different algorithms,and it will be discussed how far the proposed algorithm is better than all other algorithms.

关键词： Replay attack malware message attack file encryption cryptology data security

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Efficient Communication in Wireless Sensor Networks Using Optimized Energy Efficient Engroove Leach Clustering Protocol

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Tsinghua Science and Technology 2024年第4期29卷 985-1001页

作者： N.Meenakshi sultan Ahmad A.V.Prabu J.Nageswara Rao Nashwan Adnan Othman Hikmat A.M.Abdeljaber R.Sekar Jabeen Nazeer SRM Institute of Science and Technology KattankulathurChennai 603203India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj 11942Saudi Arabia University Center for Research and Development(UCRD) Department of Computer Science and EngineeringChandigarh UniversityGharuanMohali 140413India Department of ECE Koneru Lakshmaiah Education FoundationVaddeswaramGuntur 522502India Department of Computer Science and Engineering Lakireddy Balireddy College of Engineering(A)NTR District 521230India Department of Computer Engineering College of EngineeringKnowledge UniversityErbil 44001Iraq Department of Computer Science Faculty of Information TechnologyApplied Science Private UniversityAmman 11937Jordan Department of Electronics and Communication Engineering(ECE) School of EngineeringPresidency UniversityBangalore 834001India

The Wireless Sensor Network(WSN)is a network that is constructed in regions that are inaccessible to human *** widespread deployment of wireless micro sensors will make it possible to conduct accurate environmental monitoring for a use in both civil and military *** make use of these data to monitor and keep track of the physical data of the surrounding environment in order to ensure the sustainability of the *** data have to be picked up by the sensor,and then sent to the sink node where they may be *** nodes of the WSNs are powered by batteries,therefore they eventually run out of *** energy restriction has an effect on the network life span and environmental *** objective of this study is to further improve the Engroove Leach(EL)protocol’s energy efficiency so that the network can operate for a very long time while consuming the least amount of *** lifespan of WSNs is being extended often using clustering and routing *** Meta Inspired Hawks Fragment Optimization(MIHFO)system,which is based on passive clustering,is used in this study to do *** cluster head is chosen based on the nodes’residual energy,distance to neighbors,distance to base station,node degree,and node *** on distance,residual energy,and node degree,an algorithm known as Heuristic Wing Antfly Optimization(HWAFO)selects the optimum path between the cluster head and Base Station(BS).They examine the number of nodes that are active,their energy consumption,and the number of data packets that the BS *** overall experimentation is carried out under the MATLAB *** the analysis,it has been discovered that the suggested approach yields noticeably superior outcomes in terms of throughput,packet delivery and drop ratio,and average energy consumption.

关键词： wireless sensor networks energy efficient engroove leach protocol meta inspired Hawks fragment optimization heuristic wing antfly optimization

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The Early Warning Signs of a Stroke: An Approach Using Machine Learning Predictions

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Journal of Computer and Communications 2024年第6期12卷 59-71页

作者： Esraa H. Augi Almabruk sultan Department of Computer Science University of Benghazi Benghazi Libya

Early stroke prediction is vital to prevent damage. A stroke happens when the blood flow to the brain is disrupted by a clot or bleeding, resulting in brain death or injury. However, early diagnosis and treatment reduce long-term needs and lower health costs. We aim for this research to be a machine-learning method for forecasting early warning signs of stroke. The methodology we employed feature selection techniques and multiple algorithms. Utilizing the XGboost Algorithm, the research findings indicate that their proposed model achieved an accuracy rate of 96.45%. This research shows that machine learning can effectively predict early warning signs of stroke, which can help reduce long-term treatment and rehabilitation needs and lower health costs.

关键词： Machine Learning Stroke k-Nearest Neighbors Decision Tree Random Forest GXboost

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Optimizing the utilization of maize silage in forage blends-based rations to improve production performance and reduce methane emissions from fattening calves

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International Journal of Agricultural and Biological Engineering 2024年第3期17卷 75-81页

作者： Khalid Farooq Asad sultan Assar Ali Shah Rifat Ullah Khan Rajwali Khan Ghazala Yasmeen Nazir Ahmad Khan Faculty of Animal Husbandry and Veterinary Sciences The University of AgriculturePeshawar 25130Pakistan Pakistan Scientific and Technological Information Center(PASTIC) Islamabad 44000Pakistan

This research investigated the effect of various mixtures of maize silage and ryegrass fodder on nutrient digestibility,production performance,and methane(CH4)emission of fattening ***-four fattening calves((228±10)kg body weight(BW),(280±5)d of age)were allocated to 8 diets for 84 d,according to a randomized complete block design,and the blocks were balanced for BW,age,and *** diets consisted of 4 levels of maize silages,i.e.,40%(MS40),50%(MS50),60%(MS60),and 70%(MS70)in the maize silage and ryegrass fodder mixture on a dry matter(DM)basis,and each of the mixture was supplemented with either low(LC,1.0%BW)or high(HC,1.5%BW)levels of fattening *** results revealed greater(p<0.05)intakes of DM(8.35 kg/d),organic matter(OM,7.85 kg/d),and metabolizable energy(ME,12.00 Mcal/d)in calves fed with MS70-LC *** highest(p<0.05)digestibility(g/100 g)of DM(65.2),OM(67.3),crude protein(69.1),and neutral detergent fibre(56.3)was recorded for MS40-HC *** maximum(p<0.05)average daily gain(ADG,571 g/d)was recorded for diet *** lowest value(p<0.05)of CH4 emission was recorded in MS70-LC,MS70-HC,and MS60-HC as compared to other *** 70%maize silage in the ryegrass-maize silage based diet improved ADG by 174 g/d with LC *** is concluded that the optimum inclusion level of maize silage and ryegrass in the fattening ration can improve animal production performance and reduces CH4 production and concentrate requirements of fattening calves.

关键词： average daily gain farm-grown forages forage mixture fattening calves maize silage ryegrass fodder

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