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Enhancing Mild Cognitive Impairment Detection through Efficient Magnetic Resonance Image Analysis

Computers, Materials & Continua 2024年第8期80卷 2081-2098页

作者： Atif Mehmood Zhonglong Zheng Rizwan Khan ahmad Al Smadi Farah Shahid Shahid Iqbal Mutasem K.Alsmadi Yazeed Yasin Ghadi syed Aziz Shah Mostafa M.Ibrahim Department of Computer Science and Technology Zhejiang Normal UniversityJinhua321002China Zhejiang Institute of Photoelectronics&Zhejiang Institute for Advanced Light Source Zhejiang Normal UniversityJinhua321004China Department of Data Science and Artificial Intelligence Zarqa UniversityZarqa13100Jordan School of Chemical and Environmental Engineering College of ChemistryChemical Engineering andMaterials ScienceSoochow UniversitySuzhou215123China Department of Management Information Systems College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityDammam32244Saudi Arabia Department of Computer Science Al Ain UniversityAbu Dhabi999041United Arab Emirates Department of Electrical Engineering Faculty of EngineeringMinia UniversityMinia61519Egypt Healthcare Technology and Innovation Theme Faculty Research Centre for Intelligent HealthcareCoventry UniversityCoventryCV48BWUK

Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease(AD).Mild cognitive impairment(MCI)is a condition that falls between the spectrum of normal cognitive function and ***,previous studies have mainly used handcrafted features to classify MCI,AD,and normal control(NC)*** paper focuses on using gray matter(GM)scans obtained through magnetic resonance imaging(MRI)for the diagnosis of individuals with MCI,AD,and *** improve classification performance,we developed two transfer learning strategies with data augmentation(i.e.,shear range,rotation,zoom range,channel shift).The first approach is a deep Siamese network(DSN),and the second approach involves using a cross-domain strategy with customized *** performed experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset to evaluate the performance of our proposed *** experimental results demonstrate superior performance in classifying the three binary classification tasks:NC ***,NC ***,and MCI ***,we achieved a classification accuracy of 97.68%,94.25%,and 92.18%for the three cases,*** study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI,AD,and normal control individuals using GM *** findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD.

关键词： Alzheimer’s disease mild cognitive impairment normal control transfer learning classification augmentation

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Refined Anam-Net:Lightweight Deep Learning Model for Improved Segmentation Performance of Optic Cup and Disc for Glaucoma Diagnosis

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Computers, Materials & Continua 2024年第7期80卷 1381-1405页

作者： Khursheed Aurangzeb syed Irtaza Haider Musaed Alhussein Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia

In this work,we aim to introduce some modifications to the Anam-Net deep neural network(DNN)model for segmenting optic cup(OC)and optic disc(OD)in retinal fundus images to estimate the cup-to-disc ratio(CDR).The CDR is a reliable measure for the early diagnosis of *** this study,we developed a lightweight DNN model for OC and OD segmentation in retinal fundus *** DNN model is based on modifications to Anam-Net,incorporating an anamorphic depth embedding *** reduce computational complexity,we employ a fixed filter size for all convolution layers in the encoder and decoder stages as the network *** modification significantly reduces the number of trainable parameters,making the model lightweight and suitable for resource-constrained *** evaluate the performance of the developed model using two publicly available retinal image databases,namely RIM-ONE and *** results demonstrate promising OC segmentation performance across most standard evaluation metrics while achieving analogous results for OD *** used two retinal fundus image databases named RIM-ONE and Drishti-GS that contained 159 images and 101 retinal images,*** OD segmentation using the RIM-ONE we obtain an f1-score(F1),Jaccard coefficient(JC),and overlapping error(OE)of 0.950,0.9219,and 0.0781,***,for OC segmentation using the same databases,we achieve scores of 0.8481(F1),0.7428(JC),and 0.2572(OE).Based on these experimental results and the significantly lower number of trainable parameters,we conclude that the developed model is highly suitable for the early diagnosis of glaucoma by accurately estimating the CDR.

关键词： Refined Anam-Net parameter tuning deep learning optic cup optic disc cup-to-disc ratio glaucoma diagnosis

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An efficient permutation approach for SbPN-based symmetric block ciphers

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Cybersecurity 2024年第2期7卷 113-130页

作者： Mir Nazish M.Tariq Banday Insha syed Sheena Banday Department of Electronics and Instrumentation Technology University ofKashmirSrinagar 190006India

It is challenging to devise lightweight cryptographic primitives efficient in both hardware and software that can provide an optimum level of security to diverse Internet of Things applications running on low-end constrained ***,an effcient hardware design approach that requires some specific hardware resource may not be effcient if implemented in *** bit Permutation Network based ciphers such as PRESENT and GIFT are effcient,lightweight cryptographic hardware design *** ciphers introduce confusion and diffu-sion by employing a 4×4 static substitution box and bit *** bit-wise permutation is realised by sim-ple rerouting,which is most cost-effective to implement in hardware,resulting in negligible power ***,this method is highly resource-consuming in software,particularly for large block-sized ciphers,with each single-bit permutation requiring multiple *** paper proposes a novel software-based design approach for permutation operation in Substitution bit Permutation Network based ciphers using a bit-banding *** conventional permutation using bit rotation and the proposed approach have been implemented,analysed and compared for GIFT and PRESENT ciphers on ARM Cortex-M3-based LPC1768 development platform with KEIL MDK used as an Integrated Development *** real-time performance comparison between conven-tional and the proposed approaches in terms of memory(RAM/ROM)footprint,power,energy and execution time has been carried out using ULINKpro and ULINKplus debug adapters for various code and speed optimisation *** proposed approach substantially reduces execution time,energy and power consumption for both PRE-SENT and GIFT ciphers,thus demonstrating the effciency of the proposed method for Substitution bit Permutation Network based symmetric block ciphers.

关键词： Lightweight cryptography PRESENT GIFT SbPN Cortex-M LPC1768 Bit-band memory

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Conservation laws,Lie symmetries,self adjointness,and soliton solutions for the Selkov–Schnakenberg system

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Communications in Theoretical Physics 2024年第2期76卷 29-44页

作者： Kashif Ali Aly R Seadawy syed T R Rizvi Noor Aziz Department of Mathematics COMSATS University IslamabadLahore CampusPakistan Mathematics Department Faculty of ScienceTaibah UniversityAl-Madinah Al-Munawarah41411Saudi Arabia

In this article,we explore the famous Selkov–Schnakenberg(SS)system of coupled nonlinear partial differential equations(PDEs)for Lie symmetry analysis,self-adjointness,and conservation ***,miscellaneous soliton solutions like dark,bright,periodic,rational,Jacobian elliptic function,Weierstrass elliptic function,and hyperbolic solutions of the SS system will be achieved by a well-known technique called sub-ordinary differential *** these results are displayed graphically by 3D,2D,and contour plots.

关键词： Selkov-Schnakenberg system Lie symmetry analysis conservation laws adjointness integrability

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Detection of onset of agglomeration in a bubbling fluidized bed biomass combustor using reactive Eulerian computational fluid dynamics

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Particuology 2024年第7期90卷 504-515页

作者： Abdullah Tasleem Atta Ullah Fei Li Qun Yi William Nimmo syed Sheraz Daood Department of Chemical Engineering Pakistan Institute of Engineering and Applied Sciences(PIEAS)Lehtrar RoadP.O.Nilore45650IslamabadPakistan Institute of Process Engineering Chinese Academy of SciencesBeijing100190China School of Chemical Engineering and Pharmacy Wuhan Institute of TechnologyWuhan430205China Energy Engineering Group Energy 2050Department of Mechanical EngineeringUniversity of SheffieldSheffieldS37RDUnited Kingdom Institute of Energy and Environmental Engineering Faculty of ElectricalEnergy&Environmental EngineeringUniversity of the PunjabQuaid-e-Azam CampusLahorePakistan Energy Engineering Research and Development Centre Faculty of ElectricalEnergy&Environmental EngineeringUniversity of the PunjabQuaid-e-Azam CampusLahorePakistan

The choice of a type of combustion technology to be used for heat or power generation depends on economic,technical,operational and fuel availability *** benefits associated with the evolving market driven by the fluidised bed combustion(FBC)technology cannot be overlooked especially when gauged at 65 GWth of worldwide installed capacity alongside added benefits of handling fuel variation,low pollutant emissions and high combustion *** or biomass waste will continue to have a vital role to play in the future FBC technology-based power *** often contains high levels of inorganic species that can form sticky agglomerates posing a significant risk to boiler operation resulting in unscheduled *** added complexity of the behaviour of the fuel and bed material mix highlights the requirement for simulation models to identify agglomeration to help improve the overall performance and reliability of FBC *** resolve this problem,this research devised a simulation strategy for the detection of agglomeration using the Eulerian–Eulerian *** developed modelling strategy is validated with the experimental data available in literature for two-dimensional simplified geometry of a pilot-scale fluidised bed *** model results were found promising and robust to predict bed defluidisation times and other parameters consistent with the experimental data.

关键词： Fluidized bed combustion Eulerian Models Biomass Agglomeration Defluidisation

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An asymmetrical multilevel inverter with minimum voltage stress and fewer components for photovoltaic renewable-energy system

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CLEAN ENERGY 2024年第1期8卷 1-22页

作者： Memon, Rabail Mahar, Mukhtiar Ahmed Larik, Abdul Sattar Shah, syed Asif Ali Mehran Univ Engn & Technol Dept Elect Engn Jamshoro Pakistan

The enhanced power quality provided by multilevel inverters (MLIs) has made them more appropriate for medium- and high-power applications, including photovoltaic systems. Nevertheless, a prevalent limitation involves the necessity for numerous switches and increased voltage stress across these switches, consequently increasing the overall system cost. To address these challenges, a new 17-level asymmetrical MLI with fewer components and low voltage stress is proposed for the photovoltaic system. This innovative MLI configuration has four direct current (DC) sources and 10 switches. Based on the trinary sequence, the proposed topology uses photovoltaics with boost converters and fuzzy logic controllers as its DC sources. Mathematical equations are used to calculate crucial parameters for this proposed design, including total standing voltage per unit (TSVPU), cost function per level (CF/L), component count per level (CC/L) and voltage stress across the switches. The comparison is conducted by considering switches, DC sources, TSVPU, CF/L, gate driver circuits and CC/L with other existing MLI topologies. The analysis is carried out under various conditions, encompassing different levels of irradiance, variable loads and modulation indices. To reduce the total harmonic distortion of the suggested topology, the phase opposition disposition approach has been incorporated. The suggested framework is simulated in MATLAB (R)/Simulink (R). The results indicate that the proposed topology achieves a well-distributed stress profile across the switches and has CC/L of 1.23, TSVPU of 5 and CF/L of 4.58 and 5.76 with weight coefficients of 0.5 and 1.5, respectively. These values are notably superior to those of existing MLI topologies. Simulation results demonstrate that the proposed topology maintains a consistent output at varying irradiance levels with FLCs and exhibits robust performance under variable loads and diverse modulation indices. Furthermore, the total harmonic disto

关键词： asymmetrical multilevel inverter photovoltaic system renewable energy component count per level cost function per level total standing voltage per unit phase opposition disposition technique CONVERTER TOPOLOGY REDUCED NUMBER IMPLEMENTATION PERFORMANCE CONNECTION REDUCTION DESIGN SINGLE

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Enabling Efficient Data Transmission in Wireless Sensor Networks-Based IoT Application

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Computers, Materials & Continua 2024年第6期79卷 4197-4218页

作者： Ibraheem Al-Hejri Farag Azzedin Sultan Almuhammadi Naeem Firdous syed Information and Computer Science Department King Fahd University of Petroleum andMineralsDhahran31261Saudi Arabia Center for Cyber Resilience and Trust(CREST) Deakin UniversityGeelong3220Australia

The use of the Internet of Things(IoT)is expanding at an unprecedented scale in many critical applications due to the ability to interconnect and utilize a plethora of wide range of *** critical infrastructure domains like oil and gas supply,intelligent transportation,power grids,and autonomous agriculture,it is essential to guarantee the confidentiality,integrity,and authenticity of data collected and ***,the limited resources coupled with the heterogeneity of IoT devices make it inefficient or sometimes infeasible to achieve secure data transmission using traditional cryptographic ***,designing a lightweight secure data transmission scheme is becoming *** this article,we propose lightweight secure data transmission(LSDT)scheme for IoT *** consists of three phases and utilizes an effective combination of symmetric keys and the Elliptic Curve Menezes-Qu-Vanstone asymmetric key agreement *** design the simulation environment and experiments to evaluate the performance of the LSDT scheme in terms of communication and computation *** and performance analysis indicates that the LSDT scheme is secure,suitable for IoT applications,and performs better in comparison to other related security schemes.

关键词： IoT lightweight computation complexity communication overhead cybersecurity threats threat prevention secure data transmission Wireless Sensor Networks(WSNs) elliptic curve cryptography

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Enhancing Skin Cancer Diagnosis with Deep Learning:A Hybrid CNN-RNN Approach

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Computers, Materials & Continua 2024年第4期79卷 1497-1519页

作者： syeda Shamaila Zareen Guangmin Sun Mahwish Kundi syed Furqan Qadri Salman Qadri Faculty of Information Technology Beijing University of TechnologyBeijing100124China Computer Science International Engineering Collage Maynooth UniversityKildareW23 F2H6Irland Research Center for Data Hub and Security Zhejiang LabHangzhou311121China Computer Science Department MNS University of AgricultureMultan59220Pakistan

Skin cancer diagnosis is difficult due to lesion presentation variability. Conventionalmethods struggle to manuallyextract features and capture lesions spatial and temporal variations. This study introduces a deep learning-basedConvolutional and Recurrent Neural Network (CNN-RNN) model with a ResNet-50 architecture which usedas the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extractionand temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesionphotos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long Short-TermMemory (LSTM) for temporal dependencies, the model achieves a high average recognition accuracy, surpassingprevious methods. The comprehensive evaluation, including accuracy, precision, recall, and F1-score, underscoresthe model’s competence in categorizing skin cancer types. This research contributes a sophisticated model andvaluable guidance for deep learning-based diagnostics, also this model excels in overcoming spatial and temporalcomplexities, offering a sophisticated solution for dermatological diagnostics research.

关键词： Skin cancer classification deep learning Convolutional Neural Network(CNN) RNN ResNet-50

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AI-Driven Energy Optimization in UAV-Assisted Routing for Enhanced Wireless Sensor Networks Performance

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Computers, Materials & Continua 2024年第9期80卷 4085-4110页

作者： syed Kamran Haider Abbas Ahmed Noman Mujeeb Khan Ali Nauman Sung Won Kim College of Internet of Things(IoT)Engineering Hohai UniversityChangzhou213001China Department of Electrical and Electronics Engineering Beaconhouse International CollegeIslamabad44000Pakistan School of Computer Science and Engineering Yeungnam UniversityGyeongsan38428Republic of Korea

In recent advancements within wireless sensor networks(WSN),the deployment of unmanned aerial vehicles(UAVs)has emerged as a groundbreaking strategy for enhancing routing efficiency and overall network *** research introduces a sophisticated framework,driven by computational intelligence,that merges clustering techniques with UAV mobility to refine routing strategies in *** proposed approach divides the sensor field into distinct sectors and implements a novel weighting system for the selection of cluster heads(CHs).This system is primarily aimed at reducing energy consumption through meticulously planned routing and path *** a greedy algorithm for inter-cluster dialogue,our framework orchestrates CHs into an efficient communication *** comparative analysis,the proposed model demonstrates a marked improvement over traditional methods such as the cluster chain mobile agent routing(CCMAR)and the energy-efficient cluster-based dynamic algorithms(ECCRA).Specifically,it showcases an impressive 15%increase in energy conservation and a 20%reduction in data transmission time,highlighting its advanced ***,this paper investigates the impact of various network parameters on the efficiency and robustness of the WSN,emphasizing the vital role of sophisticated computational strategies in optimizing network operations.

关键词： UAV trajectory clustering next-generation wireless sensor network(NGWSN) energy efficiency mobile sink

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Model Agnostic Meta-Learning(MAML)-Based Ensemble Model for Accurate Detection of Wheat Diseases Using Vision Transformer and Graph Neural Networks

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Computers, Materials & Continua 2024年第5期79卷 2795-2811页

作者： Yasir Maqsood syed Muhammad Usman Musaed Alhussein Khursheed Aurangzeb Shehzad Khalid Muhammad Zubair Department of Creative Technologies Faculty of Computing and Artificial IntelligenceAir UniversityIslamabad44000Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Department of Computer Engineering Bahria School of Engineering and Applied SciencesBahria UniversityIslamabad44000Pakistan Laboratory for Neuro&Psychophysiology KU LeuvenLeuven3000Belgium

Wheat is a critical crop,extensively consumed worldwide,and its production enhancement is essential to meet escalating *** presence of diseases like stem rust,leaf rust,yellow rust,and tan spot significantly diminishes wheat yield,making the early and precise identification of these diseases vital for effective disease *** advancements in deep learning algorithms,researchers have proposed many methods for the automated detection of disease pathogens;however,accurately detectingmultiple disease pathogens simultaneously remains a *** challenge arises due to the scarcity of RGB images for multiple diseases,class imbalance in existing public datasets,and the difficulty in extracting features that discriminate between multiple classes of disease *** this research,a novel method is proposed based on Transfer Generative Adversarial Networks for augmenting existing data,thereby overcoming the problems of class imbalance and data *** study proposes a customized architecture of Vision Transformers(ViT),where the feature vector is obtained by concatenating features extracted from the custom ViT and Graph Neural *** paper also proposes a Model AgnosticMeta Learning(MAML)based ensemble classifier for accurate *** proposedmodel,validated on public datasets for wheat disease pathogen classification,achieved a test accuracy of 99.20%and an F1-score of 97.95%.Compared with existing state-of-the-art methods,this proposed model outperforms in terms of accuracy,F1-score,and the number of disease pathogens *** future,more diseases can be included for detection along with some other modalities like pests and weed.

关键词： Wheat disease detection deep learning vision transformer graph neural network model agnostic meta learning

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