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7th International Conference on Contemporary computing and Informatics, IC3I 2024

作者： Bisht, Girish Singh Jain, Arpit Bansla, Vandna Sharma, Khemraj Bhutia, Rudramani Kumar, Vikas Tula's Institute Department of Computer Science & Engineering Dehradun248001 India Koneru Lakshamaiah Education Foundation Department of Computer Science & Engineering A.P. Vadessahwaram India Shivalik Engineering College Dehradun248001 India Nanhua University Doctoral Program in Management Sciences Department of Business Administration Dalin Chiayi62248 Taiwan Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering A.P. India Dev Bhoomi Uttrakhand University School Engineering and Computing Uttrakhand Dehradun India

ISBN: (纸本)9798350350067

Copy-move forgeries often exploit homogeneous regions in images with large-scale attacks to either highlight or conceal target objects. These manipulations are simple to execute but challenging to notice. Forgery detection techniques like Copy-Move Forgery Detection (CMFD) cannot detect these forged documents because they are unable to identify a sufficient number of effective keypoints in homogeneous areas, leading to inaccurate and inefficient results. SURF stands for Speeded-Up Robust Features and is used in this paper along with A-KAZE and Scale-Invariant Feature Transforms. According to our experiment, A-KAZE offers superior detection performance under diverse attacks, especially when it comes to large-scale attacks targeting homogeneous regions. A-KAZE is found to be more accurate than SIFT, SURF, and A-KAZE when applied to the NB-CASIA dataset, achieving detection accuracies of 89.2%, 93.9% and 98.98%.) © 2024 IEEE.

关键词： Image analysis

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Marine Predators Algorithm with Deep Learning-Based Leukemia Cancer Classification on Medical Images

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computer Modeling in engineering & Sciences 2024年第10期141卷 893-916页

作者： Sonali Das Saroja Kumar Rout Sujit Kumar Panda Pradyumna Kumar Mohapatra Abdulaziz S.Almazyad Muhammed Basheer Jasser Guojiang Xiong Ali Wagdy Mohamed Department of Computer Science&Engineering Gandhi Institute for TechnologyBPUTBhubaneswar752054India Department of Information Technology Vardhaman College of Engineering(Autonomous)Hyderabad501218TelanganaIndia Department of Electronic&Communication Engineering Vedang Institute of TechnologyBhubaneswar752010India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computing and Information Systems School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Research Centre for Human-Machine Collaboration(HUMAC) School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects *** depends on the type of leukemia and the extent to which cancer has established throughout the *** leukemia in the initial stage is vital to providing timely patient *** image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive *** can be simple to generalize computer vision(CV)-based and image-processing techniques and eradicate human *** researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its *** study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical *** projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical *** MPADL-LCC system uses Faster SqueezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature ***,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia *** hyperparameter tuning process using MPA helps enhance leukemia cancer classification *** results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.

关键词： Leukemia cancer medical imaging image classification deep learning marine predators algorithm

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Accurate Extraction of Graphene Scattering Properties via a Robust Formulation for Fundamental Modes

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IEEE Transactions on Antennas and Propagation 2025年

作者： Cano, Pablo H. Zapata Amanatiadis, Stamatios A. Zaharis, Zaharias D. Yioultsis, Traianos V. Lazaridis, Pavlos I. Kantartzis, Nikolaos V. Aristotle University of Thessaloniki School of Electrical and Computer Engineering Thessaloniki54124 Greece University of Huddersfield School of Computing and Engineering HuddersfieldHD1 3DH United Kingdom

This paper explores graphene's quasi-normal modes (QNMs) by developing a finite element method solver, which addresses an augmented eigenvalue problem where graphene is represented as an equivalent surface current. This representation correlates with the surface conductivity of the material through a Debye frequency dispersion model. Initially, the straightforward intraband term is considered, while the study delves into the interband contributions to graphene's conductivity, described via a complex conjugate series of Debye terms. Also, it examines the magneto-optical properties of graphene, which can be tuned by an external magnetic field to produce non-reciprocal effects. Finally, the proposed formulation covers the normalization of graphene QNMs and the reconstruction of scattered fields, providing a complete analysis tool that handles graphene as a scattering surface. The featured methodology is successfully validated by comparing the evaluated absorption cross-section due to scattering from graphene sheets with the corresponding outcomes of a commercial full-wave solver. Graphene attributes are selected properly to model high quality-factor resonances indicating the remarkable accuracy of the proposed scheme. © 1963-2012 IEEE.

关键词： Eigenvalues and eigenfunctions

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Vision Transformer Approaches for Diabetic Retinopathy

Vision Transformer Approaches for Diabetic Retinopathy

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Advances in Computation, Communication and Information Technology (ICAICCIT), International Conference on

作者： Soubraylu Sivakumar P. Likhith Kumar Gudeti Neeraj Reddy B. Anandaraj T. Rajesh Kumar P. Palanisamy Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Computer Science Engineering Madanapalle Institute of Technology & Science Madanapalle Andhar Pradesh India Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Computer Science and Engineering School of Computing K.S.R College of Engineering (Autonomous) Tiruchengode Namakkal India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

The focus of this paper is to identify how deep learning can be utilised in identifying diabetic retinopathy, a serious complication of diabetes which causes blindness. In this paper, an end-to-end system is proposed for diagnosing the early signs of disease using CNN to analyze images. Cropped Retinal - CNN - Early Diagnosis of the First Stage of the Disease. The overall performance of the proposed system is determined using accuracy, precision, and usability that are far much better than the conventional method of diagnosing the syndromes. Five models are explored under this study which includes Vision Transformer (ViT), Convolutional Neural Network, MobileNetV2, YOLO and Hybrid model (ViT+CNN). five levels of Diabetic Retinopathy (DR) are explored viz., No, Mild, Moderate, Severe and Proliferative DR. From these five models, the hybrid model achieved a $\mathbf{9 4 \%}$ of accuracy on the DR dataset. More future work includes the expansion of the expressed model and the inclusion of real-time image processing for clinical use.

关键词： YOLO Diabetic retinopathy computer vision Accuracy Computational modeling Transformers Retina Convolutional neural networks Usability Diseases

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Optimized solar PV integration for voltage enhancement and loss reduction in the Kombolcha distribution system using hybrid grey wolf-particle swarm optimization

Results in Engineering

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Results in engineering 2025年 26卷

作者： Abera, Awot Getachew Yetayew, Tefera Terefe Alyu, Assen Beshr Faculty of Electrical and Computer engineering Bahir Dar University Bahir Dar Ethiopia College of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia School of Electrical and Computer Engineering Dilla University Dilla Ethiopia

Voltage instability and excessive power losses are common challenges in distribution networks. This research investigates optimal integration of solar photovoltaic distributed generation units so as to improve voltage profiles and reduce power losses contributing greatly to sustainable energy goals. A hybrid optimization approach combining Particle Swarm Optimization and Grey Wolf Optimization algorithms is proposed for determining optimal sizing and placement of PV-based DGs. Study focuses on Kombolcha town Feeder-3 distribution network consisting of 131 buses where mathematical model and load flow analysis were conducted using Backward-Forward Sweep (BFS) method. Three scenarios were analyzed: Base Case, Type-1 DG integration and Type-3 DG integration. BFS analysis identified total active and reactive power losses of 506.93 kW and 303.55 kVAr with 90.84% of buses operating below minimum voltage standard. Integrating up to four Type-3 DG units reduced active power losses by 87.18%-93.01% and reactive power losses by 87.18%-93.01% substantially enhancing voltage stability. Economic analysis revealed payback period of approximately six years underscoring feasibility of proposed solution. Hybrid PSO-GWO optimization effectively enhances performance of distribution networks offering practical approach to integrating solar PV systems. © 2025 The Author(s)

关键词： Particle swarm optimization (PSO)

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Flexible computational photodetectors for self-powered activity sensing

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npj Flexible Electronics 2022年第1期6卷 45-52页

作者： Dingtian Zhang Canek Fuentes-Hernandez Raaghesh Vijayan Yang Zhang Yunzhi Li Jung Wook Park Yiyang Wang Yuhui Zhao Nivedita Arora Ali Mirzazadeh Youngwook Do Tingyu Cheng Saiganesh Swaminathan Thad Starner Trisha L.Andrew Gregory D.Abowd School of Interactive Computing Georgia Institute of TechnologyAtlantaGAUSA School of Electrical and Computer Engineering Georgia Institute of TechnologyAtlantaGAUSA Department of Electrical and Computer Engineering Northeastern UniversityBostonMAUSA Department of Chemistry University of Massachusetts AmherstAmherstMAUSA Department of Electrical and Computer Engineering University of CaliforniaLos AngelesCAUSA Human-Computer Interaction Institute Carnegie Mellon UniversityPittsburghPAUSA Departments of Chemistry and Chemical Engineering University of Massachusetts AmherstAmherstMAUSA

Conventional vision-based systems,such as cameras,have demonstrated their enormous versatility in sensing human activities and developing interactive ***,these systems have long been criticized for incurring privacy,power,and latency issues due to their underlying structure of pixel-wise analog signal acquisition,computation,and *** this research,we overcome these limitations by introducing in-sensor analog computation through the distribution of interconnected photodetectors in space,having a weighted responsivity,to create what we call a computational *** photodetectors can be used to extract mid-level vision features as a single continuous analog signal measured via a two-pin *** develop computational photodetectors using thin and flexible low-noise organic photodiode arrays coupled with a self-powered wireless system to demonstrate a set of designs that capture position,orientation,direction,speed,and identification information,in a range of applications from explicit interactions on everyday surfaces to implicit activity detection.

关键词： everyday overcome photodetector

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Stationary Vehicles Detection on Smart Highways and Roads using Spatio-temporal Tracking 16

Stationary Vehicles Detection on Smart Highways and Roads us...

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16th International Conference on Developments in eSystems engineering, DeSE 2023

作者： Khan, Wasiq Kelly, Jessica Al-Kafri, Ala Kleanthous, Natasa Khayam, Umar Khan, Bilal Liverpool John Moores University Faculty of Engineering & Technology Liverpool United Kingdom Teesside University School of Computing Engineering and Digital Technologies United Kingdom Khalifa University Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Comsats University Pakistan California State University School of Computer Science & Eng San BernardinoCA United States

ISBN: (纸本)9798350381344

Where traditional motorways contain hard shoulders to provide refuge for broken-down vehicles, smart motorways instead use live outer lane to ease congestion. The live lanes can be closed due to accidents or breakdowns which is communicated to other road users through overhead gantry signs. This can only occur if the traffic management control center is made aware of the stationary vehicle(s), through either notification via phone call or Motorway Incident Detection and Automatic Signaling (MIDAS) induction loop technology. Alternatively, radar-based stopped vehicle detection is used to identify non-moving objects on the highway. However, this technology is unable to recognize objects or distinguish between congestion and break down etc., which leads to generate false alarms. For the first time, we propose a fully autonomous computer vision and deep learning-based solution to detect stationary vehicles on highways and local roads. We employ deep transfer learning to build a custom-trained vehicle detection model using a newly prepared dataset comprising over 105,000 annotated vehicle instances. DeepSort algorithm is employed for real-time vehicle tracking through associating instances between time series frames, followed by a rule-based algorithm to identify the current state of detected vehicles. Experimental outcomes show our approach as outperforming the state-of-the-art methods in terms of efficient and reliable detection of stationary vehicles (with 98.3% accuracy) as well as distinguish them from congestions when evaluated over video streams captured in realistic dynamic and diverse conditions. © 2023 IEEE.

关键词： Vehicles

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Improving Alpha-Beta Pruning Efficiency in Chess Engines 2

Improving Alpha-Beta Pruning Efficiency in Chess Engines

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2nd International Conference on IT Innovations and Knowledge Discovery, ITIKD 2024

作者： Nair, Rekha R Babu, Tina Kishore, S. Pavithra, K. Sumana, S.G. Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Bengaluru India Nitte Institute of Professional Education Nitte (Deemed to Be University Department of Computer Applications Mangalore India Dayananda Sagar University Department of Computer Applications Bengaluru India

ISBN: (纸本)9798350355468

Alpha-Beta Pruning is a critical optimization technique in chess engines, helping them make fast, accurate decisions by reducing unnecessary calculations. This paper focuses on enhancing its efficiency to improve search depth and speed with-out compromising accuracy. This investigation examines current challenges in traditional implementations, such as inefficiencies in move ordering and redundant computations, especially in complex game scenarios. The proposed innovative strategies include dynamic move ordering powered by machine learning and improved transposition table management. These techniques streamline the pruning process, ensuring the most promising moves are evaluated first and avoiding repetitive calculations. Adaptive depth control is explored to make the algorithm more responsive to the game's context. Extensive testing on various chess positions demonstrated clear improvements in pruning efficiency. The enhanced algorithm can analyze deeper game trees with reduced computational costs, making chess engines faster and smarter. These advancements not only benefit chess engines but also have broader implications for Artificial Intelligence (AI) systems requiring efficient decision-making in dynamic environments. Improved Alpha-Beta Pruning methods make chess engines smarter and faster, allowing them to analyze deeper strategies while using less computing power. © 2025 IEEE.

关键词： Adaptive Depth Control Alpha-Beta Pruning Chess Engines Game Tree Analysis Move Ordering Optimization Transposition Table

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L1TF Foreshadow CPU Vulnerabilities Analysis and Mitigation 4

L1TF Foreshadow CPU Vulnerabilities Analysis and Mitigation

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4th Intelligent Cybersecurity Conference, ICSC 2024

作者： Tawalbeh, Lo'ai Staley, Harry Velez, David Skitenko, Artem Tawalbeh, Abdelrahman Muheidat, Fadi Jordan University of Science and Technology Department of Computer Engineering Irbid22110 Jordan Texas A&M University Department of Computing and Cyber Security San AntonioTX78224 United States Abdelrahman Tawalbeh Johnson High School San AntonioTX78259 United States School of Computer Science and Engineering California State University San BernardinoCA92407 United States

ISBN: (纸本)9798350354775

This paper analyzes a recently discovered vulnerability in modern computer CPU architectures affecting Intel, AMD, and ARM. These processors use built in optimizers such Speculative Execution which have a design flaw that allows access to restricted kernel memory spaces. Exploits such as Spectre can utilize newfound CPU design flaws to obtain confidential data. This defect has affected millions of devices, from PCs and cell phones to hypervisors that make up the fabric of cloud infrastructure. It's possible for malicious actors to obtain private data using side channel attacks, that exist in the Levell cache to read data;even allowing attacks to be injected from JavaScript upon visiting a website. In this paper, we will demonstrate experimentally the weaknesses in the processor design by extracting private data using the Meltdown exploit and accessing the kernel space to retrieve data before the protect bit is read by the instruction set. © 2024 IEEE.

关键词： Memory architecture

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Automated Detection and Classification of Cotton Leaf Diseases: A computer Vision Approach

Automated Detection and Classification of Cotton Leaf Diseas...

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Advances in Modern Age Technologies for Health and engineering Science (AMATHE), International Conference on

作者： Swastick Ganguly Jathin S M Mohak M Sanjay BM Somashekhar N Shobharani Department of Computer Science Amrita School of Computing Mysuru Campus Dept of Computer Science and Engineering Gitam School of Technology Gitam University Bengaluru Campus

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

Cotton plays a vital role in agricultural sustainability, extending beyond its textile applications. Pathogens pose significant threats to cotton plants and farmers' livelihoods, highlighting the critical need for disease detection to maintain crop health and ensure optimal yields. Our research pioneers a novel approach to disease detection in cotton through advanced image analysis techniques. This involves exploring deep learning methodologies specifically tailored for detecting cotton leaf diseases and enhancing accuracy by integrating CNN models, while balancing labor intensity and cost considerations. Leveraging the Convolutional Neural Networks (CNN), the combination of the interpretability of Densenet 121 with the stability of ResNet 50, assessing the strength of their combined algorithm. This hybrid model accurately classifies various cotton plant diseases, enabling fine-grained analysis. With a precision-driven approach, the proposed model achieves exceptional results, accurately identifying six distinct diseases, including fungal, bacterial, and viral pathogens, with an impressive 96% accuracy rate. The research aims to provide a comprehensive and dependable solution for disease management in the cotton industry, ultimately promoting crop health, productivity, and sustainability

关键词： Deep learning Training Plant diseases Pathogens Accuracy Computational modeling Crops

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