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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Ali, Asfand Irfan, Asim Raza, Ali Memon, Zainab Mehran University of Engineering and Technology Department of Telecommunication Engineering Jamshoro Pakistan Mehran University of Engineering and Technology Computer Systems Engineering Jamshoro Pakistan Quaid-e-Awam University of Engineering Science and Technology Computer Systems Engineering Nawabshah Pakistan

ISBN: (纸本)9798350373042

This research project focuses on utilizing machine learning techniques to predict loan default among applicants in the context of financial organizations. Loan approval decisions carry substantial risks, and not all applicants can be deemed trustworthy. The primary goal of this research is to determine the feasibility of accurately presenting loan defaults by analyzing a dataset sourced from *** project involves data analysis and the application of machine learning algorithms, including logistic regression, K Nearest Neighbor, and Decision tree. Implementation will be carried out using Python programming language within Visual Studio Code, with version control through GitHub, and eventual deployment on the AWS Cloud platform. The anticipated outcomes aim for high prediction accuracy and moderate training and validation data loss, contributing to more informed decision-making in the lending process. © 2024 IEEE.

关键词： Data handling

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A Hybrid Cybersecurity Algorithm for Digital Image Transmission over Advanced Communication Channel Models

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computers, Materials & Continua 2024年第4期79卷 201-241页

作者： Naglaa F.Soliman Fatma E.Fadl-Allah Walid El-Shafai Mahmoud I.Aly Maali Alabdulhafith Fathi E.Abd El-Samie Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electronics and Communications Faculty of EngineeringZagazig UniversityZagazig44519Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

The efficient transmission of images,which plays a large role inwireless communication systems,poses a significant challenge in the growth of multimedia ***-quality images require well-tuned communication *** Single Carrier Frequency Division Multiple Access(SC-FDMA)is adopted for broadband wireless communications,because of its low sensitivity to carrier frequency offsets and low Peak-to-Average Power Ratio(PAPR).Data transmission through open-channel networks requires much concentration on security,reliability,and *** data need a space away fromunauthorized access,modification,or *** requirements are to be fulfilled by digital image watermarking and *** paper ismainly concerned with secure image communication over the wireless SC-FDMA systemas an adopted communication *** introduces a robust image communication framework over SC-FDMA that comprises digital image watermarking and encryption to improve image security,while maintaining a high-quality reconstruction of images at the receiver *** proposed framework allows image watermarking based on the Discrete Cosine Transform(DCT)merged with the Singular Value Decomposition(SVD)in the so-called DCT-SVD *** addition,image encryption is implemented based on chaos and DNA *** encrypted watermarked images are then transmitted through the wireless SC-FDMA *** linearMinimumMean Square Error(MMSE)equalizer is investigated in this paper to mitigate the effect of channel fading and noise on the transmitted *** subcarrier mapping schemes,namely localized and interleaved schemes,are compared in this *** study depends on different channelmodels,namely PedestrianAandVehicularA,with a modulation technique namedQuadratureAmplitude Modulation(QAM).Extensive simulation experiments are conducted and introduced in this paper for efficient transmission of encrypted watermarked *** addition,different variants of SC-FDMA bas

关键词： Cybersecurity applications image transmission channel models modulation techniques watermarking and encryption

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An Attention-Based Model for Clinical Time Series Prediction: Enhancing ICU Readmission Prediction 14

An Attention-Based Model for Clinical Time Series Prediction...

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14th International Conference on computer and Knowledge engineering, ICCKE 2024

作者： Madinei, Hananeh Sadat Keyvanpour, Mohammad Reza Shojaedini, Seyed Vahab Data Mining Laboratory Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Electrical Engineering Iranian Research Organization for Science and Technology Tehran Iran

ISBN: (纸本)9798331511272

— In recent years, time series prediction has become a highly interesting topic in various applied areas, including clinical time series analysis. Hospitals and other clinical healthcare systems collect Electronic Health Records. These records contain important information that needs to be mined. This information provides a solid basis for predicting hospital readmission, helping to improve healthcare, enhance patient outcomes, and optimize hospital resources. This study introduces a novel deep learning attention-based approach for predicting 30-day ICU readmissions using clinical time series information extracted from the MIMIC III dataset. This approach incorporates time-aware positional encoding into the attention mechanism to enhance predictive performance by capturing the temporal representation of data and considering the temporal order of clinical time series data. This model is compared with cutting-edge models. The significant enhancements in the model performance achieved by the proposed method highlight the effectiveness of incorporating time-aware positional encoding in capturing temporal dependencies and improving prediction outcomes. These results underscore the potential of advanced attention mechanisms in clinical time series analysis. ©2024 IEEE.

关键词： Electronic health record

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Combining Extreme Learning Machine and Linear Discriminant Analysis for Optimized Apple Leaf Disease Classification 4

Combining Extreme Learning Machine and Linear Discriminant A...

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4th International Conference on Electronic and Electrical engineering and Intelligent System, ICE3IS 2024

作者： Parjito Ahmad, Imam Borman, Rohmat Indra Alexander, Allan D. Jusman, Yessi Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandarlampung Indonesia Universitas Bhayangkara Jakarta Raya Faculty of Computer Science South Jakarta Indonesia Universitas Muhammadiyah Faculty of Engineering Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350378368

Apple trees are an agricultural commodity with high economic value that often face serious challenges due to various leaf diseases. Early detection and proper treatment are crucial to reducing economic losses and ensuring healthy plant growth. Manual methods for detecting apple leaf diseases tend to be time-consuming and require specialized expertise, making them difficult to apply on a large scale. This study aims to develop an automatic classification system based on image processing to detect apple leaf diseases by combining Extreme Learning Machine (ELM) and Linear Discriminant Analysis (LDA) algorithms. ELM offers high training speed with randomly assigned weights, while LDA performs dimensionality reduction to retain the most relevant features, thereby improving model performance. The study results indicate that the combination of ELM and LDA produces a model capable of classifying apple leaf diseases with an accuracy of 92.50%, an improvement of 6.25% compared to using ELM alone. This demonstrates that the model is highly effective in classifying apple leaf diseases. © 2024 IEEE.

关键词： Fruits

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Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets

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computers, Materials & Continua 2023年第2期74卷 4531-4545页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Mostafa Abotaleb Abdelhameed Ibrahim Abdelaziz A.Abdelhamid D.L.Elsheweikh Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt

Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine *** feature in a dataset has 2n possible subsets,making it challenging to select the optimum collection of features using typical *** a result,a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization(DTO-GW)algorithms has been developed in this *** can result when the selection of features is subject to metaheuristics,which can lead to a wide range of ***,we adopted hybrid optimization in our method of optimizing,which allowed us to better balance exploration and harvesting chores more *** propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of *** the proposed method,the number of features selected is minimized,while classification accuracy is *** test the proposed method’s performance against eleven other state-of-theart approaches,eight datasets from the UCI repository were used,such as binary grey wolf search(bGWO),binary hybrid grey wolf,and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hysteresis optimization(bHy),and binary hysteresis optimization(bHWO).The suggested method is superior 4532 CMC,2023,vol.74,no.2 and successful in handling the problem of feature selection,according to the results of the experiments.

关键词： Metaheuristics dipper throated optimization grey wolf optimization binary optimizer feature selection

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Performance Analysis of Existing SDN Load Balancing Controller Using Mininet 1

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International Conference on Advanced engineering, Technology and Applications, ICAETA 2024

作者： Baniya, Pashupati Nand, Parma Bhushan, Bharat Hameed, Alaa Ali Jamil, Akhtar Agrawal, Atul Department of Computer Science and Engineering School of Engineeringand Technology IEC College of Engineering and Technology Greater Noida India Department of Computer Science and Engineering School of Engineering and Technology ITS Engineering College Greater Noida India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University Istanbul34396 Turkey Department of Computer Science National University of Computer and Emerging Sciences Islamabad44000 Pakistan

ISBN: (数字)9783031709241

ISBN: (纸本)9783031709234

Due to an increase in the load of network, load balancing service, i.e., a service that gives an equal volume of each task assignment to each of the servers in data centers, it is usually performed by the specialized hardware. Networks have to adjust due to the load and also as a result of dealing with faults and complexity which are provided in the implementation and configuration process. Network engineers or administrators must be aware of selecting controllers according to the network requirement, which can lead to performance issues, scalability challenges, reliability and fault tolerance and many more problems in the network. To tackle this problem, this research analyses four different controllers in an experiment: In-band, open flow, OVS, and remote controller, which are the key aspects. The paper shows how these controllers, which are detected through a comprehensive evaluation of network performance attributes (e.g., Throughput and Jitter), affect the overall system’s performance. This information provides beneficial information for the researchers and practitioners who wish to make informed choices whenever they select suitable controls for specific network needs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Remote control

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Optimized Deep Learning Approach for Efficient Diabetic Retinopathy Classification Combining VGG16-CNN

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computers, Materials & Continua 2023年第11期77卷 1855-1872页

作者： Heba M.El-Hoseny Heba F.Elsepae Wael A.Mohamed Ayman S.Selmy Department of Computer Science the Higher Future Institute for Specialized Technological StudiesObour11828Egypt Department of Electrical Engineering Benha Faculty of EngineeringBenha UniversityBenha13511Egypt

Diabetic retinopathy is a critical eye condition that,if not treated,can lead to vision *** methods of diagnosing and treating the disease are time-consuming and ***,machine learning and deep transfer learning(DTL)techniques have shown promise in medical applications,including detecting,classifying,and segmenting diabetic *** advanced techniques offer higher accuracy and *** Diagnosis(CAD)is crucial in speeding up classification and providing accurate disease ***,these technological advancements hold great potential for improving the management of diabetic *** study’s objective was to differentiate between different classes of diabetes and verify the model’s capability to distinguish between these *** robustness of the model was evaluated using other metrics such as accuracy(ACC),precision(PRE),recall(REC),and area under the curve(AUC).In this particular study,the researchers utilized data cleansing techniques,transfer learning(TL),and convolutional neural network(CNN)methods to effectively identify and categorize the various diseases associated with diabetic retinopathy(DR).They employed the VGG-16CNN model,incorporating intelligent parameters that enhanced its *** outcomes surpassed the results obtained by the auto enhancement(AE)filter,which had an ACC of over 98%.The manuscript provides visual aids such as graphs,tables,and techniques and frameworks to enhance *** study highlights the significance of optimized deep TL in improving the metrics of the classification of the four separate classes of *** manuscript emphasizes the importance of using the VGG16CNN classification technique in this context.

关键词： No diabetic retinopathy(NDR) convolution layers(CNV layers) transfer learning data cleansing convolutional neural networks a visual geometry group(VGG16)

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Forensic Investigation of Malicious Activities in Digital Environments 4

Forensic Investigation of Malicious Activities in Digital En...

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4th IEEE International Conference on Data engineering and Communication systems, ICDECS 2024

作者： Pasumpon Pandian, A. Anakath, A.S. Kannadasan, R. Ravikumar, K. Abdul Kareem, D. Care College of Engineering Department of Computer Science and Engineering Trichy India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India School of Computer Science and Engineering Department of Software Systems Tamil Nadu Vellore India Rrase College of Engineering Padappai Department of Computer Science & Engineering Chennai India Grt Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Tiruttani India

ISBN: (纸本)9798350393354

Several digital dangers were investigated. Malware dominated analysis with 45 attacks. We found 30 phishing attacks. 22 data breaches, 15 cyber espionage, 18 identity theft. This indicates the kind and frequency of harassment digital forensic specialists endure. Digital evidence was crucial to the case. 65 pieces of evidence were collected on computers. 42 incidents showed smartphones and tablets' digital investigative potential. There were 55 examples of communication patterns requiring network logs. In 28 and 12, cloud services and removable media provided proof. A wide range of evidence sources stresses the need for extensive collecting. Digital forensics were essential to the inquiry. In 70% of cases, disk imaging software created forensic copies of metadata-and timestamp-containing storage media. Network packet analyzers examined 48 traffic logs and communication patterns. To analyze volatile memory, 35 cases used memory forensics. In 25 cases, malware analysis platforms decrypted malware. In 60 situations, file recovery software recovered lost or hidden files. Digital forensic specialists' diverse arsenal for thorough investigations is shown here. Legal and ethical considerations dominated the study. The law was important, and all inquiries followed norms. A strong chain of custody protects evidence integrity and admissibility. The data privacy rules were respected. Legal evidence is admissible in court. Ethics ensured objective investigations. This underlines digital forensic investigations' legal and ethical foundation. © 2024 IEEE.

关键词： Cybersecurity

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A Parallel Color Image Encryption Algorithm Based on a 2D Logistic-Rulkov Neuron Map

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IEEE Internet of Things Journal 2025年第11期12卷 18115-18124页

作者： Gao, Suo Zhang, Zheyi Ho-Ching Iu, Herbert Ding, Siqi Mou, Jun Erkan, Uğur Toktas, Abdurrahim Li, Qi Wang, Chunpeng Cao, Yinghong The School of Information Science and Engineering Dalian Polytechnic University Dalian116034 China School of Electrical Electronic and Computer Engineering The University of Western Australia Perth6009 Australia School of Mathematical Sciences Dalian University of Technology Dalian116024 China Department of Computer Engineering Faculty of Engineering Karamanoglu Mehmetbey University Karaman70200 Turkey Department of Artificial Intelligence and Data Engineering Engineering Faculty Ankara University Ankara06830 Turkey Jinan250000 China

Images are widely used in social networks, necessitating efficient and secure transmission, especially in bandwidth-constrained environments. This paper aims to develop a color image encryption algorithm that enhances security while optimizing computational efficiency. A novel parallel color image encryption algorithm based on the 2D Logistic-Rulkov neuron map (2D-LRNM) is proposed. In this approach, the three channels of the color image are first separated. Cross-channel information interaction is introduced to form three new channels, which are then processed in parallel. During the encryption process of each channel, a block-wise parallel encryption mechanism is applied, ensuring simultaneous encryption of each block. This block-wise strategy effectively leverages parallel computing resources and balances the task load. To meet the demand for a large number of keystreams during encryption, the 2D-LRNM is introduced. It combines the simplicity and chaotic properties of the Logistic map with the multi-timescale dynamics and neurodynamic behaviors of the Rulkov map. By overcoming the dimensional limitations inherent in the single Logistic map, this approach extends the system to a two-dimensional framework, significantly increasing the complexity of chaotic behavior and improving its unpredictability. Experimental results demonstrate that the proposed encryption algorithm achieves high security and reduces computation time by approximately 83.3%. © 2014 IEEE.

关键词： Encryption algorithms

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A Resource-Efficient Multi-core Multi-thread RISC-V-based System-on-Chip 21

A Resource-Efficient Multi-core Multi-thread RISC-V-based Sy...

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Kieu-Do-Nguyen, Binh Nguyen, Khai-Duy Binh, Nguyen The Dang, Tuan-Kiet Le, Duc-Hung Pham-Quoc, Cuong Tran, Ngoc-Thinh Pham, Cong-Kha Hoang, Trong-Thuc Department of Computer and Network Engineering Tokyo Japan Faculty of Computer Science and Engineering Hochiminh Viet Nam Faculty of Electronics and Telecommunications Hochiminh Viet Nam

ISBN: (纸本)9798350377088

In the past, a small size microcontroller was needed to control and gather data from resource-constrained electronic devices aimed at Internet of Things (IoT) applications. While the server nodes receive the requests with a high demand for calculation. However, to minimize network traffic transactions and the overall workload that the server node must handle, a portion of these operations must now be completed locally. Additionally, multiple tasks must be processed simultaneously by IoT devices. In this study, we suggested a multi-core, multi-thread enable System-on-Chip (SoC) that consume small resources. The suggested architecture achieves a notable speed increase over single-core design by utilizing the advantages of RISC-V Instruction Set Architecture (ISA). © 2024 IEEE.

关键词： System-on-chip

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