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2024 IEEE International Conference on Big Data, BigData 2024

作者： Wang, Yajing Hu, Jiazhen Luo, Zongwei Hong Kong Baptist University BNU-HKBU United International College Department of Computer Science Zhuhai China BNU-HKBU United International College Department of Computer Science Zhuhai China Beijing Normal University BNU-HKBU United International College BNU-UIC Institute of Ai and Future Networks Zhuhai China

ISBN: (纸本)9798350362480

Prompt engineering in Large Language Models (LLMs) has become pivotal for task adaptation, especially in complex reasoning tasks where traditional methods fall short. This paper introduces an innovative method to optimize prompt efficiency by constructing a decision tree of the Chain of Thought (CoT) for classification tasks, namely CoT-Tree. Initially, we demonstrate two advantages of using CoT as features for decision-tree prompts: task adaptability and semantic dissimilarity between features. These advantages theoretically ensure that the CoT-Tree prompting strategy achieves higher performance in classification tasks. Subsequently, we propose the CoT-Tree algorithm and apply it on three classic classification scenarios. By leveraging the CoT-Tree prompting strategy, we select distinctive and efficient prompts that maximize task relevance while minimizing redundancy and token consumption, allowing for a dynamic pruning mechanism, effectively controlling the depth and complexity of the prompts. The results indicate that our method achieves higher accuracy with fewer LLM interaction rounds. Additionally, a case study is conducted to further demonstrate the practicality of our method. © 2024 IEEE.

关键词： Semantics

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Empowering Diagnosis: Cutting-Edge Segmentation and Classification in Lung Cancer Analysis

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computers, Materials & Continua 2024年第6期79卷 4963-4977页

作者： Iftikhar Naseer Tehreem Masood Sheeraz Akram Zulfiqar Ali Awais Ahmad Shafiq Ur Rehman Arfan Jaffar Faculty of Computer Sciences&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Information System Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Lung cancer is a leading cause of global mortality *** detection of pulmonary tumors can significantly enhance the survival rate of ***,various computer-Aided Diagnostic(CAD)methods have been developed to enhance the detection of pulmonary nodules with high ***,the existing method-ologies cannot obtain a high level of specificity and *** present study introduces a novel model for Lung Cancer Segmentation and Classification(LCSC),which incorporates two improved architectures,namely the improved U-Net architecture and the improved AlexNet *** LCSC model comprises two distinct *** first stage involves the utilization of an improved U-Net architecture to segment candidate nodules extracted from the lung ***,an improved AlexNet architecture is employed to classify lung *** the first stage,the proposed model demonstrates a dice accuracy of 0.855,a precision of 0.933,and a recall of 0.789 for the segmentation of candidate *** suggested improved AlexNet architecture attains 97.06%accuracy,a true positive rate of 96.36%,a true negative rate of 97.77%,a positive predictive value of 97.74%,and a negative predictive value of 96.41%for classifying pulmonary cancer as either benign or *** proposed LCSC model is tested and evaluated employing the publically available dataset furnished by the Lung Image Database Consortium and Image Database Resource Initiative(LIDC-IDRI).This proposed technique exhibits remarkable performance compared to the existing methods by using various evaluation parameters.

关键词： Lung cancer segmentation AlexNet U-Net classification

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Graph Convolutional Neural Network Based Malware Detection in IoT-Cloud Environment

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Intelligent Automation & Soft Computing 2023年第6期36卷 2897-2914页

作者： Faisal SAlsubaei Haya Mesfer Alshahrani Khaled Tarmissi Abdelwahed Motwakel Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16242Saudi Arabia

Cybersecurity has become the most significant research area in the domain of the Internet of Things(IoT)owing to the ever-increasing number of *** rapid penetration of Android platforms in mobile devices has made the detection of malware attacks a challenging ***,Android malware is increasing on a daily ***,precise malware detection analytical techniques need a large number of hardware resources that are signifi-cantly resource-limited for mobile *** this research article,an optimal Graph Convolutional Neural Network-based Malware Detection and classification(OGCNN-MDC)model is introduced for an IoT-cloud *** pro-posed OGCNN-MDC model aims to recognize and categorize malware occur-rences in IoT-enabled cloud *** presented OGCNN-MDC model has three stages in total,such as data pre-processing,malware detection and para-meter *** detect and classify the malware,the GCNN model is exploited in this *** order to enhance the overall efficiency of the GCNN model,the Group Mean-based Optimizer(GMBO)algorithm is utilized to appropriately adjust the GCNN parameters,and this phenomenon shows the novelty of the cur-rent study.A widespread experimental analysis was conducted to establish the superiority of the proposed OGCNN-MDC model.A comprehensive comparison study was conducted,and the outcomes highlighted the supreme performance of the proposed OGCNN-MDC model over other recent approaches.

关键词： Cybersecurity IoT cloud malware detection graph convolution network

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Arabic Sign Language Recognition Using EfficientnetB1 and Transfer Learning Technique

Arabic Sign Language Recognition Using EfficientnetB1 and Tr...

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2023 International Conference on IT Innovation and Knowledge Discovery, ITIKD 2023

作者： Dabwan, Basel A. Jadhav, Mukti E. Ali, Yahya A. Olayah, Fekry A. Dr. Babasaheb Ambedkar Marathwada University Department of Computer Science Aurangabad India Shri Shivaji Science & Art College Department of Computer Science Buldhana India Najran University Department of Information System Najran Saudi Arabia

ISBN: (纸本)9781665463720

Deaf and mute people rely on signing as a means of communication with others and with themselves. The value of sign language, which is the sole way for the deaf and mute communities to communicate, is often overlooked by regular people. Because of these limitations or impairments, these people are experiencing considerable setbacks in their life, including joblessness, serious depression, and a variety of other symptoms. Sign language interpreters are one of the communication services they use. However, paying these interpreters is prohibitively expensive, necessitating a low-cost solution to the problem. As a result, a system has been built that would interpret the Arabic Sign Language-based visual hand dataset into written information. The dataset used for this model Arabic Alphabets Sign Language dataset consists of 32 classes, each category has 506 images, resulting in a total of 506 ∗ 32 = 16192 images. On the provided dataset, the tests have been run using a variety of pre-trained models. Most of them carried out their duties rather normally, and in the end, we constructed the Convolutional Neural networks model with EfficientnetB1 scaling loaded with weights pre-trained on the ImageNet model, Based on this observation. Using a simple but highly effective compound coefficient, it equally scales all width/depth/resolution dimensions. Based on the model's results, we demonstrated the efficacy of this strategy. The percentage of accuracy that we obtained from this model is 99% accuracy and 97.9% validation accuracy © 2023 IEEE.

关键词： Convolutional neural networks

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Robust Method for Embedding an Image Inside Cover Image Based on Least Significant Bit Steganography

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Informatica (Slovenia) 2022年第9期46卷 53-60页

作者： Almola, Sahera A.S. Qasim, Najat H. Ali Abed Alasadi, Hamid Department of Information System College of Computer Sciences and Information Technology University of Basrah Iraq Department of Computer Science College of Education for Pure Sciences University of Basrah Iraq Communications Engineering Department Iraq University College Istqlal Street Basra61004 Iraq

According to the enormous evolution of the internet and communications, the security of private or sensitive information has become the important issue. The private information can be passed between a sender and a receiver with high security using Cryptography and Steganography. By the cryptography, as the information can be secured with the cipher key for encryption. By the Steganography, the secret information can hide within usual data such as images, videos, audios. In this paper we proposed the robust method that includes two phases. The first phase consists of two stages. In the first stage we encrypt a secret image using encryption algorithm in order of increasing security of information. In the second stage, we complete the first stage for embedding the most significant bits (MSB) of the encrypted image into the least significant bits (LSB) of the cover image for instituting the stego image. The second phase is the obverse of the first phase, where, the encrypted image is retrieved from the stego image and then, decrypted process construct the secret image. The proposed method is efficient in terms of security compare with the other methods. Where, both encryption and embedding algorithms have an efficient role for embedding the secret image and preserving the good visual quality of stego images. Furthermore, extraction algorithms obtained better and faster results to a restoration of the secret image. © 2022 Slovene Society Informatika. All rights reserved.

关键词： Steganography

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UQU GIS-based Navigation system 14

UQU GIS-based Navigation System

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14th IEEE International Conference on Computational Intelligence and Communication networks, CICN 2022

作者： Obaidi, Shoaib Shahzad Tarmissi, Khalid Shalan, Atef Alotaibi, Saud S. College of Computer Science and Information System Computer Science Department Makkah Saudi Arabia College of Engineering and Computing Engineering Department Georgia United States

ISBN: (纸本)9781665487719

Umm al Qura University is one of the largest universities in the kingdom of Saudi Arabia, having more than one hundred thousand students. The university is situated in a suburb area of Makkah city;around 10 km away from the residential areas, and because of its geographic location, most of the people who are either students, professors, staff, workers, or even visitors enter the university compound daily for different purposes;like studying, teaching, working or perhaps just for a tour, and they all get into the university using vehicles. So, because of the vast area of the university which embeds tens of faculty buildings, scientific and technical research centers, and many other facilities, many students and visitors cannot reach their desired locations at an appropriate time. The problem gets doubled in the first few days of the university startup, and that's because of freshmen students' enrollment. To solve this problem, an online navigation system is built to help people find their destinations quickly. The platform acts as an outdoor kind of navigation system that shows the routes for buildings based on the user's search input or selection, then relatively the system will show the exact vehicle routes to the predefined appropriate parking lot, with a pedestrian path stretching to the destination and some statistical information about the buildings along with some panoramic photos of the classes are shown on the right side of the map. © 2022 IEEE.

关键词： Geographic information systems

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Compact Bat Algorithm with Deep Learning Model for Biomedical EEG EyeState Classification

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computers, Materials & Continua 2022年第9期72卷 4589-4601页

作者： Souad Larabi-Marie-Sainte Eatedal Alabdulkreem Mohammad Alamgeer Mohamed K Nour Anwer Mustafa Hilal Mesfer Al Duhayyim Abdelwahed Motwakel Ishfaq Yaseen Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Electroencephalography(EEG)eye state classification becomes an essential tool to identify the cognitive state of *** can be used in several fields such as motor imagery recognition,drug effect detection,emotion categorization,seizure detection,*** the latest advances in deep learning(DL)models,it is possible to design an accurate and prompt EEG EyeState classification *** this view,this study presents a novel compact bat algorithm with deep learning model for biomedical EEG EyeState classification(CBADL-BEESC)*** major intention of the CBADL-BEESC technique aims to categorize the presence of EEG *** CBADL-BEESC model performs feature extraction using the ALexNet model which helps to produce useful feature *** addition,extreme learning machine autoencoder(ELM-AE)model is applied to classify the EEG signals and the parameter tuning of the ELM-AE model is performed using *** experimental result analysis of the CBADL-BEESC model is carried out on benchmark results and the comparative outcome reported the supremacy of the CBADL-BEESC model over the recent methods.

关键词： Biomedical signals EEG EyeState classification deep learning metaheuristics

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MLPFormer: MLP-integrated transformer for colorectal histopathology whole slide image segmentation

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Neural Computing and Applications 2025年第6期37卷 4651-4661页

作者： Wang, Yuxuan Li, Dan Li, Xuechen Guo, Yan Zuo, Yanfei Shen, Linlin Department of Computer Science Wenzhou-Kean University Wenzhou China School of Electronics and Information Engineering Wuyi University Jiangmen China Department of Histo Pathology Shanghai Histo Pathology Diagnostic Center Shanghai China College of Computer Sicence and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Colorectal intraepithelial neoplasia is a precancerous lesion of colorectal cancer, which is mainly diagnosed using pathological images. According to the characteristics of lesions, precancerous lesions can be classified into four different grades, i.e., background, normal, low and high level. However, due to the continuity of lesion development, tissue in different stages has high similarity. Recently, visual transformers have achieved impressive results in different visual tasks, due to their capacity of capturing long-range dependencies. However, due to computational cost, transformer cannot well extract detailed feature, which is important for lesion grading. To accurately segment pathological tissues with high similarity, in this work, we embed the multi-head MLP mixer module in the transformer network to extract cell and edge information. Furthermore, we propose a hierarchical MLP decoder to facilitate multi-scale feature fusion. The proposed transformer, namely MLPFormer, achieves remarkable results against competitive baselines on the Histo-CRC Biopsy dataset. The experimental results demonstrate that MLPFormer significantly outperforms the competitive baselines, i.e., a 3% dice improvement is achieved over the SegFormer series. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image segmentation

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A Numerical Model Oriented Large-scale Parallel I/O Optimization Method 6

A Numerical Model Oriented Large-scale Parallel I/O Optimiza...

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6th International Conference on Data Storage and Data Engineering, DSDE 2023

作者： Zhang, Lei Wu, Tao Zhou, Zheng Wang, Dong Chen, Xi School of Computer Science Chengdu University of Information Technology Chengdu China Tsinghua University Department of Earth System Science Beijing China College of Computer Southwest Minzu University Chengdu China

ISBN: (纸本)9798350333145

Numerical models usually contain a large number of data reading and writing, which generally takes a long time. At present, massively parallel technology has accelerated the speed of computing in model, but due to the limitation of computer I/O ability, the acceleration effect is limited, and there is a serious load imbalance during the model operation process, resulting in a waste of computing resources. To deal with that, this paper proposes a new I/O scheme, which can dynamically optimize task allocation during program operation, make full use of computing resources, and increase the mutual coverage of computing and I/O processes, so as to effectively shorten the model running time. Finally, the test in the earth simulator shows that the new I/O scheme improves the operation speed of the regional high-precision air pollution model by 25.45%. © 2023 IEEE.

关键词： Numerical models

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Deep Learning Enabled Disease Diagnosis for Secure Internet of Medical Things

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computers, Materials & Continua 2022年第10期73卷 965-979页

作者： Sultan Ahmad Shakir Khan Mohamed Fahad Al.Ajmi Ashit Kumar Dutta L.Minh Dang Gyanendra Prasad Joshi Hyeonjoon Moon Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj11942Saudi Arabia College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia College of Pharmacy King Saud UniversityRiyadh11451Saudi Arabia Department of Computer Science and Information System College of Applied SciencesAlMaarefa UniversityRiyadh11597Saudi Arabia Department of Information Technology FPT UniversityHo Chi Minh CityVietnam Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea

In recent times,Internet of Medical Things(IoMT)gained much attention in medical services and healthcare management *** healthcare sector generates massive volumes of data like personal details,historical medical data,hospitalization records,and discharging records,IoMT devices too evolved with potentials to handle such high quantities of *** and security of the data,gathered by IoMT gadgets,are major issues while transmitting or saving it in *** advancements made in Artificial Intelligence(AI)and encryption techniques find a way to handle massive quantities of medical data and achieve *** this view,the current study presents a new Optimal Privacy Preserving and Deep Learning(DL)-based Disease Diagnosis(OPPDL-DD)in IoMT ***,the proposed model enables IoMT devices to collect patient data which is then preprocessed to optimize *** order to decrease the computational difficulty during diagnosis,Radix Tree structure is *** addition,ElGamal public key cryptosystem with Rat Swarm Optimizer(EIG-RSO)is applied to encrypt the *** the transmission of encrypted data to cloud,respective decryption process occurs and the actual data gets ***,a hybridized methodology combining Gated Recurrent Unit(GRU)with Convolution Neural Network(CNN)is exploited as a classification model to diagnose the *** sets of simulations were conducted to highlight the performance of the proposed model on benchmark *** experimental outcomes ensure that the proposed model is superior to existing methods under different measures.

关键词： Internet of medical things privacy security encryption radix tree deep learning

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