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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： S. Saravanan K. Muthumanickam N. Subha P.C.Senthil Mahesh Department of CSE-Data Science Dayananda Sagar Academy of Technology and Management Bengaluru India Department of Information Technology Kongunadu College of Engineering and Technology (Autonomous) Thottiyam Tamilnadu India Department of Computer Science and Engineering Knowledge Institute of Technology Salem Tamilnadu India Department of Computer Science and Engineering Excel Engineering College Komarapalayam Tamilnadu India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

The neurodegenerative disease known as Alzheimer's disease (AD), which affects people worldwide, is complicated to treat and expensive. Conventional approaches, including manual Support Vector Machine (SVM) based methods with separate feature extraction and preprocessing, lack automation and pose limitations. This project recognizes the pressing need for efficient and automated solutions. Building on existing work that employed SVM and manual processing for Alzheimer's diagnosis, this work proposes a novel approach. Utilizing brain MRI scans and the VGG16 architecture, our automated system achieves high accuracy in predicting both labeled and unlabeled data. The integration of VGG16 ensures a streamlined process, eliminating manual intervention in feature extraction and preprocessing. This automation enhances accessibility, reduces costs, and offers a user-friendly interface for users. In contrast to traditional methods, our proposed system allows users to input MRI scans, facilitating prompt and accurate predictions. Upon analysis, the proposed system provides doctor recommendations and suggests prescriptions. The VGG16-based approach not only ensures superior diagnostic accuracy but also streamlines the entire process, emphasizing the importance of early detection for improved outcomes in Alzheimer's disease management.

关键词： Support vector machines Accuracy Automation Magnetic resonance imaging Manuals Pressing Streaming media

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Evaluation of Modified VGG16 Learning Model for Classifying Skin Cancer Lesions

Evaluation of Modified VGG16 Learning Model for Classifying ...

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Network, Multimedia and information Technology (NMITCON) International Conference on

作者： Mateen Naser Alaubaidy Abdulqadir Ismail Abdullah Fatimah Mousa Alwan Department of Information Technology College of Engineering and Computer Science Lebanese French University Erbil Kurdistan Region Iraq Department of Computer Engineering College of Engineering Knowledge University Erbil Kurdistan Region Iraq Department of Computer Science College of Engineering and Computer Science Bayan University Erbil Kurdistan Region Iraq

One of the most serious types of cancer that is prevalent around the world is skin cancer. It is widely known that skin cancer characterized by uncontrolled cell and tissue growth, presenting a challenge in detecting or diagnosis skin cancer in early stage. Accurate segmentation of skin lesions in dermatoscopic images holds great importance in diagnosis and assessment of skin cancer. Thus, Fast, precise, and real-time algorithms are essential in effectively detecting skin cancer to aid physicians in their treatment decision-making process. In this paper, we evaluated the performance of the modified VGG16 with using three common convolutional neural networks(CNN) in detecting the skin cancer with dermoscopic skin lesions images. In this study, the Kaggle dataset is used to classify skin cancer images. To classify skin lesions, the model modified VGG16 is compared to the most common models such as VGG16, mobileNetV2 and Inception ResNet v2. All models are trained on the same dataset and their performance is evaluated using metrics such as: accuracy, precision, F1-score, and recall. Finally, The comparative analysis revealed a promising performance of the modified VGG16 model in detecting multiclass skin cancer, which highlights its promising potential in classifying.

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Computational Intelligence in Business Management: Strategies for Innovation and Optimization 6

Computational Intelligence in Business Management: Strategie...

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6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Ahmed, Saadaldeen Rashid Hussein, Ali Jabbar ALhashmi, Lubna Qassim Al-Attar, Bourair Dheeb, Shaymaa Majeed, Duaa A. Hussain, Abadal-Salam T. Tawfeq, Jamal Fadhil Computer Science Department Bayan University Kurdistan Erbil Iraq Artificial Intelligence Engineering Department College of Engineering Al-Ayen University Thi-Qar Iraq Al Hikma University College Baghdad Iraq Department of information and knowledge technologies College of Arts Basrah University Basrah Iraq College of Medicin University of Al-Ameed Karbala PO Box 198 Iraq Department of Cyber Security and Cloud Computing Al-Ma’moon University College Al-Washash Baghdad Iraq Aeronautical Engineering Department Baghdad University Baghdad Iraq College of Technical Engineering National University of Science and Technology Dhi Qar Iraq Department of Medical Instrumentation Techniques Engineering Technical Engineering College Al-Kitab University Altun Kupri Kirkuk Iraq Department of Medical Instrumentation Technical Engineering Medical Technical College Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350394634

In today's competitive corporate landscape, the successful integration of computational intelligence approaches provides a key edge for decision-making processes. Despite developments, there is a gap in understanding the subtle application of such methodologies in corporate management, particularly within retail environments. To address this gap, our study combines decision trees and artificial neural networks to evaluate retail transaction data, concentrating on customer behavior and purchase trends. Leveraging a comprehensive dataset spanning client demographics, transaction details, and product information, our study provides substantial insights. Results suggest an 87% accuracy rate reached by artificial neural networks in forecasting client preferences. This study contributes to bridging the research gap by giving actionable insights into the practical application of computational intelligence in boosting company strategies and decision-making processes within the retail industry. © 2024 IEEE.

关键词： Decision trees

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Intrusion Detection in 5G Cellular Network Using Machine Learning

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computer Systems Science & engineering 2023年第11期47卷 2439-2453页

作者： Ishtiaque Mahmood Tahir Alyas Sagheer Abbas Tariq Shahzad Qaiser Abbas Khmaies Ouahada Knowledge Unit of Systems and Technology UMT Sialkot CampusSialkot51040Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Faculty of Computer Science National College of Business Administration and EconomicsLahore54660Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Faculty of Computer and Information Systems Islamic University of Madinah Madinah42351Saudi Arabia Department of Computer Science&IT University of SargodhaSargodha40100Pakistan Department of Electrical and Electronic Engineering Science University of JohannesburgP.O Box 524Auckland ParkJohannesburg2006South Africa

Attacks on fully integrated servers,apps,and communication networks via the Internet of Things(IoT)are growing *** devices’effectiveness harms end users,increases cyber threats and identity theft,raises costs,and negatively impacts income as problems brought on by the Internet of Things network go unnoticed for extended *** on Internet of Things interfaces must be closely monitored in real time for effective safety and *** the 1,2,3,and 4G cellular networks,the 5th generation wireless 5G network is indeed the great invasion of mankind and is known as the global advancement of cellular *** to this day,experts are working on the evolution’s sixth generation(6G).It offers amazing capabilities for connecting everything,including gadgets and machines,with wavelengths ranging from 1 to 10 mm and frequencies ranging from 300 MHz to 3 *** gives you the most recent *** countries have already established this technology within their *** is the most crucial aspect of using a 5G *** of the absence of study and network deployment,new technology first introduces new gaps for attackers and *** Protocol(IP)attacks and intrusion will become more prevalent in this *** efficient approach to detect intrusion in the 5G network using a Machine Learning algorithm will be provided in this *** research will highlight the high accuracy rate by validating it for unidentified and suspicious circumstances in the 5G network,such as intruder hackers/*** applying different machine learning algorithms,obtained the best result on Linear Regression Algorithm’s implementation on the dataset results in 92.12%on test data and 92.13%on train data with 92%precision.

关键词： Intrusion detection system machine learning confidentiality integrity availability

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U-HRMLP: Refining Segmentation Boundaries in Histopathology Images

U-HRMLP: Refining Segmentation Boundaries in Histopathology ...

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IEEE International Symposium on Biomedical Imaging

作者： Tao Wang Kai Zhang Weijie Wang Mingrui Ma Ye Zhang He Zhao Guixia Liu College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China School of Information Science and Engineering Shandong Normal University China Department of Information Engineering and Computer Science University of Trento Italy Faculty of Information Engineering and Automation Kunming University of Science and Technology China School of Information Science and Technology Northeast Normal University China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Segmenting regions of interest in histopathology images is a critical prerequisite for biomedical applications. CNN-based models have advanced medical image segmentation, yet accurately delineating boundaries is still challenging. This challenge arises from the loss of spatial resolution due to downsampling and the complexity of capturing contextual information when upsampling to the original resolution. To address these issues, we design U-HRMLP, a U-shaped network based on a hybrid combination of HRNet and Multi-Layer Perceptron (MLP). Specifically, we propose a multi-scale feature fusion module to avoid the loss of image resolution in the encoding stage as much as possible. Simultaneously, we propose an MLP-based decoding block to recover image details and resolution progressively, alleviating the issues related to the inadequate segmentation of smaller objects and the blurring of tissue boundaries. Results on the MoNuSeg and GlaS datasets demonstrate that U-HRMLP outperforms conventional or transformer-based methods and achieves state-of- the-art performance.

关键词： Measurement Image segmentation Histopathology Biological system modeling Semantics Refining Transformers

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A Real-Time Hand Sign Language Recognition System for Threatening Situations Using Deep Learning

A Real-Time Hand Sign Language Recognition System for Threat...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Zardasht Abdulaziz Abdulkarim Shwany Shayda Khudhur Ismail Karwan Hoshyar Khalid Khoshnaw Twana Saeed Mustafa Murat Karabatak Nashwan Adnan Othman Physiotherapy Department Erbil Polytechnic University Erbil Iraq Network Department Ministry of Higher Education and Scientific Research Erbil Iraq Department of Information and Communication Technology Engineering Erbil Polytechnic University Erbil Iraq Department of Software Firat University Elazig Turkey Department of Computer Science College of Science Knowledge University Erbil Iraq Department of Computer Engineering Knowledge University Erbil Iraq Department of Computer Engineering Al-Kitab University Altun Kupri Iraq

ISBN: (数字)9798350330366

ISBN: (纸本)9798350330373

Hand sign language has been done as physical movements in natural languages that humans have used since ancient times, along with letters, words, and spoken language. This paper presents a real-time method to identify threatening signs by criminals during interrogation, which increases their criminal rank and helps to reach conclusions. The proposed method is to install a camera of appropriate quality in front of the offender, record hand gestures in a specific area of the hand, apply some image processing techniques, such as contrast enhancement techniques, to the image to facilitate recognition as input, and then classify the images using a convolutional neural network (CNN) for a specific problem with high specifications for that topic by and using AlexNet. The accuracy of the presented method is more than 94.2% in real-time testing.

关键词： Deep learning Sign language Image recognition Natural languages Digital forensics Cameras Real-time systems

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Advanced Deep Reinforcement Learning Strategies for Enhanced Autonomous Vehicle Navigation Systems

Advanced Deep Reinforcement Learning Strategies for Enhanced...

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computer, Communication and Control (IC4), International Conference on

作者： M Dhinakaran R.Thalapathi Rajasekaran V. Balaji V. Aarthi S. Ambika Department of Electronics and Communication Engineering Galgotias College of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computer Science and Engineering Easwari Engineering College Chennai India Department of Computer Science and Engineering GRT Institute of Engineering and Technology Tiruttani Tamil Nadu India Department of Information Technology E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India

The objective of this research is to investigate more sophisticated techniques in Deep Reinforcement Learning (DRL) for the purpose of improving navigation systems for autonomous vehicles. The results of our research indicate that the success rates of DRL algorithms are encouraging: DQN (75.2%), PPO (82.6%), and TRPO (78.1%). The success of navigation is highly influenced by optimisation factors such as parameter tuning (87.4%), exploration-exploitation balance (85.1%), and reward shaping (88.9%). The generalisation score for transfer learning is rather good (92.3%), while the safety modules are designed to make the system safer. These findings highlight the significance of algorithm selection, parameter optimisation, and advanced techniques in the process of optimising navigation for autonomous vehicles, hence establishing essential benchmarks for future developments.

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Prompt Learning to Mitigate Catastrophic Forgetting in Cross-lingual Transfer for Open-domain Dialogue Generation

arXiv

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arXiv 2023年

作者： Liu, Lei Huang, Jimmy Xiangji Information Retrieval and Knowledge Management Research Lab Department of Electrical Engineering & Computer Science York University Toronto Canada Information Retrieval and Knowledge Management Research Lab School of Information Technology York University Toronto Canada

Dialogue systems for non-English languages have long been under-explored. In this paper, we take the first step to investigate few-shot cross-lingual transfer learning (FS-XLT) and multitask learning (MTL) in the context of open-domain dialogue generation for non-English languages with limited data. We observed catastrophic forgetting in both FS-XLT and MTL for all 6 languages in our preliminary experiments. To mitigate the issue, we propose a simple yet effective prompt learning approach that can preserve the multilinguality of multilingual pre-trained language model (mPLM) in FS-XLT and MTL by bridging the gap between pre-training and fine-tuning with Fixed-prompt LM Tuning and our hand-crafted prompts. Experimental results on all 6 languages in terms of both automatic and human evaluations demonstrate the effectiveness of our approach. Our code is available at https://***/JeremyLeiLiu/XLinguDial. Copyright © 2023, The Authors. All rights reserved.

关键词： Linearization

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Academic Evaluation System Using Adaptive Categorized and Authenticated Recommendation

Academic Evaluation System Using Adaptive Categorized and Au...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Haider Abdula Haddad Abdulqadir Ismail Abdullah Qaysar Salih Mahdy Wasfi Kahwachi Department of computer science and information technology College of Science Salahaddin University Erbil Kurdistan Region Iraq Department of Computer Engineering College of Engineering Knowledge University Erbil Kurdistan Region Iraq Department of Mechatronics Engineering College of Engineering Tishk International University Erbil Kurdistan Region Iraq Research Center Tishk International University Erbil Kurdistan Region Iraq

ISBN: (数字)9798350374131

ISBN: (纸本)9798350374148

Teacher performance is an important issue for any academic institution, so these institutions try to develop methodologies and systems to evaluate the performance of its academic staff based on measurable and scientific metrics. These metrics include collecting data from students about classes and how the teacher is performing in the classroom and measuring the learning objectives from students. Major objective of this research work is to develop a web-based multi-user system for academic performance and a recommendation system that depends on the performance and skills of the teacher. The system includes multiple entry points for best factor such as the feedback unit, student attendance unit, teacher classification unit, and teacher recommendation unit, which can be strictly accessed by the administrative authority for academic teachers. The attendance module and the student feedback module are required to achieve better results in the teacher rating module and the teacher recommendation module. A reliability factor has been incorporated to achieve the accuracy of the proposed system. The performance of the system was improved due to the integration of the paperless approach and resulted in less data loss. The developed system was tested for a large university in Kurdistan region, Iraq. For data analysis an adaptive unsupervised learning method was used to categorize teachers according to their performance in the classroom. The system proved very successful in the academic evaluation and recommendation process in the university. This research proved that using student feedback data is the best factor in determining academic performance.

关键词： Performance evaluation Data analysis Adaptive systems Education Data collection Reliability Task analysis

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Performance Evaluation of Application Layer IoT Protocols: MQTT and CoAP

Performance Evaluation of Application Layer IoT Protocols: M...

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International Conference on computer, Communication, Control and information Technology (C3IT)

作者： Manika Debnath Subrata Modak Dhrubasish Sarkar Department of Computer Science & Engineering Supreme Knowledge Foundation Group of Institutions Mankundu West Bengal India Computer Science & Information Technology Supreme Institute of Management and Technology Mankundu West Bengal India

ISBN: (数字)9798350389128

ISBN: (纸本)9798350389135

The Internet of Things (IoT) is one of the most intriguing technological revolutions of the last few years due to its rapid expansion. The advancement of IoT applications is dependent on standard and real-time communication protocols. The IoT protocols are designed to enable reliable messaging. However, the selection of a standard and efficient messaging protocol is a difficult and intimidating task for any organization. One of the main challenging issues facing the loT is wireless sen-sor networks (WSNs). The devices that make up these networks have low levels of memory, processing power, and energy. Certain restricted protocols must be in place to function in WSNs due to their constrained nature. In the context of a business application for monitoring, this paper shows the experimental performance of two protocols: Message Queue Telemetry Transport (MQTT) and Constrained Application Protocol (CoAP).

关键词： Performance evaluation Wireless communication Wireless sensor networks Technological innovation Protocols Power demand Throughput Internet of Things Telemetry Testing

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