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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Seoyeon Jeong Jaeho Kim Kyusung Choi Young-Rae Cho Dept. of Computer and Telecommunications Engineering Yonsei University Wonju Republic of Korea Dept. of Software Yonsei University Wonju Republic of Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

Recent advancements in AI technology are revolutionizing the medical field by enhancing diagnostic accuracy, optimizing treatment plans, and providing personalized medical services. AI-based algorithms are particularly effective in analyzing medical images to detect diseases early. Telemedicine, which allows remote medical consultations, benefits from these techniques by improving access to healthcare and enabling accurate diagnoses through efficient data exchange. This work developed an AI-based diagnostic system using a Convolutional Neural Network (CNN) model to analyze wound images. The wound dataset from Kaggle, consisting of 377 images across seven categories, was augmented 25 times to improve model performance. The app allows users to take or upload wound images, which are then classified by the model, providing treatment and aftercare information. This app achieved a high accuracy of 95.716% in classifying wounds and provides basic treatment suggestions. Despite challenges like limited dataset size, the project successfully demonstrated the potential for real-time wound analysis in telemedicine and healthcare. Future improvements should include a more diverse dataset and advanced treatment recommendations to enhance the app's utility.

关键词： Analytical models Accuracy Telemedicine Medical services Wounds Real-time systems Information and communication technology Convolutional neural networks Artificial intelligence Medical diagnostic imaging

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Advanced Tool Learning and Selection System (ATLASS): A Closed-Loop Framework Using LLM

arXiv

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arXiv 2025年

作者： Haque, Mohd Ariful Williams, Justin Siddique, Sunzida Islam, Md. Hujaifa Ali, Hasmot Gupta, Kishor Datta George, Roy Dept. of Cyber Physical Systems Clark Atlanta University United States Dept. of Computer Science and Engineering Daffodil International University Bangladesh Dept. of Computer Science and Engineering Ahsanullah University of Science and Technology Bangladesh

The combination of LLM agents with external tools enables models to solve complex tasks beyond their knowledge base. Human-designed tools are inflexible and restricted to solutions within the scope of pre-existing tools created by experts. To address this problem, we propose ATLASS, an advanced tool learning and selection system designed as a closed-loop framework. It enables the LLM to solve problems by dynamically generating external tools on demand. In this framework, agents play a crucial role in orchestrating tool selection, execution, and refinement, ensuring adaptive problem-solving capabilities. The operation of ATLASS follows three phases: The first phase, Understanding Tool Requirements, involves the Agents determining whether tools are required and specifying their functionality;the second phase, Tool Retrieval/Generation, involves the Agents retrieving or generating tools based on their availability;and the third phase, Task Solving, involves combining all the component tools necessary to complete the initial task. The Tool Dataset stores the generated tools, ensuring reusability and minimizing inference cost. Current LLM-based tool generation systems have difficulty creating complex tools that need APIs or external packages. In ATLASS, we solve the problem by automatically setting up the environment, fetching relevant API documentation online, and using a Python interpreter to create a reliable, versatile tool that works in a wider range of situations. OpenAI GPT-4.0 is used as the LLM agent, and safety and ethical concerns are handled through human feedback before executing generated code. By addressing the limitations of predefined toolsets and enhancing adaptability, ATLASS serves as a real-world solution that empowers users with dynamically generated tools for complex problem-solving. © 2025, CC BY-SA.

关键词： Reusability

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Improved Residual U-Net for COVID-19 Lung Infection Multi-Class Segmentation in CT Image

Improved Residual U-Net for COVID-19 Lung Infection Multi-Cl...

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2022 International Conference of Advanced Technology in Electronic and Electrical engineering, ICATEEE 2022

作者： Khouloud, Sahib Ahlem, Melouah Fadel, Toure Amel, Slim LRI Badji-Mokhtar Annaba University Dept. Computer Science Annaba23000 Algeria Trois-Rivieres Laboratory of Software Engineering Dept. du Quebec Quebec Canada

ISBN: (纸本)9798350346749

COVID-19, the new coronavirus, is a threat to global public health. Today, there is an urgent need for automatic COVID-19 infection detection tools. This work proposes an automatic COVID-19 infection detection system based on CT image segmentation. A deep learning network developed from an improved Residual U-net architecture extracts infected areas from a CT lung image. We tested the system on COVID-19 public CT images. An evaluation using the F1 score, sensitivity, specificity and accuracy proved the effectiveness of the proposed network. Besides, experimental results showed that the proposed network performed well in extracting infection regions so, it can assist experts in COVID-19 infection detection. © 2022 IEEE.

关键词： Coronavirus

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CADS: Cyber-Attack Detection System Using Decision Tree for Automotive Cyber-Physical systems

CADS: Cyber-Attack Detection System Using Decision Tree for ...

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International Conference on Electrical, Communication, and computer engineering (ICECCE)

作者： Seo-Hee Park Youngmi Baek Dept. of Computer Software Engineering Changshin University Changwon-si Korea

Modern vehicles are equipped with a large number of electronic control units (ECUs) to provide more convenient and safe services for drivers. These ECUs are required to share the various control data for driving using the CAN (Controller Area Network) protocol that is one of the communication protocols for in-vehicle networks. Recently, as vehicles become capable of real-time, two-way communication with various elements on the road, modern vehicles faces a wide range of security issues. The in-vehicle networks are already suffered from the cyber-attacks exploiting newly exposed security vulnerabilities. However, it cannot guarantee the safety and security of the vehicles from as the CAN is designed without considering security. Therefore, this paper proposes a machine learning-based cyber-attack detection system (CADS) in CAN to ensure driving safety. This CADS learns the data pattern of the CAN frames generated while the driving and is able to distinguish between regular messages and cyberattack messages. The performance of the fully trained detection model of the CADS is evaluated using a vast number of testing dataset. The results show that our CADS in terms of accuracy, precision, recall, and F1 score has 100% of the detection rate for regular messages, replay attacks, brute-force attacks except for modification messages.

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Recognition of American sign language using modified deep residual CNN with modified canny edge segmentation

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Multimedia Tools and Applications 2025年 1-28页

作者： Hariharan, U. Devarajan, Devikanniga Kumar, P. Santhosh Rajkumar, K. Meena, M. Akilan, T. Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Faculty of Engineering and technology SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Tambaram602109 India Research Coordinator Dept. of IQAC Amity University Kolkata West Bengal 700135 India

American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algorithms still have some drawbacks, such as difficulty recognizing hand movements and low recognition accuracy for most sign language recognition. A Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system has been developed to address this problem. There are three main parts presents in the method. The first part is pre-processing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.97% accuracy and a 0.05% False Positive Rate. Other CNN architectures such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognizes the difficult hand gestures through the image data. It obtains high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Textual Authenticity in the AI Era: Evaluating BERT and RoBERTa with Logistic Regression and Neural Networks for Text Classification

Textual Authenticity in the AI Era: Evaluating BERT and RoBE...

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International Symposium on Electronics and Telecommunications (ISETC)

作者： Layth Rafea Hazim Oguz Ata Dept. of Electrical and Computer Engineering Altinbas University Istanbul Turkey College of Computer Science and Mathematics Tikrit University Tikrit Iraq Dept. of Software Engineering Altinbas University Istanbul Turkey

ISBN: (数字)9798350390865

ISBN: (纸本)9798350390872

AI-generated content impersonating human writing is an issue that has gained attention as AI spreads its wings. This particular study serves as a comparison between the existing Logistic Regression and Feedforward Neural Networks (FNNs) by employing sentence-BERT-appended models and RoBERTa in the determination of authenticity in a given text. A dataset that was balanced between human-written and AI-generated texts was utilized. Techniques like tokenization and normalization were first used, followed by feature extraction using transformer-based models. Cross-validation and confusion matrix analysis that used measures such as accuracy, precision, recall, F1 score, and ROC AUC were included to guarantee the models’ robustness. The hybrid RoBERTa-FNN model that was deposed challengers was the most outstanding model in respect of precision and recall, and the highest accuracy (99.95%) was obtained as mentioned in the data. The improved performance serves as a proof of how effectively RoBERTa uses its embeddings to represent context on the fine-grained level required for this kind of text classification. This work is a stepping stone to the creation of strong AI text detection systems, besides our advancements to the knowledge of models and embedding performance with respect to text classification. The results lay emphasis on the selection of model configuration and the embedding technique, as they are the key factors in achieving the best results in practical applications.

关键词： Training Logistic regression Ethics Accuracy Text categorization Writing Transformers Data models Feedforward neural networks Artificial intelligence

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Chaos Based Pixel Scrambling Algorithm Based On Corner Traversal

Chaos Based Pixel Scrambling Algorithm Based On Corner Trave...

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International Artificial Intelligence and Data Processing Symposium (IDAP)

作者： Cemile İnce Kenan İnce Davut Hanbay Computer Engineering Dept Inönü University Malatya Türkiye Software Engineering Dept. Inönü University Malatya Türkiye

ISBN: (数字)9798331531492

ISBN: (纸本)9798331531508

Lightweight image encryption has become a critical area of cryptography, especially for resource-constrained devices like those used in the Internet of Things (IoT). Chaotic maps, known for their sensitivity to initial conditions, ergodicity, and seemingly random behavior, are commonly used in image encryption algorithms. These maps are employed in either a standalone or combined fashion during the diffusion or confusion stages of the encryption process. The high correlation between pixels in image data necessitates specialized algorithms. Pixel scrambling, or confusion, is a fundamental step in image encryption. It involves rearranging pixel positions to disrupt the correlation between neighboring pixel values. This study enhances the Corner Traversal algorithm, a pixel scrambling technique, by using chaotic maps to determine the algorithm parameters at each layer. This modification increases the nonlinearity of the encryption process. The results demonstrate that these improvements lead to a more secure encryption scheme compared to the original Corner Traversal algorithm. The performance of the enhanced algorithm is evaluated by comparing it to the original version.

关键词： Performance evaluation Chaos Correlation Sensitivity Data processing Encryption Internet of Things Artificial intelligence Random sequences

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Testing Intelligent Traffic Control Solutions efficiency in reducing traffic and pollution in Tirana

Testing Intelligent Traffic Control Solutions efficiency in ...

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2024 International Workshop on Quantum and Biomedical Applications, Technologies, and Sensors, Q-BATS 2024

作者： Musta, Eugen Elmazi, Donald Elmazi, Klea Mehmeti, Fatjon Hidri, Florenc Department of Finance Canadian Institute of Technology Tirana Albania Department of Computer Engineering University Metropolitan Tirana Tirana Albania Dept. of Information Technology Innovation Universiteti Luarasi University of Rijeka Tirana Albania Department of Software Engineering Canadian Institute of Technology Tirana Albania

ISBN: (纸本)9798331516390

Urban traffic congestion is an issue that metropolitan regions face throughout the world. It has a substantial negative influence on the levels of pollution in the environment and lowers the standard of living in urban areas. This study examines the efficacy of three different traffic control methods in the urban environment of Tirana, Albania: conventional static, actuated, and delay-based. The study assesses how effective they are in controlling traffic flow and reducing emissions by using simulation approaches. Using the Simulation of Urban Mobility (SUMO) tool, the investigation compares the performance of these systems, focusing on key indicators such as time spent in traffic, fuel consumption, and emission levels. Contrary to initial assumptions, the simulations reveal the actuated system as the most efficient, showcasing its superior adaptability to real-time traffic conditions at multiple consecutive intersections. This adaptability optimizes traffic flow and contributes to a reduction in environmental impact. This discovery emphasizes the significance of implementing dynamic traffic management solutions tailored to the specific dynamics of urban environments. Based on these findings, the study advocates for the adoption of actuated traffic management systems in cities struggling with similar traffic challenges, highlighting their potential to bolster urban mobility and sustainability. The research also advocates for further exploration into the scalability of these systems and their integration into broader smart city frameworks. By contributing to the ongoing discourse on urban traffic management, this study provides valuable insights that can inform policy-making and the deployment of intelligent traffic solutions. ©2024 IEEE.

关键词： Traffic congestion

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Enhance Energy Using Bio-Inspired Algorithms in Manet: An Overview

Enhance Energy Using Bio-Inspired Algorithms in Manet: An Ov...

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Electrical engineering and Automatic Control (ICEEAC), International Conference on

作者： Abdelmalek Djihene Boumedjout Amal Kies Ali dept. Computer science USTO Oran Algeria dept. Systems Engineering ENPO Maurice Audin Oran Algeria

ISBN: (数字)9798350349740

ISBN: (纸本)9798350349757

The limited power availability in Mobile Ad hoc Networks (MANETs) poses a fundamental challenge, as node instability and the lack of power supply contribute to reduced network lifetime, impacting MANET performance. In recent years, there has been a growing interest in exploring Artificial Intelligence approaches, particularly bio-inspired algorithms, for MANET routing due to their adaptability and robustness. These approaches have shown promise in minimizing energy consumption, reducing overhead, and improving overall network efficiency. This paper provides an overview of enhancing energy efficiency using bio-inspired algorithms through the application of various meta-heuristic approaches in MANET routing. This paper provides a synthesis of existing research on bio-inspired algorithms in MANET routing, highlighting their effectiveness in addressing energy-related challenges. Furthermore, the paper outlines potential future research directions, including the ex- ploration of hybrid algorithms and the integration of machine learning techniques to further optimize energy efficiency in MANETs.

关键词： Machine learning algorithms Heuristic algorithms Metaheuristics Machine learning Routing Ad hoc networks Energy efficiency

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Towards an Automatic Construction of Simulation Scenarios: A Systematic Review 23

Towards an Automatic Construction of Simulation Scenarios: A...

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Proceedings of the Winter Simulation Conference

作者： Christopher W. H. Davis Antonie J. Jetter Philippe J. Giabbanelli Microsoft USA Dept. of Engineering & Technology Management Portland State University USA Dept. of Computer Science & Software Engineering Miami University Oxford USA

ISBN: (纸本)9798350369663

A predictive simulation is built on a conceptual model (e.g., to identify relevant constructs and relationships) and serves to estimate the potential effects of 'what-if' scenarios. Developing the conceptual model and plausible scenarios has long been a time-consuming activity, often involving the manual processes of identifying and engaging with experts, then performing desk research, and finally crafting a compelling narrative about the potential futures captured as scenarios. Automation could speed-up these activities, particularly through text mining. We performed the first review on automation for simulation scenario building. Starting with 420 articles published between 1995 and 2022, we reduced them to 11 relevant works. We examined them through four research questions concerning data collection, extraction of individual elements, connecting elements of insight and (degree of automation of) scenario generation. Our review identifies opportunities to guide this growing research area by emphasizing consistency and transparency in the choice of datasets or methods.

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