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Iranian Conference on Intelligent Systems (ICIS)

作者： Erfaneh Khanmohammadi Mehdi Esnaashari Amirhossein Damia Hadi Bahrampoor Faculty of Computer Engineering AmirKabir University of Technology (Tehran Polytechnic) Tehran Iran Faculty of Computer Engineering K. N. Toosi University of Technology Tehran Iran Faculty of Computer Engineering Islamic Azad University Science and Research Branch Yasuj Iran

ISBN: (数字)9798331507565

ISBN: (纸本)9798331507572

The software testing process accounted for nearly forty percent of the total software development cost, and one of the most important parts of software testing was test data generation. Performing this process manually led to slowdowns, increased costs, and errors. Therefore, we sought an automated method for generating this set to identify the maximum number of defects. In the process of generating test data, the problem could be transformed into an optimization problem, as their ability to intelligently search in the vast input space and reduce costs and time was superior. Thus, search algorithms could be used to generate test data. Genetic algorithms, particle swarm optimization, etc. were among the most widely used algorithms in this field, but previous algorithms also had drawbacks. In recent years, newer optimization algorithms with better performance were introduced. In this paper, the Coati Optimization Algorithm (COA) was used. This algorithm had lower complexity, appropriate exploration and exploitation phases, and lacked initial parameters compared to previous algorithms. However, the Coati Optimization Algorithm also included weaknesses. Under certain conditions, a solution might never move towards the best solution. Therefore, in this paper, an idea was proposed to improve the performance of the Coati Optimization Algorithm in corrective search ability. Optimization algorithms also needed guidance for search, and the stronger and faster this guidance, the better the quality of the obtained solutions. In this paper, a new fitness function was introduced, which had better performance compared to other fitness functions. Before applying the search algorithm to the data generation problem, first, the control flow graph of the program under test had to be drawn. Then, the execution paths of this program were extracted from the graph, so that the algorithm could generate test data by running on it. However, it should be noted that we would not be able to cove

关键词： Software testing Costs Software algorithms Data collection Search problems Particle swarm optimization Optimization Standards Genetic algorithms Software development management

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AI-Enabled UAVs for Advanced Network Management in the Era of 6G Communications 1

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7th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Dwivedi, Upendra Rajawat, Anand Singh Goyal, S.B. Faisal, Sm Sulakhe, Vishal N. Sudhir, Meesala GL Bajaj Institute of Technology & Management UP Greater Noida201306 India School of Computer Science & Engineering Sandip University Nashik422213 India Faculty of Information Technology City University Petaling Jaya46100 Malaysia Sandip Institute of Technology and Research Centre Nashik India Mechanical Engineering Dept Sandip University Nashik422213 India

ISBN: (数字)9783031508875

ISBN: (纸本)9783031508868

The evolving landscape of wireless communication is heralding the era of 6G, characterized by hyper-connectivity, extremely low latencies, and ubiquitous coverage. In this context, Unmanned Aerial Vehicles (UAVs) equipped with artificial intelligence (AI) capabilities present an innovative solution for advanced network management and service provisioning. This paper offers a com-prehensive overview of the synergistic integration of AI-enabled UAVs in 6G networks. We first elucidate the fundamental challenges of 6G networks, including the demand for denser deployments, dynamic spectrum sharing, and real-time network optimization. AI-powered UAVs address these challenges by serving as aerial base stations, facilitating three-dimensional (3D) network coverage, and enabling adaptive beamforming and interference management. Subsequently, we delve into the operational roles of AI-UAVs in 6G networks. Emphasis is placed on predictive network analytics, where UAVs forecast user demand and potential bottlenecks, autonomous fault detection and self-healing, reducing net-work downtimes, and (iii) dynamic resource allocation, ensuring optimal quality of service (QoS) for users. Finally, we discuss the practical considerations and potential limitations of deploying AI-enabled UAVs, including regulatory constraints, power consumption issues, and UAV-to-UAV coordination. Through this paper, we underscore the paramount importance of AI-UAVs in realizing the full potential of 6G communications, proposing future research directions and emphasizing the need for holistic frameworks that seamlessly integrate terrestrial and aerial network components. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Quality of service

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Fusion Fuzzy Logic and Deep Learning for Depression Detection Using Facial Expressions

Fusion Fuzzy Logic and Deep Learning for Depression Detectio...

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2022 International Conference on Machine Learning and Data engineering, ICMLDE 2022

作者： Rajawat, Anand Singh Bedi, Pradeep Goyal, S.B. Bhaladhare, Pawan Aggarwal, Alok Singhal, Ravi Shankar School of Computer Science and Engineering Sandip University Nashik India Computer Science and Engineering Galgotias University Greater Noida India Faculty of Information Technology City University Malaysia School of Computer Science University of Petroleum and Energy Studies Dehradun India

One of the critical issues in detecting depression is using facial expressions with image data classification. In This research paper, we proposed Fusion Fuzzy Logic((FFL) with deep learning for identifying depressed people based on their facial expressions. Our proposed model the based on an advanced fuzzy algorithm with deep learning for unordered fuzzy rule(FR) initiation to offer appropriate and suitable opinions based on depressed people's facial expressions(FE), to allow Depression Recognition(DR) from image files and recorded video files. The primary goal of this research work was to use the fusion method to turn these facial expressions (FE) into the detection of depressed states. To elevate the performance of the Fusion Fuzzy Logic((FFL) (fuzzy logic and CNN)), delivering them entreated them several times to imitate specific facial expressions. Our proposed FFL with the CNN model produces exact and dependable results with a 94.3% overall accuracy comparable to human recognition. © 2023 The Authors. Published by Elsevier B.V.

关键词： Fuzzy inference

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RFBES at SemEval-2024 Task 8: Investigating Syntactic and Semantic Features for Distinguishing AI-Generated and Human-Written Texts

arXiv

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arXiv 2024年

作者： Rad, Mohammad Heydari Farsi, Farhan Bali, Shayan Etezadi, Romina Shamsfard, Mehrnoush Computer Engineering Department Amirkabir University of Technology Tehran Iran School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran

Nowadays, the usage of Large Language Models (LLMs) has increased, and LLMs have been used to generate texts in different languages and for different tasks. Additionally, due to the participation of remarkable companies such as Google and OpenAI, LLMs are now more accessible, and people can easily use them. However, an important issue is how we can detect AI-generated texts from human-written ones. In this article, we have investigated the problem of AI-generated text detection from two different aspects: semantics and syntax. Finally, we presented an AI model that can distinguish AI-generated texts from human-written ones with high accuracy on both multilingual and monolingual tasks using the M4 dataset. According to our results, using a semantic approach would be more helpful for detection. However, there is a lot of room for improvement in the syntactic approach, and it would be a good approach for future work. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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An Empirical Study of Unanswered Python-Related Questions on Stack Overflow

An Empirical Study of Unanswered Python-Related Questions on...

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Information technology Research and Innovation (ICITRI), International Conference on

作者： Yusuf Sulistyo Nugroho Salsabil Aisyah Abdul Halim Syful Islam Yogiek Indra Kurniawan Tati Erlina Informatics Engineering Faculty of Communication and Informatics Universitas Muhammadiyah Surakarta Surakarta Indonesia Computer Science and Engineering Faculty of Engineering BSMRSTU Gopalganj Bangladesh Informatics Engineering Faculty of Engineering Universitas Jenderal Soedirman Purwokerto Indonesia Computer Engineering Faculty of Information Technology Universitas Andalas Padang Indonesia

ISBN: (数字)9798350376210

ISBN: (纸本)9798350376227

As a high-level programming language, Python supports user-friendly coding and system integration. It is not only used for data analytics but is also applied in software development. Although it has several benefits, users have various issues while running Python code. When programmers face issues, they often use Stack Overflow (SO) to get assistance for their problems. However, many Pythonrelated questions on SO are not responded to and remain unanswered. Thus, in this paper, we analyzed these unanswered questions to identify potential weaknesses of user and discussion characteristics. We applied a mixed-methods approach, including quantitative and qualitative analysis of user reputation, question types, and discussion topics. Our analysis shows that unanswered questions are mostly from low-reputation users ($\mathbf{7 2. 5 2 \%}$), followed by mid-reputation users $(\mathbf{1 7. 9 3 \%}$), and high-reputation users $\mathbf{(9. 5 5 \%)}$. The distribution of the unanswered question types shows that low-reputation users ask more “how” type of question ($\mathbf{3 8. 9 0 \%}$) followed by mid and high-reputation users. In contrast, the “what” and “why” questions are mostly posted by high-reputation users, followed by mid and low-reputation users. Our word-cloud analysis suggests a correlation between user reputation and the question topics. We acknowledge that there are limitations in our study, include potential misclassifications due to the time of posting and biases in manual labeling, which may affect the accuracy of our findings. This study provides insights into unanswered questions posted by the community, open up potential areas for future work such as improving support mechanisms and community engagement.

关键词： Technological innovation Information sharing System integration Manuals Encoding Labeling Information technology Faces Python Software development management

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Ensemble Model for Detection and Classification of Brain Tumors

Ensemble Model for Detection and Classification of Brain Tum...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： Bhagyalaxmi K B. Dwarakanath Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Ramapuram Tamilnadu India Department of Information Technology SRM Institute of Science and Technology Ramapuram Tamilnadu India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Brain tumors, characterized by abnormal cell growth within the brain, present significant challenges for early detection and accurate classification due to their complex and heterogeneous nature. Manual evaluation of MRI scans, though critical, is often laborintensive and prone to errors, highlighting the need for advanced, automated diagnostic methods. Convolutional Neural Networks (CNNs) have demonstrated strong potential in this domain by effectively learning hierarchical features from raw medical images to detect and classify brain tumors. However, single CNN models often encounter difficulties in handling the variability in tumor appearance, such as differences in shape, size, and intensity. To address these challenges, this research proposes an ensemble model that integrates pretrained CNN architectures-ResNet50, Inception, and Xception. The ensemble technique strengthens the adaptability and accuracy of brain tumor identification and categorization by incorporating the advantages of these distinct models. The proposed model was trained and evaluated using a large dataset of 5,316 MRI images, representing a range of brain tumor types, including gliomas, pituitary, meningiomas, and cases no tumor. With a high classification accuracy of 99.39%, our ensemble model proved to be useful in enhancing the reliability of diagnoses and assisting surgeons in making decisions on the treatment of brain tumors.

关键词： Adaptation models Accuracy Magnetic resonance imaging Transfer learning Brain tumors Surgery Brain modeling Ubiquitous computing Convolutional neural networks Ensemble learning

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AI-Powered Object Detection for Autonomous Vehicles: A Comparative Study of Machine Learning Models

AI-Powered Object Detection for Autonomous Vehicles: A Compa...

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2025 IEEE International Conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Shrivastava, Anurag Kansal, Vipashi Nagpal, Amandeep Manjunatha Dixit, Krishna Kant Rajkumar, K. Varada Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India GLA University Department of Electrical Engineering Mathura India MLR Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798331512965

Object detection is an integral piece of autonomous cars learning their surroundings and making safe judgements about where and how to drive in real time. This research compares and contrasts multiple machine learning models for object identification in autonomous cars in order to assess accuracy, computational economy, and real time performance. Then, we present how various well known proposed models (CNN, YOLO, SSD, R-CNN) perform in changing driving conditions. It researches the models capabilities to run real time applications within autonomous cars, as well as the trade off of speed and accuracy between different models. We also study how hyperparameter adjustment, data augmentation and transfer learning impact these models' performance. Through thorough research on benchmark datasets, this work shows the benefits of the adoption of hybrid models, which mix speed and accuracy, to increase item recognition in real world contexts. This study contributes to the expanding corpus of knowledge on AI driven solutions for autonomous systems by having useful insights regarding the improvement of autonomous vehicle object identification algorithms. © 2025 IEEE.

关键词： Contrastive Learning

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The ForEx++ based decision tree ensemble approach for robust detection of Parkinson’s disease

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 11429-11453页

作者： Pramanik, Moumita Pradhan, Ratika Nandy, Parvati Bhoi, Akash Kumar Barsocchi, Paolo Department of Computer Applications Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim Majitar737136 India Department of Computer Engineering Sikkim Institute of Science and Technology South Sikkim Jorethang India Department of Medicine Sikkim Manipal Institute of Medical Sciences Sikkim Manipal University Sikkim Gangtok737102 India Department of Computer Science and Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim Majitar737136 India Institute of Information Science and Technologies National Research Council Pisa56124 Italy KIET Group of Institutions Delhi-NCR Ghaziabad201206 India Burla Sambalpur768018 India

The progressive reduction of dopaminergic neurons in the human brain, especially at the substantia nigra is one of the principal causes of Parkinson’s Disease (PD). Voice alteration is one of the earliest symptoms found in PD patients. Therefore, the impaired PD subjects’ acoustic voice signal plays a crucial role in detecting the presence of Parkinson's. This manuscript presents four distinct decision tree ensemble methods of PD detection on a trailblazing ForEx++ rule-based framework. The Systematically Developed Forest (SysFor) and a Penalizing Attributes Decision Forest (ForestPA) ensemble approaches has been used for PD detection. The proposed detection schemes efficiently identify positive subjects using primary voice signal features, viz., baseline, vocal fold, and time–frequency. A novel feature selection scheme termed Feature Ranking to Feature Selection (FRFS) has also been proposed to combine filter and wrapper strategies. The proposed FRFS scheme encompasses Gel’s normality test to rank and selects outstanding features from baseline, time–frequency, and vocal fold feature groups. The SysFor and ForestPA decision forests underneath the ForEx++ rule-based framework on both FRFS feature ranking and subset selection represents Parkinson’s detection approaches, which expedite a better overall impact on segregating PD from control subjects. It has been observed that the ForestPA decision forest in the ForEx++ framework on FRFS ranked features proved to be a robust Parkinson’s detection scheme. The proposed models deliver the highest accuracy of 94.12% and a lowest mean absolute error of 0.25, resulting in an Area Under Curve (AUC) value of 0.97. © 2022, The Author(s).

关键词： Decision trees

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Triple des Cryptography Based on Hash Function and DSA for Digital Certificate Authentication

Triple des Cryptography Based on Hash Function and DSA for D...

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2022 International Seminar on Application for technology of Information and Communication, iSemantic 2022

作者： Rachmawanto, Eko Hari Budi Handoko, Lekso Umam, Chaerul Jatmoko, Cahaya Ali, Rabei Raad Dian Nuswantoro University Faculty of Computer Science Department of Informatics Engineering Semarang Indonesia National University of Science and Technology Nasiriyah Iraq

ISBN: (数字)9781665488396

ISBN: (纸本)9781665488396

Digital certificates are digital files that are conventionally used as proof of participation or a sign of appreciation owned by someone. Cryptographic authentication method using digital signatures that are used as confidential evidence and validation of digital certificate ownership. Digital signatures use cryptographic algorithms such as one-way hashes, non-symmetric key cryptography, and symmetric key cryptography. So this study uses the proposed algorithm used, namely SHA-256, DSA & 3DES. The SHA-256 algorithm is used because of its high security, while the DSA algorithm is a special standard for digital signatures with 2 (two) functions, namely signing & verifying, and the last is the 3DES algorithm as double security which guarantees 3 (three) times the encryption process and description. By implementing a digital signature using the SHA256 hash method, the DSA signing & verifying method, and the 3DES cryptographic algorithm, digital certificates can avoid editing threats, and ensure authentication security and validation of the validity of the file, so that it can detect the original digital certificate. In 10 experiments, it is known that the digital signature method with the SHA-256, DSA and 3DES algorithms has an average run time speed of 0.0518186331 seconds for the signing method and 0.1321175146 seconds for the verify method. © 2022 IEEE.

关键词： Authentication

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Dynamic Patient Triage Optimization in Healthcare Settings Using RNNs for Decision Support 10

Dynamic Patient Triage Optimization in Healthcare Settings U...

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10th International Conference on Smart Computing and Communication, ICSCC 2024

作者： Ranganathan, Chitra Sabapathy Basavaraddi, Chethan Chandra S Saillaja, V. Pandey, Pramod Sundaramurthy, B. Murugan, S. Mphasis Corporation Tamil Nadu Chennai India Artificial Intelligence and Machine Learning R&D Don Bosco Institute of Technology VTU Karnataka Belagavi India SRM Institute of Science and Technology Faculty of Management Studies Tamil Nadu Chennai India Nagpur Campus Maharashtra Pune India Department of Computer Science and Engineering Tamil Nadu Salem India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350363104

The article presents a sophisticated healthcare system that uses recurrent neural networks (RNNs) to optimize real-time patient triage. The developed model integrates patient data from the Internet of Things (IoT), and it performs dynamic assessments of vital indicators like heart rate, blood pressure, and temperature to prioritize pre-operation care based on the urgency and severity of diseases. The RNN architecture considers the temporal connections included in the data, which enables a more sophisticated comprehension of the changing patient states. The training and evaluation of the model make use of a large dataset. In comparison to more conventional triage methods, the model displays considerable gains in both accuracy and efficiency. The system not only reduces the amount of time needed to respond and allocate resources, but it also improves the flexibility to react to shifting patient conditions. This research represents a significant step toward the development of intelligent decision support systems in the healthcare industry. It demonstrates the potential of more sophisticated machine learning approaches to transform patient care processes in environments with high stakes and a dynamic nature. © 2024 IEEE.

关键词： Recurrent neural networks

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