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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Kate, Vandana Bhalerao, Shilpa Sharma, Vanshika Acropolis Institute of Technology and Research Dept. of Computer Science and Information Technology Indore India Acropolis Institute of Technology and Research Dept. of Computer Science and Technology Indore India

ISBN: (纸本)9798350343274

In the realm of academic research, the task of determining the most suitable Software Development Life Cycle (SDLC) becomes complex, owing to the diverse project requirements, rapidly changing technology, team skills and experience, organizational culture and readiness, flexibility and adaptability needs, and decision-making complexity. It requires a thorough analysis of various factors to make a wise decision that aligns with the project's goals and constraints. A variety of models, including Waterfall, Prototyping, and Spiral, offer distinct sets of advantages and disadvantages. Agile modeling is a flexible and iterative software development strategy that emphasizes collaborative modeling and rapid feedback. The agile philosophy promotes transparency, effective communication, and embracing change to meet evolving project needs efficiently and effectively. An In-dept. Examination of Agile Methodologies versus Conventional Approaches gives a deeper understanding of their respective strengths, weaknesses, and applicability. Also as the educational institutes increasingly recognize the importance of providing industry-relevant skills and preparing students for dynamic work environments, agile methodologies are likely to play a vital role in shaping the future of education and fostering the success of students in their careers. In this paper, we have explored the practical application of well-established Agile methodologies including Scrum, Kanban, Lean, Extreme Programming (XP), Feature Driven Development, and Dynamic systems Development Method (DSDM) within the context of educational institutions and project management. © 2023 IEEE.

关键词： Project management

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Review on TIRADS Scoring Based Thyroid Nodule Malignancy Detection 9

Review on TIRADS Scoring Based Thyroid Nodule Malignancy Det...

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9th International Conference on Smart Computing and Communications, ICSCC 2023

作者： James, Jisha Anand, H.S. Dept. of Computer Science and Engineering Kochi India Muthoot Institute of Technology and Science Kochi India Cvv Institute of Science and Technology Dept. of Computer Science Ernakulam India

ISBN: (纸本)9798350314090

Thyroid nodules are a common clinical finding, and their accurate risk stratification is essential for determining appropriate management strategies. The Thyroid Imaging Reporting and Data System (TIRADS) has emerged as a valuable tool for evaluating thyroid nodules by integrating various ultrasound features to predict malignancy. This research article aims to assess the efficacy of TIRADS scoring in detecting thyroid nodule malignancy and its potential as a standardized diagnostic tool. A comprehensive literature review was conducted to identify studies that evaluated the diagnostic performance of TIRADS scoring systems in different patient populations. The included studies demonstrated that TIRADS scoring systems exhibited substantial accuracy, sensitivity, and specificity in predicting thyroid nodule malignancy. The incorporation of ultrasound features, such as nodule size, composition, echogenicity, margins, and presence of calcifications, allowed for a standardized and reproducible risk stratification. TIRADS scoring systems also demonstrated the ability to optimize the decision-making process for further diagnostic workup, reducing unnecessary fine-needle aspiration biopsies and surgical interventions. The article also discusses the limitations and challenges associated with TIRADS scoring. © 2023 IEEE.

关键词： Machine learning

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A HIGH-PRECISION 3D LASER MEASUREMENT METHOD BASED ON ESTIMATION OF TRUE VALUES BY HIGH-DENSITY MEASUREMENTS AND CALCULATION OF DIFFERENCES BETWEEN MEASUREMENT DATA OF TWO TIMES

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AIJ Journal of technology and Design 2022年第68期28卷 515-520页

作者： Kakizaki, Ken'ichi Mukai, Tomohisa Araki, Shunsuke Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Dept. of Structural Engineering Building Research Institute

3D laser measurement has a problem that the measurement precision is not sufficient for damage analysis of buildings. In addition, when there is an unevenness in the measurement target, it is difficult to distinguish whether it is an initial shape or damage. In this paper, we propose a method to reduce the measurement error by processing the high-density laser measurement data with statistical methods. In addition, we propose a method to detect the damaged area by comparing the initial shape data of the measured object with the newly measured data. © 2022 Architectural Institute of Japan. All rights reserved.

关键词： Measurement errors

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Does a Dense Point Cloud for Training Data Generation Improve Segmentation Accuracy?

Does a Dense Point Cloud for Training Data Generation Improv...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Wada, Marin Sudo, Hiroaki Adachi, Miho Miyamoto, Ryusuke Meiji University Graduate School of Science and Technology Dept. of Computer Science Kawasaki Japan Meiji University School of Science and Technology Dept. of Computer Science Kawasaki Japan

ISBN: (纸本)9798350324457

Semantic segmentation can provide significant information for a robot to actualize autonomous moving. To increase the classification accuracy of segmentation, appropriate datasets should be prepared, which requires huge human labors. The authors attempt to realize a semi-automatic method that generates two dimensional images having pixel-wise class labels from 3D point clouds obtained by a 3D scanner. In this study, dense point clouds are generated to improve the quality of training samples. Experimental results using data obtained around the course of the Tsukuba Challenge, which is the famous competition for autonomous moving robots in Japan, showed that the dense point clouds effective but appropriate variety of textures should be included in the training samples. © 2023 IEEE.

关键词： 3D scanned data dataset generation density of point clouds semantic segmentation

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Enhanced Autonomous Mobile Robot Navigation Using a Hybrid BFO/PSO Algorithm for Dynamic Obstacle Avoidance

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第17期48卷 209-222页

作者： Makhlouf, Amina Benmachiche, Abdelmadjid Boutabia, Ines LIMA Laboratory Dept. of Computer Science Faculty of science and technology House of Artificial Intelligence Chadli Bendjedid University El-Tarf PB 73 Algeria36000 Algeria

Determining path planning for mobile robots has received a lot of attention over the last three decades, with the goal of identifying safe and effective paths between starting points and destinations. The investigation of diverse navigation methodologies exhibits potential in expanding the range of uses for autonomous mobile robots. This research introduces a novel navigation strategy by combining the Particle Swarm Optimization (PSO) method with the Bacteria Foraging Optimization Algorithm (BFO). Utilizing the benefits of PSO, this bio-inspired method is used to optimize paths for mobile robots, drawing inspiration from the foraging behavior of bacteria, namely E. coli. The principal aim of this study is to design routes that are practical and safe. The proposed model features a robot that emulates bacterial foraging behavior to determine the most optimal path through environments densely populated with obstacles, effectively linking a starting point to a specified destination. This strategy has been implemented and rigorously evaluated across a variety of scenarios. Experimental results reveal that the hybrid BFO-PSO algorithm achieves a success rate exceeding 90% in navigating dynamic environments. Furthermore, the algorithm significantly outperforms traditional methods, offering approximately 50% less computational speed, efficiency, and superior security metrics, confirming its effectiveness and potential for real-world applications. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Escherichia coli

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Counting Malicious Host Behind NAT Using Ensemble Machine Learning Approach 6

Counting Malicious Host Behind NAT Using Ensemble Machine Le...

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6th World Symposium on Artificial Intelligence, WSAI 2024

作者： Shukla, Sanjeev Misra, Manoj Varshney, Gaurav Indian Institute of Technology Roorkee Dept. of Computer Science and Engg Uttrakhand Roorkee India Indian Institute of Technology Jammu Dept. of Computer Science and Engg J&K Jammu India

ISBN: (纸本)9798350374162

Network Address Translation (NAT) is widely used to map private IP addresses to public ones, offering anonymity that not only benefits legitimate users by protecting their internal IP addresses but also poses a challenge by enabling malicious users to obscure their identities during cyber-attacks. Identifying the actual host behind NAT involved in such activities is critical for effective forensic analysis. In this study, we introduce a novel, passive, and non-intrusive technique that leverages ensemble machine learning (ML) to pinpoint and characterize malicious hosts behind NAT. By analyzing network traffic patterns at the flow level, our method successfully penetrates the veil of anonymity NAT provides. Designed to be operating system-agnostic and independent of IP addresses, our approach addresses privacy concerns while maintaining high efficacy. Demonstrating a remarkable accuracy rate of 99.12%, our technique not only outperforms existing ML and non-ML-based methods but also sets a new benchmark in detecting malicious entities operating behind NAT. © 2024 IEEE.

关键词： Anonymity

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AI-Powered DeepFake Defense 3

AI-Powered DeepFake Defense

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Chaitra, M. Kushal, B. Likhitha, H. Priyanka, H.R. Bnm Institute of Technology Affiliated to Vtu Dept. of Computer Science and Engineering Bengaluru India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

Deepfake technology, known for its ability to produce convincingly face-swapped videos, presents significant risks to privacy, security, and public trust. Misuse of this technology for spreading misinformation, defamation, and influencing politics is on the rise, creating a pressing need for more effective detection mechanisms. As deepfake generation methods continue to advance, conventional detection techniques are increasingly unable to keep up, often falling short in accuracy and adaptability. This project aims to create an AI-driven detection system specialized in identifying face-swap deepfakes. The system focuses on subtle irregularities in facial details, movements, and the synchronization between audio and visuals. Employing neural network models such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Capsule Networks, it pinpoints anomalies like unnatural expressions, atypical skin textures, and lighting inconsistencies. The system achieves high reliability with metrics including accuracy (96%), precision (95%), recall (94%), and F1 score (95%), showing marked improvement compared to traditional techniques. It outperforms existing methods such as XceptionNet (92%) on the Celeb-DF dataset, underscoring its superior generalizability and robustness. This scalable solution has broad applications in media, law enforcement, and cybersecurity, reinforcing efforts to combat deepfake threats and fostering improved trust and security across various sectors. © 2025 IEEE.

关键词： Long short-term memory

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Using the Tsetlin Machine to Discover Fatality-based Transitions in Political Violence 3

Using the Tsetlin Machine to Discover Fatality-based Transit...

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3rd International Symposium on the Tsetlin Machine, ISTM 2024

作者： Hsu, Hsu-Chiang Bang-Jensen, Bree Colaresi, Michael P. Chrysanthis, Panos K. Zadorozhny, Vladimir I. Dept. of Informatics & Network Systems University of Pittsburgh United States Dept. of Political Science University of Pittsburgh United States Dept. of Computer Science University of Pittsburgh United States

ISBN: (纸本)9798331504984

There is considerable momentum across multiple continents to build and deploy systems that can usefully inform policy-makers and aid agencies about the timing, location, and scale of political violence. In this paper we introduce a framework that utilizes an architecture based on the Tsetlin Machine to detect notable transitions between lower and higher levels of political violence as measured by fatalities. We engineer input features based on the influential framework of horizontal inequalities (HI) and conflict history as they have been theorized to presage violence. However, we encounter the challenge that some classes are more or less distinguishable from others using TM-generated rules. We leverage the analytical structure of TMs and their weights to explain these clusters. To do so, we introduce the concept of a class spectrum and reduce the dimensions of these spectrums across classes and features with principal components analysis (PCA). This pipeline allows us to find transition (change) points between the fatality classes and cluster the classes into efficiently detectable groups. These tools provide both researchers and policy-makers a means of understanding similarities and differences in explanations across different conflict phases. ©2024 IEEE.

关键词： Data warehouses

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TotCareBot: A Comprehensive Approach Towards Enhancing Early Childhood Education through Robotics 5

TotCareBot: A Comprehensive Approach Towards Enhancing Early...

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5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Chougala, Manjula R Sushma, B. Bhayyar, Akshata S Bhandarkar, Abhay Pavithra, P.N. Ramaiah Institute of Technology Dept. of Computer Science and Engineering Bangalore India Ramaiah Institute of Technology Department of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798331528539

Robots have emerged as versatile tools with significant potential to enhance teaching and learning environments, including classrooms, laboratories, play homes, crèches, and even at home. Their engaging nature can captivate young children, boosting motivation and interest. This paper proposes a methodology for an Educational Robot (ER) designed for play homes. The ER is capable of moving around the classroom, monitoring children, analyzing their emotions, responding appropriately, and alerting the teacher when necessary. The proposed system aims to leverage the positive impact of expressive behaviors in robots, such as storytelling and pampering. Our findings indicate that expressive robot narration is as effective as that of a human displaying similar behaviors and even surpasses a static, inexpressive human narrator. Furthermore, the robot excels in conveying educational content, although its limited emotional expression slightly reduces its effectiveness in narrating emotionally rich stories. This research underscores the potential of ERs to create dynamic and interactive learning experiences for young children, fostering both their cognitive and emotional development. © 2024 IEEE.

关键词： Educational robots

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Optical Evanescent Field for Direct Observation of Sub-100nm Particle Motion on Glass Surface 1

Optical Evanescent Field for Direct Observation of Sub-100nm...

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1st International Conference on Robotics, Engineering, science, and technology, RESTCON 2024

作者： Khajornrungruang, Panart Permpatdechakul, Thitipat Suzuki, Keisuke Blattler, Aran Terayama, Yutaka Goto, Daiki Phaisalpanumas, Pipat Bun-Athuek, Natthaphon Phunopas, Amornphun Kyushu Institute of Technology Iizuka-Shi Mechanical Science and Technology Division Dept. of Intelligent and Control System Faculty of Computer Science and Systems Engineering Fukuoka Japan Kyushu Institute of Technology Iizuka-Shi Mechanical Information Science and Technology Division Dept. of Interdisciplinary Informatics Graduate School of Computer Science and Systems Engineering Fukuoka Japan King Mongkut's University of Technology North Bangkok Bangsue Faculty of Engineering Dept. of Production and Robotics Engineering Bangkok Thailand Kyushu Institute of Technology Iizuka-Shi Mechanical Science and Technology Course Dept. of Intelligent and Control System Graduate School of Computer Science and Systems Engineering Fukuoka Japan King Mongkut's University of Technology North Bangkok Bangsue Faculty of Engineering Dept. of Industiral Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Bangsue College of Industrial Technology Dept. of Mechanical Engineering Technology Bangkok Thailand

ISBN: (纸本)9798350348842

To directly investigate the dynamic nanoscale phenomenon on the surface being processed in wet conditions such as precision polishing, and cleaning in semiconductor industrial, an optical method for visualization and observation of each sub-100 nm sized particle that is moving on an interface such as a silica glass surface by applying an evanescent field have been proposing. Subsequently, we developed an experimental apparatus equipped with an optical microscopy system for verifying the moving particle observation method in a laboratory scale. This article introduces some experimentally direct observation results of duplicated wet processes. © 2024 IEEE.

关键词： Visualization

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