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International Conference on Electrical engineering and Information & Communication Technology (ICEEICT)

作者： Md. Hamid Hosen Arnob Saha Altaf Uddin Kahakashan Ashraf Sadia Nawar Dept. of Computer Science & Engineering East Delta University Chittagong Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

A bacterial or viral infection of the lungs can cause pneumonia, one of the dangerous and potentially fatal illnesses that can have dire repercussions in a short amount of time. Therefore, a key component of a successful treatment plan is an early diagnosis. Therefore, a sophisticated and automated system that can diagnose chest X-rays and make the process of diagnosing pneumonia easier for both specialists and novices is required. This research aims to create a CNN model that will help with the accurate classification of pneumonia. In this work, we have presented our Deep Learning method for the classification challenge, which is taught using modified images through several pre-processing stages. With an overall accuracy of 93.30%, we were able to classify X-ray images of pneumonia using a custom CNN model. Our proposed model is able to precisely detect pneumonia from X-ray images with amazing accuracy and loss. Furthermore, we employed the LIME and SHAP tools of the XAI technique to generate a noteworthy conclusion to persuade medical practitioners.

关键词： Training Deep learning Pneumonia Computational modeling Predictive models Loss measurement Robustness

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Gait Recognition: A Cross - Dataset Evaluation of Deep Learning Models

Gait Recognition: A Cross - Dataset Evaluation of Deep Learn...

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IEEE Students' Conference on Electrical, Electronics and computer science (SCEECS)

作者： Hemal Singloo Manoj Kumar Simran Namdev Kinsukh Suni Sonbawne Nishant Indoria Dept. Computer Science and Engineering VIT Bhopal University Sehore India

This research paper investigates the effectiveness of deep learning models for gait recognition using a variety of data, including gait phase data and sensor data. This study evaluates the performance of convolutional neural networks (CNN) and long-term memory networks (LSTM), focusing on comparative analysis across multiple datasets to gain insight and contribute to different data analyses. The gait level dataset contains important movement patterns as a main area of research. Additionally, sensor-based data identifies various information needs, and complete analysis, for greater understanding and use. These comprehensive experimental results provide several observations on the variability and performance of CNNs and LSTMs across various datasets. The applicability of these models in recognizing activities in different products is presented, and their advantages and limitations in model recognition, display, and feature extraction are seen. This study distinguishes between different types of deep learning models and demonstrates their suitability for handling different types of data. These results lead to decisions regarding model selection and application, contributing to advances in analytics, biometrics, and human recognition applications.

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Impact of Human-in-the-Loop Paradigm in AI Labeling and Annotation

Impact of Human-in-the-Loop Paradigm in AI Labeling and Anno...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Meeth Jitendra Davda Sindhu D V Dept. of Computer Science R V College of Engineering Bangalore India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

Incorporating human expertise directly into artifi-cial intelligence workflows, a concept known as the Human-in-the-Loop (HITL) paradigm, has proven to be a crucial approach in improving the processes of AI data labeling and annotation. This survey paper delves into the impact of the HITL paradigm on the quality and efficiency of data annotation in AI systems, emphasizing the synergistic benefits of combining human insights with automated techniques. This paper reviews a variety of recent methodologies that integrate human evaluative input with automated systems, showcasing the enhancement of data accu-racy and system reliability across several applications, including healthcare, autonomous vehicles, and language processing. It classifies various HITL techniques, evaluates the applicability of each and offers potential solutions.

关键词： Surveys Adaptation models Accuracy Annotations Computational modeling Scalability Human in the loop Spatial databases Labeling Artificial intelligence

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AI and Machine Learning Applications in Hepatitis B Diagnosis: An Exploration of Techniques and Outcomes

AI and Machine Learning Applications in Hepatitis B Diagnosi...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Sandeep Kumar Mohit Arora Dept. of Computer Science and Engineering Lovely Professional University Phagwara Punjab India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Hepatitis B is one of the major global health challenges, affecting millions worldwide and frequently leading to serious liver conditions like hepatocellular carcinoma and cirrhosis. Early and accurate diagnosis is very important for effective management and treatment; however, most of the traditional diagnostic approaches, including serological tests, liver function tests, liver biopsy, etc., are very often affected by limitations in resource availability and diagnostic accuracy. This review paper highlights the current development in various research works on the application of artificial intelligence (AI) and machine learning (ML) in the diagnosis of Hepatitis B. This paper reviews all the major ML computational techniques used in Hepatitis B diagnosis, underlining the use of various computational techniques: Generalized regression neural networks (GRNN), multilayer Mamdani fuzzy inference system (ML-MFIS), hybrid models combining clustering with ensemble learning, ANFIS, deep neural network (DNN), and support vector machine (SVM). These methodologies have been reported to show substantial improvement in the performance of diagnosis, where the maximum accuracy rate is up to 99.35%. This paper has also listed the datasets used for the studies and compared the performance metrics of these studies in terms of precision, accuracy, and F-score. Despite the progress, several issues persist, such as small dataset sizes, data imbalance, and the need for thorough validation across diverse clinical settings. Several research questions are also outlined to showcase the current ML diagnosis methods and their performances along with their limitations to provide the future research direction. It also covers the adoption of explainable AI (XAI) as of one the potential future aspect.

关键词： Support vector machines Fuzzy logic Accuracy Liver diseases Explainable AI Reviews Artificial neural networks Nonhomogeneous media Ensemble learning Informatics

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Automated Cognate Detection as a Supervised Link Prediction Task with Cognate Transformer

arXiv

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arXiv 2024年

作者： Mahesh Akavarapu, V.S.D.S. Bhattacharya, Arnab Dept. of Computer Science and Engineering Indian Institute of Technology Kanpur India

Identification of cognates across related languages is one of the primary problems in historical linguistics. Automated cognate identification is helpful for several downstream tasks including identifying sound correspondences, proto-language reconstruction, phylogenetic classification, etc. Previous state-of-the-art methods for cognate identification are mostly based on distributions of phonemes computed across multilingual wordlists and make little use of the cognacy labels that define links among cognate clusters. In this paper, we present a transformer-based architecture inspired by computational biology for the task of automated cognate detection. Beyond a certain amount of supervision, this method performs better than the existing methods, and shows steady improvement with further increase in supervision, thereby proving the efficacy of utilizing the labeled information. We also demonstrate that accepting multiple sequence alignments as input and having an end-to-end architecture with link prediction head saves much computation time while simultaneously yielding superior performance. © 2024, CC BY.

关键词： Automation

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The Internet of Musical Things Meets Satellites: Evaluating Starlink Support for Networked Music Performances in Rural Areas

The Internet of Musical Things Meets Satellites: Evaluating ...

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Internet of Sounds, International Symposium on the

作者： Luca Turchet Paolo Casari Dept. of Information Engineering and Computer Science University of Trento Trento Italy

ISBN: (数字)9798350366525

ISBN: (纸本)9798350366532

Low Earth Orbit (LEO) satellite constellations offer a viable alternative to traditional fixed and wireless technologies, ensuring comparable speeds and latency even in rural areas. One of the applications that can be served by such types of satellite-based Internet access is represented by networked music performance (NMP) systems, which connect remote musicians and enable them to play together. Such systems are particularly relevant to communities located in rural areas, as they might lack access to local music education services, or may encounter difficulties in traveling to meet in presence for playing together. The integration of NMP system with LEO satellites could offer a viable alternative to in-person collaborative music activities, al-lowing musical stakeholders to reduce the costs for travel and the times to commute, with the benefit of zeroing pollution. However, thus far the use of NMP systems over satellite connections has been largely overlooked by the research community. To bridge this gap, we evaluate the performance of two Starlink-based communication infrastructures in supporting an NMP between two Italian cities, Trento and Verona. The first infrastructure connected two rural areas in the respective cities via two satellite links, one per terminal. The second connected one rural area in Trento to a neighborhood of Verona, respectively via a satellite link and a wired terrestrial network. Our findings clearly indicate that the performance of the current Starlink service in the tested geographical areas does not adhere to the strict latency and reliability requirements of NMPs. This calls for further research in this scarcely investigated domain at the frontiers of 6G networks.

关键词： Wireless communication Satellite constellations Satellites Pollution Urban areas Music Low earth orbit satellites Internet Stakeholders Reliability

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Work in Progress: Enhancing Students' Learning Experience in Formal Methods through Practical Assignments

Work in Progress: Enhancing Students' Learning Experience in...

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IEEE World engineering Education Conference (EDUNINE)

作者： Ruth Schorr Dept. of Computer Science and Engineering Frankfurt University of Applied Sciences Frankfurt Germany

ISBN: (数字)9798331542788

ISBN: (纸本)9798331542795

Formal methods are crucial for ensuring higher integrity levels for safety-critical systems. However, teaching these methods can be quite challenging. Students often show low motivation and are primarily focused on passing formal methods courses with minimal effort. Performance in compulsory formal methods courses is usually below average, with students perceiving the subject as overly mathematical and lacking practical relevance. To address these challenges and enrich the learning experience, we have integrated mandatory group homework assignments into our teaching framework. Students are required to work collaboratively on case studies and present their solutions during class. This work-in-progress paper provides an experience report on enhancing the learning possibilities of master’s students in a model checking course at the Frankfurt University of Applied sciences (FRA-UAS).

关键词： Model checking engineering education

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Zero Area Overhead Based Scan-Chain Watermarking

Zero Area Overhead Based Scan-Chain Watermarking

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Intelligent Systems and Embedded Design (ISED), International Conference on

作者： Ashwin CS Suchismita Roy Dept. of Computer Science & Engineering National Institute of Technology Durgapur India

ISBN: (数字)9798331522612

ISBN: (纸本)9798331522629

Preventing the illegal use of Intellectual Property (IP) cores remains a crucial challenge for the semiconductor industry. Hardware watermarking has emerged as a practical and widely adopted solution to address this issue by verifying the authorship of the design, thereby detecting IP piracy. For a watermarking scheme to be robust, it must ensure easy detectability and be resilient against various attacks. In this paper, we propose a novel scan chain-based watermarking technique to protect hardware intellectual property (IP) against unauthorised usage and counterfeiting. The proposed method leverages the inherent properties of scan chains used in design-for-testability (DFT) to embed a unique watermark within the hardware design. By utilising genetic algorithms for optimal scan chain reordering and watermark embedding, our approach ensures minimal impact on the hardware's performance and testability. Experimental results demonstrate the robustness and effectiveness of the proposed watermarking scheme in various scenarios.

关键词： Electronics industry Watermarking Intellectual property Probability Hardware Robustness Protection Counterfeiting Intelligent systems Genetic algorithms

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Insights into Zooplankton Abundance Dynamics in Tropical Temporary Ponds using Machine Learning and Explainable AI

Insights into Zooplankton Abundance Dynamics in Tropical Tem...

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International Conference on Innovations in science, engineering and Technology (ICISET)

作者： Shahriar Siddique Ayon Sharia Arfin Tanim Hadiur Rahman Nabil Maruful Islam Tonmoy Mohajan Kamruddin Nur Dept. of Computer Science American International University-Bangladesh Dhaka Bangladesh Dept. of Computer Science and Engineering International Islamic University Chittagong Chittagong Bangladesh Dept. of Electrical and Electronic Engineering Chittagong University of Engineering and Technology Chittagong Bangladesh

ISBN: (数字)9798350355499

ISBN: (纸本)9798350355505

Determining the health and resilience of ecosystems depends on an understanding of the dynamics of zooplankton abundance in tropical temporary ponds. Using ensemble modelling and explainable artificial intelligence (XAI) techniques, this paper explores the complex dynamics of zooplankton abundance in tropical temporary ponds. This study uses a Machine Learning framework to evaluate the predictive performance of models such as k-nearest Neighbors ($k N N$) regression, Random Forest Regression, Lasso Regression, Passive Aggressive Regression, Ridge Regression, and Ensemble Voting Regression. Among the machine learning models tested, the ensemble model, which combines ridge and ridge CV, performs best, with metrics such as MAE of 0.02, MSE of 0.00, and $\mathbf{R 2}$ of 1.00. The Shapley Additive ExPlanations (SHAP) analysis identifies ‘Rotifers’, ‘Culex’, ‘Chironomodidae’, and ‘Calanoids’ as significant predictors. Data preprocessing techniques improve the dataset derived from a study of fish predation effects in tropical temporary ponds. Our research advances our understanding of zooplankton ecology and provides insights into conservation strategies and management practices in these ecosystems. Using ensemble modelling and explainable AI, we contribute to developing accurate predictive models for the long-term management of tropical temporary pond ecosystems.

关键词： Accuracy Explainable AI Biological system modeling Ecosystems Machine learning Predictive models Ecology Zooplankton Environmental management Resilience

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DevOps 2.0: Embracing AI/ML, Cloud-Native Development, and a Culture of Continuous Transformation

DevOps 2.0: Embracing AI/ML, Cloud-Native Development, and a...

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： V. Hemanth Das Chowdary A. Shanmukh T. Phaneendra Nikhil B. Sandeep Kumar Feroz Khan Dept. of Computer Science Engineering Koneru Lakshmiah University Vaddeswaram India

ISBN: (数字)9798350386349

ISBN: (纸本)9798350386356

This research study investigates the emerging domain of DevOps, assessing its capacity to fundamentally transform the processes of software development and delivery. A notable advancement in the field of DevOps is the amalgamation of AI/ML and artificial intelligence. These technologies possess significant potential to revolutionize the DevOps domain through the automation of tedious duties, including provisioning infrastructure, managing configurations, and identifying anomalies. This study highlights the increasing need for streamlined solutions. By harnessing the capabilities of AI and ML-driven tools, DevOps teams will have the ability to streamline their processes, enabling them to allocate resources to more strategic approaches such as nurturing innovation, addressing complex challenges, and making decisions based on data. This study will investigate the precise methods by which AI/ML can enhance DevOps practices, resulting in a software development environment that is more secure, efficient, and agile.

关键词： Industries Technological innovation DevOps Automation Social networking (online) Microservice architectures Transforms

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