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Innovation in Technology (ASIANCON), Asian Conference on

作者： Soujanya KJ Ashwini Kodipalli Sanjeev Gosai Sneha BJ Trupthi Rao Dept. of Artificial Intelligence & Data Science Global Academy of Technology Bangalore Karnataka India Dept. of Computer Science & Engineering Global Academy of Technology Bangalore Karnataka India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

Using a variety of machine learning techniques, this research study suggests a unique method for classifying diseases using symptom-based analysis. To improve model transparency and comprehension, the study makes use of the interpretability tools, SHAP kernels and LIME. The research attempts to close the gap between interpretability and prediction accuracy in medical diagnosis by combining two approaches. A thorough assessment of symptom-based categorization is ensured by the use of many machine learning methods. The application of LIME and SHAP kernels aids in the clarification of model predictions and provides information on the primary symptoms that impact illness categorization. This study advances the creation of accurate and comprehensible illness categorization models, promoting confidence in the use of machine learning methods in healthcare.

关键词： Technological innovation Machine learning algorithms Accuracy Machine learning Predictive models Prediction algorithms Vectors Classification algorithms Kernel Diseases

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Enhancing Hospital Mortality Prediction for Heart Failure Patients through Hyperparameter Optimization and Interpretability Analysis with LIME and SHAP

Enhancing Hospital Mortality Prediction for Heart Failure Pa...

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Women in Computing (InCoWoCo), International Conference for

作者： Meena S Monisha Murthy Ashwini Kodipalli Dept. of Computer Science and Engineering Global Academy of Technology Bengaluru Karnataka India Dept. of Artificial Intelligence & Data Science Global Academy of Technology Bengaluru Karnataka India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

Heart failure is one of the primary causes for deaths caused in the hospital. Predicting mortality rate of such patients is extremely important for the efficient use of health care resources. This research aims to estimate the hospital mortality rate of Heart Failure(HF) patients using machine learning(ML). Using computational technique, the obtained model has given the most efficient accuracy. This paper uses the classification model to detect two outcomes (0 and 1, i.e., if the patient will survive or not). The classification models used for the hospital mortality prediction are, single classifiers such as Logistic regression, SVM, KNN, Decision tree (Ginni and Entropy);with Logistic regression obtaining highest accuracy of 88.9%, Bagging and boosting models such as Randomforest, Adaptive boosting, Gradient boosting, XG boosting, Categorical boosting; with CatBoost obtaining highest accuracy of 90.2%. To achieve the best accuracy, ensemble models were used out of which ensemble classifier ensemble gave 91.1% accuracy. To understand the features playing important role in decision making, explainable AI(XAI) is used. LIME and SHAP are considered. LIME generated local explanations, it explained the decision of a complex model for a specific instance or observation. In contrast, SHAP provided global explanations, it explained the overall behavior of the model across all instances.

关键词： Logistic regression Accuracy Machine learning algorithms Hospitals Computational modeling Mortality Predictive models Boosting Prediction algorithms data models

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YOLOv8-Based Deep Learning Approach for Real-Time Skin Lesion Classification Using the HAM10000 dataset

YOLOv8-Based Deep Learning Approach for Real-Time Skin Lesio...

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International Conference on e-health Networking, Applications and Services (HealthCom)

作者： Utsha Saha Imtiaj Uddin Ahamed Md Ashique Imran Imam Uddin Ahamed Al-Amin Hossain Ucchash Das Gupta Dept. of Computer Science North Dakota State University Fargo ND USA Agricultural Data Analytics Unit North Dakota State University Fargo ND USA Dept. of Computer Science University of South Dakota Vermillion SD USA Dept. of Applied Computer Science The University of Winnipeg Winnipeg Canada Dept. of Computer Science Liverpool John Moores University Liverpool UK

ISBN: (数字)9798350350548

ISBN: (纸本)9798350350555

Skin cancer is a prevalent and potentially fatal disease that requires early detection for effective treatment. We trained and evaluated five YOLOv8 classification model variants (YOLOv8n-cls, YOLOv8s-cls, YOLOv8m-cls, YOLOv8l-cls, and YOLOv8x-cls) on the HAM10000 dataset, which contains 10,015 dermatoscopic images of common pigmented skin lesions. The models were trained for 30 epochs using data augmentation techniques to enhance generalization. Performance was assessed using metrics including accuracy, precision, recall, F1-score, and inference time. The YOLOv8x-cls model achieved the highest accuracy of 86.2% and precision of 82.1%, while the YOLOv81-cIs model demonstrated the best balance with the highest F1-score of 77.0%. Compared to previous ensemble approaches, our single YOLOv8 models achieved superior performance with lower computational overhead. The YOLOv8n-cls variant showed the fastest inference time of 0.5 ms, making it suitable for real-time applications. Our results demonstrate the potential of YOLOv8-based models for accurate and efficient skin lesion classification, which could aid in early skin cancer detection and improve patient outcomes.

关键词： Measurement Deep learning Accuracy Computational modeling Skin Real-time systems Lesions Ensemble learning Skin cancer Diseases

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Utilizing Machine Learning Techniques for Crime Classification Under the Indian Penal Code (IPC) Based on Crime Reporting data

Utilizing Machine Learning Techniques for Crime Classificati...

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data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： R. Ponnusamy Veeramalai Sankaradass HarshivKrishna M Sre Sandhya K Bharath Sampath Aashika B Dept. of Artificial Intelligence and Data Science Madha Engineering College Chennai Tamil Nadu India Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Crime classification using the Indian Penal Code (IPC) is a common task at each police station. It involves complex decision-making related to crimes and respective IPC sections for different cases. The IPC is the foundational criminal code in India, containing a list of all substantive penal laws with definitions and punishments for each offense. Automating the crime classification is helpful due to the complexity and sheer volume of crimes reported daily. This work uses machine learning techniques to automate crime classification based on IPC. To serve this purpose, this paper utilizes the K-Nearest Neighbors algorithm. The proposed system accepts an input crime report and textually corroborates the report to an IPC section. For this intent, the system relies on the bag-of-words technique for identifying sections under IPC by scrutinizing repetition and availability of key terms in the crime to 98% accuracy in classifying crimes. Hence, the automated method is quite effective for mapping crime reports into the corresponding sections of the IPC. Also, because the system can easily be deployed online, it will be easy to integrate with existing workflows at the police stations. So, case filing and management would become more effective. This reduces the amount of manual effort and also brings consistency in classification; for this reason, the tool is of immense value to law enforcement agencies nationwide. The innovation in the paper is the development of a machine learning-based system using the K-Nearest Neighbors (KNN) algorithm to automate the classification of crimes under the Indian Penal Code (IPC) based on textual crime reports.

关键词： Technological innovation Codes Accuracy Machine learning algorithms Automation Law enforcement Machine learning Nearest neighbor methods Classification algorithms System implementation

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Vader protocol based sentiment analysis technique using LSTM for Stock Trend prediction 14

Vader protocol based sentiment analysis technique using LSTM...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Ticku, Amrita Tripathy, Niva Mishra, Pankaj Kumar Sinha, Anurag Jadon, Shivangi Raj, Aditya Kulshrestha, Harsh Rai, Vipin Shamim, Rejuwan Bharti Vidyapeeth's College of Engineering India Driems Autonomous Engineering College Cuttack India Rungta College of Engg. & Technology R1 Department of Electronics and Telecommunication Engg. CG Bhilai India Ignou School of Computing and Information Science New Delhi India Galgotias University Dept. of Computer Science and Engineering Noida India Galgotias University Department of Computer Science and Engineering Noida India Maharishi University of Information Technology Dept. of Computer Science and Engineering with Data Science Noida India

ISBN: (纸本)9798350335095

Stock prediction has always been a difficult task, and numerous strategies for predicting stock market behavior have been offered. Researchers have recently begun to investigate the use of sentimental analysis and deep learning models such as Long Short-Term Memory (LSTM) to predict stock prices. We offer a stock prediction model that integrates sentimental analysis and LSTM in this research. The model considers the mood of stock market news items and use LSTM to forecast future stock prices. The results of our suggested model's evaluation on the S& P 500 dataset reveal that it outperforms traditional stock prediction approaches. © 2023 IEEE.

关键词： Forecasting

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A Cross-Language Analysis on Sarcasm Detection

A Cross-Language Analysis on Sarcasm Detection

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International Conference on computer and Information Technology (ICCIT)

作者： Nuzat Tasnim Rudra Pratap Deb Nath Dept. of Computer Science and Engineering Big Data Information and Knowledge Engineering (BIKE) Lab University of Chittagong Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Cross-language sarcasm detection is valuable for assessing if a model effective in one language works equally well in another, potentially saving significant time and effort in developing language-specific models. This study proposes the following null hypothesis: Performance variations in Sarcasm Detection models due to single model component changes (e.g., embeddings or classifiers) are independent of the target language. We evaluated 27 models on English and Bangla datasets, two languages with distinct sentence structures, and analyzed performance differences using the Wilcoxon test. Based on the F1-score, the test revealed p value of 0.21, implying that there are no significant difference between the two datasets, failing to reject the hypothesis. Nonetheless, further research on cross-language sarcasm detection is needed to ensure a comprehensive grasp of this field and its potential applications.

关键词： Measurement Analytical models Ethics Computational modeling Training data computer architecture Natural language processing Multilingual Cultural differences Information technology

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Presenting Bangladesh Education data in Analytical and Semantic Format

Presenting Bangladesh Education Data in Analytical and Seman...

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International Conference on computer and Information Technology (ICCIT)

作者： Tonmoy Chandro Das Rudra Pratap Deb Nath Rezaul Karim Dept. of Computer Science and Engineering Big Data Information and Knowledge Engineering (BIKE) Lab University of Chittagong Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

In an era where data plays a pivotal role in shaping policy and driving progress, especially in education, the need for well-informed decision-making is more important than ever. Like other governments, Bangladesh regularly publishes education data as part of open data policies to promote transparency and accessibility. However, while Bangladesh’s educational data is rich in key metrics, it faces limitations such as 1) a lack of a unified analytical format and insufficient aggregation, which restricts its usability for Business Intelligence, Online Analytical Processing (OLAP), and gaining a complete view of the data to extract meaningful insights; 2) limited interoperability and accessibility. In this study, we model Bangladesh’s education data across multiple dimensions, integrating diverse sources into a multidimensional framework using data Warehousing technology. This enables OLAP-like analysis for comprehensive insights. To improve interoperability and accessibility, the data is integrated into a Knowledge Graph called BEdKG. BEdKG is modeled with the Resource Description Framework (RDF) model and composed of 337,308 RDF triples, adheres to Findability, Accessibility, Interoperability, and Reusability (FAIR) principles, and is linked to 72 external resources. Finally, evaluations through a query interface show that BEdKG, with a size of 45.3 MB, effectively addresses the issue of insufficient aggregation by providing interactive query responses and performing operations over 99% faster than manual methods.

关键词： Education Decision making Warehousing Knowledge graphs Resource description framework data models Business intelligence Usability Interoperability Semantic technology

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Explainable Adversarial Attacks on Coarse-to-Fine Classifiers

arXiv

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arXiv 2025年

作者： Heidarizadeh, Akram Hatfield, Connor Lazzarotto, Lorenzo Cai, HanQin Atia, George Dept. of Electrical and Computer Engineering University of Central Florida OrlandoFL United States School of Technology Pontifícia Universidade Católica do Rio Grande Sul Porto Alegre Brazil Dept. of Statistics and Data Science Dept. of Computer Science University of Central Florida OrlandoFL United States Dept. of Electrical and Computer Engineering Dept. of Computer Science University of Central Florida OrlandoFL United States

Traditional adversarial attacks typically aim to alter the predicted labels of input images by generating perturbations that are imperceptible to the human eye. However, these approaches often lack explainability. Moreover, most existing work on adversarial attacks focuses on single-stage classifiers, but multi-stage classifiers are largely unexplored. In this paper, we introduce instance-based adversarial attacks for multi-stage classifiers, leveraging Layer-wise Relevance Propagation (LRP), which assigns relevance scores to pixels based on their influence on classification outcomes. Our approach generates explainable adversarial perturbations by utilizing LRP to identify and target key features critical for both coarse and fine-grained classifications. Unlike conventional attacks, our method not only induces misclassification but also enhances the interpretability of the model’s behavior across classification stages, as demonstrated by experimental results. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

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A Dual-Port, and Single-Element Planar Inverted-F Antenna for Current 4G/5G Portable Applications 21

A Dual-Port, and Single-Element Planar Inverted-F Antenna fo...

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21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Alja'Afreh, Saqer S. Altakhaineh, Amjaad T. Al-Shamaileh, Mallak H. Matarneh, Aser M Daoud, Omar R. Al-Khawaldah I, Mohammad School of Engineering and Computing American International University Electrical and Computing Engineering Al-Jahra Kuwait Pioneers International Academy Data Science and Machine Learning Dept. Amman Jordan Al-Ahliyya Amman University Communications and Computer Engineering Amman Jordan

ISBN: (纸本)9798350374131

A first-hand, low-profile, two-port planar inverted-F antenna is proposed for current 4G/5G sub-3 GHz hand-portable multiple-input and multiple-output (MIMO) applications. The design features a profile of about 5 mm and dual-coplanar feeds, which are isolated using only a modified top plate, while the shared ground plane is not perturbed as in previous designs. An open-edge slot on the top of the PIFA provides a 3λ/4 current path. The proposed antenna covers approximately a 15% impedance bandwidth fraction in the range of 2.45-2.85 GHz. The simulated results, including scattering parameters, far-field radiation patterns, and the envelope correlation coefficient, all exhibit excellent features. © 2024 IEEE.

关键词： Antenna feeders

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Take Loads Off Your Developers: Automated User Story Generation using Large Language Model

Take Loads Off Your Developers: Automated User Story Generat...

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International Conference on Software Maintenance (ICSM)

作者： Tajmilur Rahman Yuecai Zhu Lamyea Maha Chanchal Roy Banani Roy Kevin Schneider dept. Computer Science University of Saskatchewan Saskatoon Canada Enterprise Data Platform Bell Mobility Montreal Canada

ISBN: (数字)9798350395686

ISBN: (纸本)9798350395693

Software Maintenance and Evolution (SME) is moving fast with the assistance of artificial intelligence (AI), especially Large Language Models (LLM). Researchers have already started automating various activities of the SME workflow. Un-derstanding the requirements for maintenance and development work i.e. Requirements Engineering (RE) is a crucial phase that kicks off the SME workflow through multiple discussions on a proposed scope of work documented in different forms. The RE phase ends with a list of user stories for each unit task and usually created and tracked on a project management tool such as GitHub, Jira, AzurDev, etc. In this research, we collaborated with Bell Mobility to develop a tool “Geneus” (Generate UserSory) using GPT-4-turbo to automatically create user stories from software requirements documents. Requirements documents are usually long and contain complex information. Since LLMs typically suffer from hallucination when the input is too complex, this paper proposes a new prompting strategy, “Refine and Thought” (RaT), to mitigate that issue and improve the performance of the LLM in prompts with large and noisy contexts. Along with manual evaluation using RUST (Readability, Understandability, Specificity, Technical-aspects) survey questionnaire, automatic evaluation with BERTScore, and AlignScore evaluation metrics are used to evaluate the results of the “Geneus” tool. Results show that our method with RaT performs consistently better in most of the cases of interactions compared to the single-shot baseline method. However, the BERTScore and AlignScore test results are not consistent. In the median case, Geneus performs significantly better in all three interactions (requirements specifi-cation, user story details, and test case specifications) according to AlignScorebut it shows slightly low performance in requirements specifications according to BERTScore. Distilling RE documents requires significant time & effort from the senior members of th

关键词： Surveys Productivity Software maintenance Large language models Project management Maintenance Planning Requirements engineering Stakeholders Testing

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