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1st International Conference on Innovations in Communications, Electrical and Computer Engineering, ICICEC 2024

作者： Sowmya, B.J. Mugada, Swathi Swetha, B.N. Vijaya Kumar, B.P. Ramaiah Institute of Technology Department of Artificial Intelligence and Data Science Bangalore India

ISBN: (纸本)9798350376517

The need for analyzing a sonar dataset using machine learning algorithms arises from critical applications such as naval operations, marine exploration, and environmental monitoring. Accurate classification of underwater objects, including rocks and mines, is essential for maritime safety, environmental assessment, and underwater security. Manual interpretation of sonar data is time- consuming and error-prone, necessitating automated classification methods. By leveraging machine learning, we aim to develop robust models capable of efficiently identifying underwater objects, thereby advancing underwater sensing technology and supporting various marine-related endeavors. Here, we investigate the efficacy of Random Forest and Linear Regression machine learning algorithms in analyzing a sonar dataset comprising frequencies to predict whether an object is a rock or a mine. The primary objective is to compare the performance of these two algorithms in classifying underwater objects based on acoustic signals. By leveraging machine learning techniques, we aim to develop models capable of effectively discriminating between rocks and mines based on their acoustic signatures, thus addressing the challenge of underwater object identification. Therefore, the experimental setup involves preprocessing techniques to enhance feature extraction and mitigate noise, ensuring the robustness of the model and optimizing computational efficiency without compromising accuracy. © 2024 IEEE.

关键词： Random forests

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Prediction of Schizophrenia in Patients Using Fuzzy AHP and TOPSIS Methods 3rd

Prediction of Schizophrenia in Patients Using Fuzzy AHP and ...

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3rd International Conference on Machine Learning, Internet of Things and big data, ICMIB 2023

作者： Anoop, R. Anand, Impana Rehan, Mohammed Yashvanth, R. Kodipalli, Ashwini Rao, Trupthi Kamal, Shoaib Department of Artificial Intelligence and Data Science Global Academy of Technology Bangalore India

ISBN: (纸本)9789819939312

Schizophrenia is a chronic illness that most frequently affects people between the ages of 16 and 30. There are many elements that lead to a patient receiving a diagnosis of the illness, but since the origin of the illness is unknown, fuzzy analysis can be an important tool in identifying the factors. The purpose of this study report is to inform readers of the factors that have the greatest influence on an individual. Fuzzy logic is a powerful tool for tackling a wide range of problems in research. It can be used to model complex systems, analyze decision-making processes, and develop systems that can recognize patterns in data. In addition, fuzzy logic can be used to model complex systems such as financial markets and natural phenomena. As fuzzy logic is able to deal with uncertainty and imprecise data, it is particularly well-suited to a wide range of research. Fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is a method used to determine the best of the options under consideration with respect to the weights and influence of each of the attributes associated with every option. Fuzzy AHP (Analytic Hierarchy Process) is a multi-criteria decision-making method that uses fuzzy set theory to evaluate alternatives. This method seeks to identify the best option from a set of alternatives based on the relative importance of the criteria. The goal of this study is to emphasize a through and in-depth literature evaluation of multi criteria decision making challenges in addition to pinpointing the aspects that contribute most significantly. We have implemented MCDM techniques to propose an effective approach towards decision making of various factors at multiple levels in patients suffering from schizophrenia and observed that the highest ranked Person (P4) with 0.8097 CCi can be inferred to most likely be affected by Schizophrenia, while the person at the bottom of the rank hierarchy (P1) with 0.2813 CCi is least likely to be affected. © The A

关键词： Fuzzy logic

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Engage Learn: An AI-Based English Proficiency Improviser 4

Engage Learn: An AI-Based English Proficiency Improviser

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Jadhav, Balasaheb Jain, Manas Jajoo, Adhip Kadam, Devika Kadam, Harshvardhan Kakkad, Toshish Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798331529635

In this research, the new and highly effective Web-based English learning and teaching platform called Engage Learn is introduced with the main purpose of avoiding the mistakes of the traditional models and to help users gain the ability to write and speak English more effectively. Engage Learn employs a marked-defined approach, that involves the application of several advanced technologies. Generative models including ChatGPT 3.5 turbo and Gemini 1.5 flash get enhanced presence when trained using Retrieval-Augmented Generation (RAG) technique. First, the ability for users to speak English makes for an almost informal procedure when learning since the user and the computer are communicating with one another by use of words. In addition to this, targeted feedback modules make an assessment of the user's input and give an immediate-feedback of what is required in terms of improving a user's vocabulary, grammar, and fluency. It offers another learning approach since it presents the content in a manner that is suited more to the current ability level of the user and the usage of preferred learning objectives. Engage Learn also provides flexibility of use based on learning modes as encompassed by general conversation practice, focused grammar correction and vocabulary buildup. This paper reviews the system architecture, explaining features of the different parts of the system, including the speech recognition, LLM with RAG, output analyzing, and the feedback-giving modules on grammar, vocabulary, and reading preferences. Further the paper discusses the tuning of the LLMs in a carefully selected set for language acquisition. This fine-tuning makes sure that the LLMs shall have adequate capability and knowledge required so as to get a feel about how spoken English is and about the particular feedbacks that different user may require out of them. In the last section of the abstract, the planned evaluation methods are presented with the focus on the responsibility of user te

关键词： Contrastive Learning

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Adoption of Medical AI and its Effect on Speciesism 3

Adoption of Medical AI and its Effect on Speciesism

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Abangbila, Linda Li, Xianmiao Agordzo, George K. Fomude, Akwi Helene Anhui University of Science and Technology School of Economics and Management Huainan232000 China Anhui University of Science and Technology School of Mathematics and Big Data Huainan232000 China

ISBN: (纸本)9798350375190

AI can change healthcare by boosting medical decision-making and patient outcomes. However, the use of AI in medicine creates ethical issues, notably with speciesism. Participants' awareness of AI in healthcare, perceptions of speciesism, ethical considerations, and willingness to use A I technologies were all gathered through the questionnaire. Descriptive statistics, chi-square tests, and correlation analyses established the relationship between speciesism and AI adoption. 70 % of participants were aware of healthcare AI technologies, but just 45 % showed a willingness to utilize them. 60 % of those hesitant cited ethical concerns regarding speciesism in AI decision-making. The investigation also found a substantial association between participants' perceptions of speciesism and their willingness to adopt AI technologies, demonstrating that speciesist attitudes affected AI adoption. The study emphasizes ethical considerations and non-human animal interests in medical AI system development and implementation. This research contributes to the ethical use of AI in healthcare by highlighting the effect of speciesism on AI adoption and emphasizing the need to overcome speciesist prejudices to achieve fairness, inclusion, and equity. This study highlights the importance of speciesism awareness and education in medical AI adoption. This study illuminates the relationship between speciesism and medical AI adoption. To enable more inclusive, ethical, and equitable use of AI technologies in healthcare, it addresses ethical considerations and obstacles. © 2024 IEEE.

关键词： Decision making

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Optimizing Consumer Insights: Predicting Customer Purchase Behavior by using Artificial Neural Networks Approach 2

Optimizing Consumer Insights: Predicting Customer Purchase B...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Reddy, Vonteddu Adharsh Dilish, M. Kodipalli, Ashwini Harish, L. Soma, Bhavani Rohini, B.R. Department of Artificial Intelligence & Data Science Global Academy of Technology Bangalore India

ISBN: (纸本)9798331504960

This study investigates the use of artificial neural networks (ANN) to predict consumer purchase behavior based on behavioral and demographic characteristics. The proposed ANN model comprises input, hidden, and output layers, optimized through hyperparameter tuning for improved prediction accuracy. The model employs non-linear activation functions, including ReLU, Sigmoid, and Tanh, alongside both Stochastic Gradient Descent (SGD) and Adam optimizers to enhance learning efficiency. Results indicate that Adam and RMSprop optimizers achieve the best balance, with Adam providing the highest accuracy (0.88) and RMSprop showing the lowest loss (0.2614) with strong accuracy (0.87), making these optimizers well-suited for capturing complex consumer behavior patterns. Further accuracy, loss, confusion matrix, ROC curve, and area under the curve (AUC)-demonstrate the model's capacity to capture intricate patterns. The confusion matrix highlights high precision in classification, with an overall accuracy of correctly predicting 262 out of 300 cases. The ROC curve further supports the model’s classification strength, achieving an AUC of 0.92, which confirms its high discriminatory ability. These findings suggest the ANN model is effective for deriving consumer insights, capable of delivering accurate forecasts for data-driven marketing strategies and customer behavior optimization in practical business settings. © 2024 IEEE.

关键词： Stochastic systems

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A Course Recommendation System based on User Intents and Resource Alignment 5

A Course Recommendation System based on User Intents and Res...

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5th International Seminar on Artificial Intelligence, Networking and Information technology, AINIT 2024

作者： Xie, Wenzhu Pu, Peng East China Normal University Department of Data Science and Technology Shanghai China

ISBN: (纸本)9798350385557

With the digital transformation of education in Chinese universities, a data-driven computer education revolution is emerging, aggregating high-quality courses and resources to support student development, while the phenomenon of ever-increasing AI-beyond-humans has triggered a new wave of discussion on how AI may change education. Students' demand for personalized course recommendation systems is increasing. However, existing recommendation systems face challenge of data sparsity due to limited user-course interactions, resulting in less than optimal recommendation performance. To effectively address this challenge, for the first time we introduce user intent in the field of personalized recommendations. Moreover, We have added knowledge graphs to enrich data information and proposed a resource alignment module based on the characteristics of course data. We propose a new model, KGIRA, based on these two modules. The effectiveness of our model was assessed using two course recommendation datasets, and a comparative analysis with current state-of-the-art models shows our model's superiority in several metrics, including NDCG. This indicates a significant advancement in the field of personalized course recommendation systems. © 2024 IEEE.

关键词： Recommender systems

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Design of Subpixel Computer Vision Detection Algorithm Based on Texture Image

Design of Subpixel Computer Vision Detection Algorithm Based...

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2023 International Conference on Networking, Informatics and Computing, ICNETIC 2023

作者： Wang, Junxian Mao, Kaihui Zhang, Xiaolong Lv, Wei Gao, Junhan Zhuhai College of Science and Technology Alibaba Cloud Big Data Application College Zhuhai519041 China

ISBN: (纸本)9798350313314

Testing technology based on computer vision is a new testing technology. It uses the image as a means or carrier to detect and transmit information, extracts useful signals from the image, and obtains various required parameters by processing the measured image. It has the characteristics of non-contact, full-field measurement, high precision and high degree of automation. In this paper, an optimal design scheme of sub-pixel computer vision detection algorithm based on texture image is proposed. The sub-pixel image texture is extracted by texture database, and then the sub-pixel accuracy is grayed by computer vision detection to achieve the optimal design of sub-pixel accuracy. Finally, the sub-pixel of texture image is analyzed experimentally. Finally, the experiment shows that the sub-pixel gray level is improved by 8.45% compared with the traditional algorithm in the case of texture primitives. It is of great practical significance to carry out the research of computer vision inspection design with sub-pixel as the core to speed up the improvement of vision inspection technology. © 2023 IEEE.

关键词： Image texture

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Comprehensive Image Analytics for Diversion of Forest Land 4

Comprehensive Image Analytics for Diversion of Forest Land

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Kambare, Shweta Rudrabhate, Aabha Divate, Srinath Taktode, Tapasvi Tekam, Anurag Thakare, Yatharth Vishwakarma Institute Of Technology Department of Artificial Intelligence & Data Science Pune India

ISBN: (纸本)9798331529635

This research study presents an initiative that seeks to revolutionize the process of tree enumeration and categorization, traditionally carried out manually. It harnesses the power of image analytics, the system aims to streamline the process, reduces manpower requirements, and lowers costs, all while minimizing the environmental impact of development projects. By incorporating techniques such as Binary Image Thresholding and Convolutional Neural Networks, the system categorizes and monitors trees using high-resolution satellite imagery. Analytical tools, including YOLOv8 for object detection and NDVI analysis for vegetation classification, are deployed to optimize land allocation and pathfinding. The proposed system's design is centered around efficiency and environmental consciousness. An interactive user interface is developed ensuring accessibility and ease of use. The integration of Google Maps API enables real-time monitoring of forest changes, supporting effective decision-making for deforestation and reforestation efforts. © 2024 IEEE.

关键词： Convolutional neural networks

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Cancer Regions in Mammogram Images Using ANFIS Classifier Based Probability Histogram Segmentation Algorithm

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Intelligent Automation & Soft Computing 2023年第7期37卷 707-726页

作者： V.Swetha G.Vadivu Department of Data Science and Business Systems School of ComputingSRM Institute of Science and TechnologyKattankulathur CampusChennai603203India

Every year,the number of women affected by breast tumors is increasing ***,detecting and segmenting the cancer regions in mammogram images is important to prevent death in women patients due to breast *** conventional methods obtained low sensitivity and specificity with cancer region segmentation *** high-resolution standard mammogram images were supported by conventional methods as one of the main *** conventional methods mostly segmented the cancer regions in mammogram images concerning their exterior pixel *** drawbacks are resolved by the proposed cancer region detection methods stated in this *** mammogram images are clas-sified into normal,benign,and malignant types using the Adaptive Neuro-Fuzzy Inference System(ANFIS)approach in this *** mammogram classification process consists of a noise filtering module,spatial-frequency transformation module,feature computation module,and classification *** Gaussian Filtering Algorithm(GFA)is used as the pixel smooth filtering method and the Ridgelet transform is used as the spatial-frequency transformation *** statistical Ridgelet feature metrics are computed from the transformed coefficients and these values are classified by the ANFIS technique in this ***,Probability Histogram Segmentation Algo-rithm(PHSA)is proposed in this work to compute and segment the tumor pixels in the abnormal mammogram *** proposed breast cancer detection approach is evaluated on the mammogram images in MIAS and DDSM *** the extensive analysis of the proposed tumor detection methods stated in this work with other works,the proposed work significantly achieves a higher *** methodologies proposed in this paper can be used in breast cancer detection hospitals to assist the breast surgeon to detect and segment the cancer regions.

关键词： Mammogram cancer gaussian filter ridgelet classification

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Optimized Neural Network for Automated Cardiovascular Disease Classification: Meta-heuristic Enabled Model

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SN Computer science 2025年第1期6卷 1-14页

作者： Kusuma, S. Jothi, K.R. Department of Computer Science & Engineering (Data Science) Madanapalle Institute of Technology & Science Madanapalle India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Cardiovascular disease remains a major issue for mortality and morbidity, making accurate classification crucial. This paper introduces a novel heart disease classification model utilizing Electrocardiogram (ECG) signals. The model consists of two main steps: feature extraction using a refined Lion Algorithm with New Mutated evaluation (LA-NM) and classification through an ensemble approach combined with a Neural Network (NN). The proposed model significantly outperforms traditional classifiers. At 80% training data, it achieves accuracy improvements of 8.4% to 3.11% over Neural Network (NN), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and ensemble models. At 90% training data, it shows precision improvements ranging from 1.03% to 0.31% better than the comparison models and specificity improvements ranging from 13.92% to 0.52%. These results underscore the effectiveness of the model and suggest its potential for further development in personalized treatment recommendations based on patient history. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Ensemble models Feature extraction Heart disease classification Neural network Optimization

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