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A Novel Music Recommendation System Using Filtering Techniques

Informatica (Slovenia) 2024年第4期48卷 595-610页

作者： Vashishtha, Srishti Varshney, Deepika Sarin, Eva Kaur, Simran Computer Science and Engineering Dept. Bharati Vidyapeeth’s College of Engineering GGSIPU New Delhi India Computer Science and Engineering Dept. Jaypee Institute of Information Technology Noida India

With the growth of the World Wide Web, a large amount of music data is available on the Internet. A large number of people listen to music online rather than downloading and listening offline. But only some sites provide personalized and accurately recommended songs while they listen to an auto-playing playlist. Hence the need for recommendation systems arises. Two approaches can be applied to the recommendation system: content based filtering and collaborative filtering. While in content-based filtering approach, analysis on the songs’ content which has been preferred by the user in history is done and the songs with relative similarity are recommended. While latter suggests songs that certain users of similar listening pattern have preferred. But collaborative filtering-based recommendation systems not only requires time to attain stability but also might recommend unsuitable music because it is not personalized to each user’s preference. Also, the latter requires the songs to be listened by a few users already to recommend it any other user. Hence for overall analysis, answers to few questions need to be implemented in recommendation systems: the very first one is how the properties should be analyzed, next one is how the analysis should be done and last is how the songs related to the user’s preference should be chosen. So, for suggesting the better system which solves these three questions as well as provides better and personalized recommendations, In this paper we build a collaborative filtering as well as content based filtering recommendation system, where various factors determine the essence of the songs, i.e. liveliness, keys used, loudness, pitch, valence, etc., are analyzed for comparison. From the experimental analysis in has been identified that content-based filtering technique performed best on KNN machine learning classifier with accuracy of 85%. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Collaborative filtering

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Skin Melanoma diagnosis using machine learning and ABCD rule 4

Skin Melanoma diagnosis using machine learning and ABCD rule

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4th International Conference of Science and information technology in Smart Administration, ICSINTESA 2024

作者： Kadeem, Zahra B. Mosa, Qusay O. College of Computer Science and Information Technology Dept. Computer Science Diwaniyah Iraq

ISBN: (纸本)9798350376111

Skin cancer, being the most predominant type of malignant growth, represents more than half of all cancer instances. While melanoma is less common than other skin cancers, it poses the highest risk due to its tendency to metastasize and potentially result in mortality. Nonetheless, early detection significantly increases the chances of curing melanoma. The detection of melanoma poses a considerable challenge, primarily attributed to the minimal interclass variance observed between images of melanoma and non-melanoma, alongside the heightened interclass variance within melanoma images. Hence, it is imperative to formulate a computer-aided Diagnosis (CAD) to identify such lesions and facilitate early disease diagnosis to enhance the probability of patient survival. The proposed methodology consists of a sequence of four distinct phases. The initial stage involves preprocessing, which incorporates the application of filtering and contrast enhancement algorithms such as the Median filter, Dull razor, and Histogram Equalization. The segmentation process proceeds by utilizing Otsu methods in order to detect any anomalies. Subsequently, the following phase involves the extraction of features through GLCM for textural attributes and ABCD base parameters, encompassing asymmetry, border irregularity, color, and diameter. Following this, a model based on artificial neural networks (ANN) is developed by leveraging these extracted traits to discern instances of skin cancer within the provided dataset. This methodology achieves a commendable overall accuracy rate of 99%. Dermatologists may derive significant assistance in their clinical decision-making procedures from this promising breakthrough. © 2024 IEEE.

关键词： Lung cancer

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DEMONSTRATING EXPERIMENT FOR SOCIAL IMPLEMENTATION OF QUIET ROOM USING EASY-TO-BUILD STRUCTURE

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AIJ Journal of technology and Design 2024年第75期30卷 915-920页

作者： Kimura, Shoko Ito, Kenichi Natsume, Yoshinori Fujii, Ayaka Kitagawa, Keisuke Dept. of Architecture Nagoya Institute of Technology M.Knowledge of Science Academic Specialist International Architectural Education Platform The Univ. of Tokyo Japan Artificial Intelligence Research Center Dr.Information Science and Technology Japan Graduate School of Frontier Sciences The Univ. of Tokyo M.Environmental Studies Japan Dept. of Architecture Nagoya Institute of Technology Japan Dept. of Architecture Nagoya Institute of Technology Japan

Many people with autism or developmental disabilities have sensory hypersensitivity. The spread of "quiet rooms" in which they can feel at ease is necessary to support them, but their high setup cost is a hurdle. We developed the "Inclusive Quiet Room" which is a new concept of a portable quiet room that combines an easy-to-construct instant house, immersive videos, and relaxing sounds. In this paper, we investigate the effectiveness and usefulness of the Inclusive Quiet Room through the analysis of questionnaires and the heart rate of people who experienced it at an exhibition. © 2024 Architectural Institute of Japan. All rights reserved.

关键词： Diseases

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VascularVision: Mixed Reality Based Teaching and Learning Environment for Cardiovascular Conditions 6

VascularVision: Mixed Reality Based Teaching and Learning En...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Nissanka, Hashith Wijayasundara, Sajini Rajarathne, Jude Vidanagamage, Sahan Arachchi, Didula Thanaweera Abeygunawardhana, Pradeep K. W. Sri Lanka Insitute of Information Technology Dept. Information Technology Kandy Sri Lanka Sri Lanka Insitute of Information Technology Dept. Information Technology Colombo Sri Lanka Sri Lanka Insitute of Information Technology Dept. Information Technology Malabe Sri Lanka Sri Lanka Insitute of Information Technology Dept. Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798331517878

Developments in Augmented Reality (AR) and Virtual Reality (VR) have the potential to revolutionize medical education, especially in the areas of Coronary Angiography and Cardiac Auscultation. By giving medical and nursing students realistic, interactive experiences, the suggested VR and AR settings seek to modernize medical education by addressing the issues regarding the lack of real-world practice. Interactive 3D models, such as a realistic virtual body, rotatable heart models, ECG recordings, and real-life heart sounds, are available in the VR Cardiac Auscultation Environment. With the use of these components, students can practice diagnosing diseases including bradycardia, tachycardia, and cardiac murmurs in a dynamic and risk-free setting, improving their diagnostic skills. Similar to this, the AR Cardiac Auscultation Application combines interactive quizzes, machine learning for diagnostic verification, and real-time ECG scans to provide a powerful tool for teaching students how to recognize and understand various heart diseases and noises, this also can be a verification tool for junior doctors. By giving students firsthand experience with intricate medical procedures, virtual reality and augmented reality platforms offer a compelling and realistic approach to medical education. According to the research, these instruments can greatly enhance diagnostic proficiency, which will ultimately result in better educational outcomes and more qualified healthcare workers. © 2024 IEEE.

关键词： Students

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A review on 3D image reconstruction on specific and generic objects

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Materials Today: Proceedings 2023年 80卷 2400-2405页

作者： Shalma, H. Selvaraj, P. Dept. of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Dept. of Information Technology SRM Institute of Science and Technology Kattankulathur Chennai India

With the emergence of various techniques involved in deep learning the researchers of computer vision tends to focus on the strategies such as object recognition and segmentation of image. This has inclined to accomplish the deep learning techniques in 3D reconstruction of both specific and generic objects. As the space for reconstruction of 3D images either in single or multi view has envisioned the researchers to concentrate on the available technologies used for reconstruction. With the available built in methods and technologies in deep learning, the performance of the proposed methods were reviewed and analyzed using several parameters. As the remaking of 2D images is still in the beginning stage, it is important to study the 3D shape representations, various network architecture, methodologies and approaches behind 3D reconstruction. In this work a review of deep learning methods for single or multiple RGB images of specific and generic object 3D reconstruction was done. Several methods and their importance were also discussed along with the challenges encountered and with further research directions. This paper critically analyzes the various 3D Shaped Representations, 3D Data Network architectures, dept. Estimation methods, Multi View Representations and the Data Representation Techniques. © 2021

关键词： Object recognition

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Active Element Arrangement and Prediction Model for Channel Estimation in IRS-Assisted Systems

Active Element Arrangement and Prediction Model for Channel ...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Tsuchiya, Yoshihiko Suga, Norisato Uruma, Kazunori Fujisawa, Masaya Tokyo University of Science Dept. of Information and Computer Technology Tokyo Japan Shibaura Institute of Technology Dept. of Information and Communication Engineering Tokyo Japan Kogakuin University Dept. of Computer Science Tokyo Japan

ISBN: (纸本)9798350350821

Intelligent reflecting surfaces (IRS) that can dynamically control the phase of radio waves and reflect them are attracting attention to realize non line-of-sight communication in the high-frequency band. Channel state information (CSI) is estimated to properly control the phase, however the overhead of the estimation reduces communication efficiency. To reduce the overhead, IRS systems with active elements that perform channel estimation are considered. One approach treats the partial CSI obtained from the active elements as an image and utilizes a super-resolution technique to predict the entire CSI. However, because the active elements are arranged in grid, prediction is difficult when the active elements spacing and the CSI fluctuation period match. In this study, we propose a new arrangement that gathers the active elements in the center of the IRS. Furthermore, we regard prediction as an image-inpainting problem that recovers missing pixels from known pixel values, and we propose applying the U-Net to solve this problem. Numerical experiments show that the proposed method outperforms conventional methods in prediction accuracy and spectral efficiency. © 2024 IEEE.

关键词： Channel estimation

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IRS Controlling Method With Machine Learning Based on Position information 13

IRS Controlling Method With Machine Learning Based on Positi...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

ISBN: (纸本)9798350355079

Intelligent Reflecting Surfaces (IRS) is attracting attention for wireless communications at high-frequency band. IRS can control radio propagation by reflecting radio waves and shifting their phase. As one of the methods to control IRS, location-based methods have been proposed. However, to achieve highly accurate IRS control by the methods, the assumed propagation model must match the actual propagation environment. This study proposes a method to control the IRS from location information adapted to the propagation environment by using machine learning. Numerical experiments confirm that the proposed method can appropriately control IRS regardless of the propagation environment. © 2024 IEEE.

关键词： Adversarial machine learning

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Improvising Early Detection of Diabetes through Deep Learning on Retinal Fundus images 3

Improvising Early Detection of Diabetes through Deep Learnin...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Balaji, Krishna Ganesh, Maniprabha Shankar Murali, Daneesh Pachiappan, Kalaivani Iyyanar, P. Jayakumar, Nithya Kongu Engineering College Dept. of Computer Science Erode India Sona College of Technology Dept. of Information Technology Salem India K.S Rangasamy College of Technology Dept. of Information Technology Tiruchengode India

ISBN: (纸本)9798331509675

Diabetes is a chronic disease whose timely and accurate diagnosis will prevent serious complications from health. This paper explores using iridology principles in a deep learning method to detect diabetes from retinal images in order to streamline the process and automate the procedure. The objective here is to create a non-invasive procedure for early and reliable detection of the disease. Deep learning in medical imaging is the new way to interfere with conventional modes of diagnostics. The retinal imaging is non-invasive and has, over the years, been put to use in medical examinations, and its potential is now absolutely being utilized in the early diagnosis of diabetes. A retinal analysis that is based on principles, integrating them into iridology, concentrating on the patterns and signs portrayed in the iris for possible reflections of a general health condition, is aimed at achieving earlier indications of diabetes in a more effortless and rapid way. Models such as ResNet50, ResNet101, and EfficientNetB7 were precious but actually, it was InceptionV3 that delivered a great accuracy of 97.14%. This result proves the feasibility application of InceptionV3 for diagnosing diabetes in the early stages, thus eventually providing an easy and non-invasive way of moving forward to better health. © 2025 IEEE.

关键词： Ophthalmology

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Enhancing G.C.E. Ordinary Level Mathematics Through Adaptive Learning in Sri Lanka 6

Enhancing G.C.E. Ordinary Level Mathematics Through Adaptive...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Fernando, P.S.D. Amarasekara, H.P.D.E.R. Rathnakumara, M.D.B. Amarakoon, B.W. Rajapaksha, Samantha Krishara, Jenny Dept. of Information Technology Malabe Sri Lanka Dept. of Computer Systems Engineering Sri Lanka Institute of Information and Technology Malabe Sri Lanka

ISBN: (纸本)9798331517878

Proficiency in mathematics is essential for both academic and professional success, yet many students find the subject difficult, especially when preparing for important exams like Sri Lanka's General Certificate of Education (G.C.E.) Ordinary Level examination. In Sri Lanka, the students show poor pass rates in Ordinary Level mathematics examination. In order to improve mathematics education for O/L students, this research suggests an innovative mobile application that makes use of adaptive learning strategies. This application features components like a comprehensive knowledge bank that is curriculum-mapped, pre-assessment tools for identifying knowledge shortages, a weaknesses based lesson recommendation for personalized lesson suggestions, a custom developed chat assistant to interact with the knowledge bank, interactive 3D visualizations and justifications of complex concepts, and platforms for problem-solving, writing, and time management skill development. The suggested adaptive learning strategy makes use of multimedia tools and datadriven insights to tailor instruction to each student's unique needs, improving math comprehension and readiness for the O/L examination in Sri Lanka. © 2024 IEEE.

关键词： Students

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Agritech Harmony: Real-time ESP32 Automation and Cloud Computing for Socially-Informed Crop Suggestions 5

Agritech Harmony: Real-time ESP32 Automation and Cloud Compu...

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5th IEEE International Conference for Emerging technology, INCET 2024

作者： Bawadkar, Vishal Verma, Aman Pawar, Aditya Phatale, Utkarsh Sadaphule, Prashant Dept. of Computer Engineering AISSMS Institute of Information Technology Pune India

ISBN: (纸本)9798350361155

Accurately predicting long-term crop yield trends remains a crucial challenge in optimizing agricultural practices and ensuring food security. This paper proposes a novel framework that merges real-time data acquired from Internet of Things (IoT) sensors with a Bi-directional LSTM model with an attention mechanism to achieve this objective. This architecture extracts insights from historical and future environmental conditions and prioritizes critical factors influencing yield through attention weighting. The framework incorporates an ESP32-managed sensor network measuring humidity, temperature, moisture, rainfall, and NPK levels, providing a continuous stream of real-time field data. Evaluation of real-world datasets demonstrates the framework's superiority over traditional methods, yielding significantly higher prediction accuracy and exposing key drivers of yield fluctuations through attention analysis. An accessible API facilitates seamless integration with farm management systems, empowering farmers with data-driven decision-making capabilities. This paper highlights the transformative potential of integrating deep learning, real-time IoT data, and APIs for revolutionizing agriculture, paving the way for a more sustainable and resilient agricultural futur. © 2024 IEEE.

关键词： Crops

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