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2023 IEEE International Conference on Network, multimedia and Information Technology, NmITCON 2023

作者： Goudar, somanath R shidaganti, Ganeshayya M.S. Ramaiah Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350300826

There are now an excessive number of courses available to students of all ages and skill levels as a result of the development of online education. Finding the courses that best meet their needs has become difficult for students as a result of this. In order to solve this problem, we describe an educational recommendation system (ERs) that uses cosine similarity (Cs) and content-based filtering (CBF) to suggest online courses to users based on their search terms. This approach uses a dataset of online courses that includes information about several course parameters, including course title, enrolment figures, price, reviews, duration, and topic matter. In this paper, we begin with a comprehensive review of existing literature related to ERs. We then describe methodology and approaches to developing the system along with the process of dataset selection and pre-processing steps, feature extraction, and the evaluation metrics used to assess the system.s.performance. The system was able to identify and recommend courses that were highly relevant to the user's query, with a precision rate of over 90%. © 2023 IEEE.

关键词： Recommender systems

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Information Compression in the AI Era: Recent Advances and Future Challenges

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IEEE Journal on selected Areas in Communications 2025年第7期43卷 2333-2348页

作者： Chen, Jun Fang, Yong Khisti, Ashish Özgür, Ayfer shlezinger, Nir McMaster University Department of Electrical and Computer Engineering HamiltonONL8S 4K1 Canada Chang'an University School of Information Engineering Shaanxi Xi'an710064 China University of Toronto Department of Electrical and Computer Engineering TorontoONM5S 3G4 Canada Stanford University Department of Electrical Engineering StanfordCA94205 United States Ben-Gurion University of the Negev School of Electrical and Computer Engineering Be'er Sheva8410501 Israel

This survey article focuses on the emerging connections between machine learning and data compression. While the fundamental limits of classical (lossy) data compression are well-established through rate-distortion theory, recent advancements have uncovered new theoretical analyses and application areas inspired by machine learning. We review recent works on task-based and goal-oriented compression, rate-distortion-perception theory, and compression for estimation and inference. Deep learning-based approaches have provided natural, data-driven methods for compression. Accordingly, we survey recent efforts in applying deep learning techniques to task-based or goal-oriented compression, as well as image/video compression and transm.s.ion. Additionally, we discuss the potential use of large language models for text compression. Finally, we outline future research directions in this prom.s.ng field. © 1983-2012 IEEE.

关键词： signal distortion

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Through the Looking Glass—The 2025 Edition: Trends in solid-state circuits from IssCC

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IEEE solid-state Circuits magazine 2025年第1期17卷 97-118页

作者： mirabbasi, shahriar Fujino, Laura C. University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada University of Toronto Department of Electrical and Computer Engineering TorontoONM5S 1A1 Canada

The International solid-state Circuits Conference (IssCC) is the flagship conference of the IEEE solid-state Circuits society. The theme for IssCC 2025 is "The silicon Engine Driving the Artificial Intelligence (AI) Revolution." This theme further highlights how today’s IC research and development can contribute to the solid-state foundation upon which AI is rapidly evolving and pervasively entering people’s lives. IssCC 2025 is the 72nd edition of IssCC and is more than ever focused on innovation, in particular, innovative solutions that facilitate the efficient implementation of AI algorithms. To enhance and support the widespread use of AI, IssCC 2025 promotes and shares new circuit ideas that advance the state of the art in IC design and further equip AI systems with improved functionality and efficiency. © 2025 IEEE.

关键词： Timing circuits

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Unlocking the m.s.eries of speech: A Hybrid RNN-CNN model for Vocal Language Identification

Unlocking the Mysteries of Speech: A Hybrid RNN-CNN Model fo...

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2023 IEEE International Conference on Distributed Computing, VLsI, electrical Circuits and Robotics, DIsCOVER 2023

作者： shidaganti, Ganeshayya Harebailu, Pathanjali Kishore Kumar, K. Akmal, mohammad Nagesh, D. M.S. Ramaiah Institute of Technology Dept. of Computer Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350340129

speech is the primary form of communication, and being able to identify the language used in an audio sample is crucial in numerous fields. Language identification serves a purpose in speech recognition, language translation, voice assistants, and many other domains. Traditional language identification systems rely on statistical or acoustic models, which frequently require extensive domain-specific knowledge and have limitations in accuracy and resilience. By employing a hybrid Recurrent neural network and Convolutional Neural Network, this paper aims to develop a novel method of language identification. The suggested approach entails spectrogram preprocessing of audio samples, mel-Frequency cepstrum coefficient extraction, and Convolutional Neural Network and Recurrent neural network architecture model training on a sizable dataset. Recurrent neural networks are good at capturing temporal dependencies in data, while Convolutional Neural Networks are better at capturing spatial patterns. This methodology successfully identifies the languages with an overall accuracy of 93%, proving the efficacy of the proposed model. © 2023 IEEE.

关键词： Recurrent neural networks

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Analysis of Bio Inspired Based Hybrid Learning model for software Defect Prediction

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sN computer science 2024年第7期5卷 1-22页

作者： shankar, sahana P. Chaudhari, shilpa shashikant Department of Computer Science and Engineering M.S. Ramaiah Institute of Technology (Affiliated to Visvesvaraya Technological University Belgaum) Bengaluru India Department of Computer Science and Engineering Ramaiah University of Applied Sciences Bengaluru India

The software’s quality can be ensured through software testing, which is one of the critical methods. However, it was found that testing consum.s.more than half of the project's total expenses. Effective and efficient software testing utilizes minimal software resources to find as many flaws in the software system as possible. This paper presents research on software defect prediction using hybrid machine learning algorithm. The algorithm incorporates the benefits of supervised as well as unsupervised learning methodology that improves the accuracy of defect prediction. The study uses publicly available datasets from the open source PROm.s. repository provided by NAsA mDP to perform machine learning analysis and maximize accuracy. In particular, the study suggested a hybrid learning model and conducted a comparative analysis among Naïve Bayes, multilayer Perceptron, Random Forest, CNN, Adaboost, and KNN on four different datasets namely Cm1, KC1, KC2 and PC1. With respect to F1-score, accuracy, precision, and recall, the results show that the hybrid method performed better than the other methods. The study achieved an accuracy of 93.67% (BAT + CNN) on PC1 dataset, 92% on (Bat + CNN) for Cm1 and KC1 respectively. This indicates that the hybrid learning technique of (BAT + CNN) is more potent than other methodologies in prediction of defects. The research can help software development teams identify potential bugs early on, leading to efficient and effective software creation. One useful approach for predicting software defects is the hybrid machine learning method, which can lead to improved software quality and lower maintenance costs. This aids the software testing phase by predicting the faulty modules with better accuracy thereby reducing the testing effort. The main objectives of the research include applying different machine learning algorithms and methods to the given datasets and determine the m.s. effective ones. © The Author(s), under exclusive licence to

关键词： Bat CNN PROm.s. sparrow search squirrel search Testing

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ChatGPT: Information Retrieval from Image using Robotic Process Automation and OCR 7

ChatGPT: Information Retrieval from Image using Robotic Proc...

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7th International Conference on Intelligent Computing and Control systems, ICICCs 2023

作者： shidaganti, Ganeshayya sanjana, R. shubeeksh, K. monish Raman, V.R. Thakshith, Valmeeki M.S. Ramaiah Institute of Technology Dept. of Computer Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350397253

ChatGPT hit 1m users in just 5 days once it went public, the leading companies such as Facebook, Instagram, spotify, Netflix took several months to years to achieve this mark. As a result of the conversational AI Chabot being hailed as a technological revolution, ChatGPT has emerged as one of the year's m.s. popular buzzwords. This research study intends to initiate interaction with ChatGPT more convenient to users by using user sent WhatsApp images containing text as input by converting it into text-based prompt which is accepted by ChatGPT and provide the user with the response generated by ChatGPT. This is achieved by integrating RPA to automate an in-depth examination of images for information discovery using OCR to extract text and ChatGpt to retrieve information. This research study also focuses on providing a quick response to the user by comparing images with previous users' inputs and sending the same response in case there is repetition of images which increases efficiency in terms of computation resources too. The proposed solution is designed to provide a quick suitable response to the user inscribed text in the image. © 2023 IEEE.

关键词： Robotics

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Empowering Rural minds: Leveraging Predictive Analytics to Bridge Urban-Rural Educational Disparities Through Financial Assistance 2

Empowering Rural Minds: Leveraging Predictive Analytics to B...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： shidaganti, Ganeshayya Harebailu, Pathanjali Kumar, K. Kishore mohammad, Akmal M.S. Ramaiah Institute of Technology Dept. of Computer Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350308846

Access to quality education is a critical factor in individual empowerment, economic development, and societal progress. However, students from rural backgrounds often face significant obstacles when it com.s.to pursuing technical education, primarily due to financial barriers. Financial barriers act as a deterrent, restricting the participation of rural students in technical education programm.s. This paper presents an innovative approach to address this issue by leveraging the power of artificial intelligence (AI) and predictive analytics to provide equitable financial assistance and scholarships for technical education. By adopting a comprehensive strategy, this paper addresses the biases, transparency shortcomings, and contextual factors that have been prevalent in previous algorithm methods. The strategy employed in this study involves a multi-faceted approach, integrating various methodologies to achieve optimal outcom.s. The key steps encompassed in the methodology include algorithmic fairness, model training and selection, transparency and interpretability, human-in-the-loop review, contextualised assessment, continuous learning, and feedback loops. Adversarial Debiasing is utilised to mitigate biases and ensure a fair decision-making process. To enhance transparency and interpretability, rule-based systems and LImE (Local Interpretable model-Agnostic Explanations) are employed. Human-in-the-loop review is an integral part of the methodology, ensuring that AI-driven decisions are validated and refined by human experts. The expected outcome of this research is an AI-enabled financial assistance system that democratises technical education by making it more accessible to students from rural backgrounds. The implementation of this model can have a transformative impact on education equality, empowering rural minds to pursue their academic dreams. © 2023 IEEE.

关键词： Adversarial Debiasing Decision Trees Logistic Regression Predictive Analytics Rule-based systems and LImE

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Performance Evaluation of Various Approaches for Disease Prediction using Weka 15

Performance Evaluation of Various Approaches for Disease Pre...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kalmegh, sushil Tayade, Prerna s. Bhagat, Amol P. PG Department of Computer Science Sant Gadge Baba Amravati University Amravati M.S. 444 602 India Department of Information Technology Prof Ram Meghe College of Engineering & Management Amravati M.S. Badnera444 701 India

ISBN: (纸本)9798331300579

This paper introduces Weka, which uses data mining algorithms to classify viruses. This paper introduces Weka's proposed data mining algorithm for classifying viruses. This paper presents a review and evaluation of a data mining algorithm for classifying diseases using Weka. The data mining algorithms ZeroR, Linear Regression, multilayer Perceptron, Random Forest, simple K-means, Hierarchical Clustering, and Farthest First are proposed for disease classification using Weka. Weka acts as the judge in evaluating these algorithms, which possess the capability to accurately classify any ailment. Their performance is scrutinized based on both precision and the time taken to classify diseases. © Grenze scientific society, 2024.

关键词： classification Data mining data preprocessing disease prediction Weka tool

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All-perovskite tandems go bifacial

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Light(science & Applications) 2023年第1期12卷 10-11页

作者： suhas mahesh Bin Chen Edward H.sargent The Edward S.Rogers Department of Electrical and Computer Engineering University of TorontoTorontoON M5S 3G4Canada Department of Chemistry Northwestern UniversityEvanstonIL 60208USA Department of Electrical and Computer Engineering Northwestern UniversityEvanstonIL 60208USA

All-perovskite tandem cells are attractive candidates for next-generation photovoltaic technology as they hold the potential to combine high-efficiency with low weight and reduced energy-payback ***,researchers show that such tandem cells can be engineering to be bifacial,allowing them to utilize stray light reflected off the surrounding environment,resulting in a 17%boost in the power output.

关键词： utilize attractive perovskite

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Hierarchical Federated Learning Architectures for the metaverse

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ZTE Communications 2024年第2期22卷 39-48页

作者： GU Cheng LI Baochun Department of Electrical and Computer Engineering University of TorontoToronto M5S2E8Canada

In the context of edge computing environments in general and the metaverse in particular,federated learning(FL)has emerged as a distributed machine learning paradigm that allows multiple users to collaborate on training a shared machine learning model locally,eliminating the need for uploading raw data to a central *** is perhaps the only training paradigm that preserves the privacy of user data,which is essential for computing environments as personal as the ***,the original FL architecture proposed is not scalable to a large number of user devices in the metaverse *** mitigate this problem,hierarchical federated learning(HFL)has been introduced as a general distributed learning paradigm,inspiring a number of research *** this paper,we present several types of HFL architectures,with a special focus on the three-layer client-edge-cloud HFL architecture,which is m.s. pertinent to the metaverse due to its delay-sensitive *** also examine works that take advantage of the natural layered organization of three-layer client-edge-cloud HFL to tackle some of the m.s. challenging problems in FL within the ***,we outline some future research directions of HFL in the metaverse.

关键词： federated learning hierarchical federated learning metaverse

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