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24th IEEE International Conference on data Mining, ICDM 2024

作者： Lai, Kai-Huang Xi, Wu-Dong Xing, Xing-Xing Wan, Wei Wang, Chang-Dong Chen, Min Guizani, Mohsen School of Computer Science and Engineering Sun Yat-sen University Guangzhou China NetEase Games China UX Center NetEase Games Guangzhou China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Machine Learning Department Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331506681

In the evolving landscape of sequential recommendation systems, the application of Large Language Models (LLMs) is increasingly prominent. However, current attempts typically utilize general-purpose LLMs, which present a mismatch in capability and a large semantic gap relative to the specialized needs of recommendation tasks. To tackle these issues, we introduce RecCoder, an innovative model that reformulates sequential recommendation as a code completion task. This approach leverages the superior reasoning capability of code LLMs as a backbone, aligning well with the requirements of recommendation systems. To bridge the semantic gap, RecCoder creates extra tokens for each item and employs item content to initialize token embeddings. Furthermore, we have developed a suite of Semantic Adaptation Fine-tuning tasks, tailored to enhance the model's acquisition of both content and collaborative semantic information, thus aligning the model's intrinsic capabilities with the unique demands of recommendation tasks. Through extensive testing on three public datasets, RecCoder has shown remarkable improvements over existing models in terms of recommendation accuracy and efficiency. This success highlights the substantial yet previously underexplored potential of code LLMs in improving recommendation accuracy and efficiency, suggesting a promising new direction for future research in this area. The implementation code is accessible at https://***/AllminerLab/Code-for-RecCoder-master. © 2024 IEEE.

关键词： Semantics

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Natural Language Processing in Virtual Assistants Current Approaches and Challenges

Natural Language Processing in Virtual Assistants Current Ap...

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2024 International Conference on science, Technology, Engineering and Management, ICSTEM 2024

作者： Solaimalai, Gautam Shanthi, J. Maria Kumar, Sampath S. Khabiya, Priyanka Geetha, K. Talele, Gokul U.S. Bank GA30041 United States Department of Artificial Intelligence and Machine Learning J. B. Institute of Engineering and Technology Telangana Hyderabad500075 India Department of Computer Science and Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore641202 India Department of Computer Science Engineering Mandsaur University Madhya Pradesh Mandsaur458002 India Department of Computer Science G.T.N. Arts College Tamil Nadu Dindigul624005 India Department of Data Science Organization name Accenture Maharashtra Pune411013 India

ISBN: (纸本)9798350376913

The proliferation of virtual sidekicks in colorful disciplines has prodded a swell of interest in advancing Natural Language Processing(NLP) ways to enhance their effectiveness. This paper provides a comprehensive review of the current approaches and challenges encountered in integrating NLP into virtual sidekicks. It begins by outlining the foundational generalities of NLP and its vital part in enabling mortal- suchlike relations with virtual sidekicks. latterly, it delves into a discussion of the different methodologies employed in NLP for understanding and generating mortal language, ranging from rule-grounded systems to deep literacy models. likewise, the paper highlights the crucial challenges such as nebulosity resolution, environment understanding, and handling different verbal variations that stymie the flawless functioning of virtual sidekicks. By synthesizing perceptivity from recent exploration trials, this paper offers precious perspectives on the state-of-the-art NLP ways and directions for unborn exploration to overcome the challenges and propel the development of further intelligent and intuitive virtual sidekicks. © 2024 IEEE.

关键词： Deep learning

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Big data in Social Media: Analyzing Trends, Patterns and Challenges 2

Big Data in Social Media: Analyzing Trends, Patterns and Cha...

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2nd International Conference on machine learning and Autonomous Systems, ICMLAS 2025

作者： Jikar, Nayan Tale, Yash Tale, Abhay Barhate, Aditya Verma, Prateek Jikar, Aman Datta Meghe Institute of Higher Education and Research Sawangi Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Sawangi Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

The huge volumes of data produced in social media provide both new possibilities and challenges to analytics. The present paper emphasizes Big data analytics and machine learning (ML) methods to uncover trends, patterns, and user behavior from social media-related data. Such vital concepts, like the 'Four Vs' of Big data: Volume, Velocity, Variety, and Veracity, and innovative tools for datacollection, storage, and preprocessing are covered here as well. Sentiment analysis, trend forecasting, and user behaviormodeling are some of the examples of what is possible using ML and Artificial Intelligence (AI) for deriving actionable insights. Marketing campaigns, crisis communications, and political campaigns are some of the scenarios that are covered to bring out real world applications of such technologies, along with their transformative effects. Lastly, data privacy, quality, and scalability issues are noted, along with ethical concerns and biases. Directions for the future point to developments in real-time data analysis, anonymization, and the changing role of AI in social media platforms. This research emphasizes the significance of Big data and machine learning in driving innovation and decision-making in the digital era. © 2025 IEEE.

关键词： Tweets

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Neural Network Estimation Model to Optimize Timing and Schedule of Software Projects

Neural Network Estimation Model to Optimize Timing and Sched...

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2021 IEEE International Conference on Smart Information Systems and Technologies, SIST 2021

作者： Hamada, Mohamed A. Abdallah, Abdelrahman Kasem, Mahmoud Abokhalil, Mohamed International It University Department of Information System Kazakhstan Department of Machine Learning Data Science Satbayev University Kazakhstan Department of Information Technology Assiut University Egypt

ISBN: (纸本)9781728174709

Software projects have a probability of high failure rates that appear to linger around 60% for significant IT projects. Estimating time and project schedule are crucial tasks and extremely influence the project outcomes. Artificial Intelligence now can provide multiple solutions for most problems of software projects. This article aims to develop a Neural Network estimation model to manipulate the problem of timing for software projects. The model can predict the estimation value of project time which optimizes the scheduling process, the developed model achieved high accuracy after testing through the test datasets. © 2021 IEEE.

关键词： Project management

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AI-Optimized Placement and Routing for PCB Design 9

AI-Optimized Placement and Routing for PCB Design

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9th International Conference on science, Technology, Engineering and Mathematics, ICONSTEM 2024

作者： Sathyaraj, P. Arulkumar, S. Ajina, A. Beshir, Kedir Umasankar, L. Arularasan, A.N. Rmk College of Engineering And Technology Department of Electronics And Communication Engineering Tamil Nadu Puduvoyal601206 India Wachemo University Department of Electrical And Computer Engineering Ethiopia M S Ramaiah Institute of Technology Department of Artificial Intelligence And Machine Learning Bangalore560054 India S.A. Engineering College Department of Electrical And Electronics Engineering Tamil Nadu Tiruvallur600077 India Panimalar Engineering College Department of Artificial Intelligence And Data Science Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350365092

Rapid electronic device development requires more complicated and densely packed PCB designs. These systems need properly placed and connected electrical components for best performance and reliability. Complexity and time restrictions are rendering manual methods ineffective. Therefore, AI-based PCB insertion and routing automation methods have been developed. This research suggests using AI to optimize PCB component arrangement and routing. The system uses machine learning and optimization to improve design efficiency. The AI system creates layouts that match performance requirements, decrease signal interference, and meet manufacturing limits using previous design data and user preferences. A learning module that updates its knowledge base, an algorithm that chooses between design objectives, and real-time user input are examples. Signal integrity, thermal issues, and manufacturability are considered while modifying design requirements. Evaluation of the suggested methodology shows considerable gains in design efficiency, time-to-market, and electronic system performance. This research improves AI-automated PCB design, enabling more complex and effective electrical systems. AI in PCB design will revolutionize the industry by solving complicated design problems and speeding up the development of cutting-edge electronics. © 2024 IEEE.

关键词： Reinforcement learning

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Precision Land Use and Land Cover Mapping with a High-Accuracy Time Series LSTM Model

Precision Land Use and Land Cover Mapping with a High-Accura...

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

作者： Adla Aryan Prakash RV Srinivas Aluvala Venigalla Shashank Chowdary Department of Artificial Intelligence & Machine Learning Ardhaman College of Engineering Hyderabad Telangana India Department of Computer Science & Artificial Intelligence SR University Warangal Telangana India Department of Computer Science and Engineering (Data Science) Rajeev Gandhi Memorial College of Engineering and Technology Nandyal India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

This paper introduces a machine-learning-based LULC classifying model developed with a time series data set, trained on a diverse set of agriculture and non-agricultural classes. It used a strong generalization capability employing an LSTM architecture; that is, it achieved accuracy of 90.04% in training and also validation accuracy of 90.55%. The ultimate accuracy was obtained at the minimal loss of 0.1909 at the accuracy of 95.58%, thereby validating its usability for LULC pattern classification. This model has a wide-range of potential application in environmental monitoring and resource management. Future work involves introducing more variables into the dataset, the inclusion of other environmental factors, and fine-tuning of the model towards more general applications in geospatial applications. Thus, this work advances the current state of remote sensing and LULC classification to present an effective tool in sustainable land management.

关键词： Deep learning Accuracy Image processing Time series analysis Land surface Spatial databases Environmental monitoring Resource management Remote sensing Long short term memory

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A Survey of Advanced Algorithms and Experimental Approaches for Kidney Abnormality Detection in Ultrasound Imaging

A Survey of Advanced Algorithms and Experimental Approaches ...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： N. Thulasi Chitra Sreenivasulu Gogula Sukla Satapathy Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Hyderabad Telangana India Department of Computer Science and Engineering(Data Science) Vardhaman College of Engineering Hyderabad Telangana India Department of Artificial Intelligence and Machine Learning Koneru Lakshmaiah Education Foundation Hyderabad Telangana India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Kidney abnormalities, such as cysts, tumors, and stones, are critical health concerns requiring early and precise detection. This study reviews advanced algorithms and experimental approaches for kidney abnormality detection in ultrasound imaging, emphasizing deep learning (DL) methodologies and explainable AI (XAI) techniques. Contributions include exploring CNN-based segmentation frameworks like SDFNet and U-Net, multi-scale feature fusion, and transfer learning to enhance diagnostic accuracy. data augmentation and adversarial training are employed to address dataset bias, while Grad-CAM and CAManim provide interpretability, fostering clinician trust. Results demonstrate significant improvements in diagnostic performance, with sensitivity and specificity exceeding 95% and Dice coefficients surpassing 95% on diverse clinical datasets. The findings highlight the transformative potential of DL to automate kidney abnormality detection, reducing dependency on operator expertise while improving accuracy and efficiency. Challenges such as dataset scarcity and computational complexity persist, but future research on multimodal imaging, CNN optimization, and emerging technologies like quantum machine learning presents promising opportunities. This study serves as a guide for advancing diagnostic tools, paving the way for next-generation solutions in clinical applications.

关键词： Deep learning Training Image segmentation Ultrasonic imaging Accuracy Explainable AI Transfer learning Imaging Kidney Tumors

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Approximating Positive Homogeneous Functions with Scale Invariant Neural Networks

arXiv

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arXiv 2023年

作者： Bamberger, Stefan Heckel, Reinhard Krahmer, Felix Department of Mathematics Technical University of Munich Holsten Systems GmbH Germany Department of Computer Engineering Technical University of Munich Munich Center for Machine Learning Germany Department of Mathematics Munich Data Science Institute Technical University of Munich Munich Center for Machine Learning Germany

We investigate to what extent it is possible to solve linear inverse problems with ReLu networks. Due to the scaling invariance arising from the linearity, an optimal reconstruction function f for such a problem is positive homogeneous, i.e., satisfies f(λx) = λf(x) for all non-negative λ. In a ReLu network, this condition translates to considering networks without bias terms. We first consider recovery of sparse vectors from few linear measurements. We prove that ReLu- networks with only one hidden layer cannot even recover 1-sparse vectors, not even approximately, and regardless of the width of the network. However, with two hidden layers, approximate recovery with arbitrary precision and arbitrary sparsity level s is possible in a stable way. We then extend our results to a wider class of recovery problems including low-rank matrix recovery and phase retrieval. Furthermore, we also consider the approximation of general positive homogeneous functions with neural networks. Extending previous work, we establish new results explaining under which conditions such functions can be approximated with neural networks. Our results also shed some light on the seeming contradiction between previous works showing that neural networks for inverse problems typically have very large Lipschitz constants, but still perform very well also for adversarial noise. Namely, the error bounds in our expressivity results include a combination of a small constant term and a term that is linear in the noise level, indicating that robustness issues may occur only for very small noise *** Codes 41A30, 68T07 Copyright © 2023, The Authors. All rights reserved.

关键词： Recovery

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Estimating fish weight growth in aquaponic farming through machine learning techniques

Estimating fish weight growth in aquaponic farming through m...

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Intelligent Technologies (CONIT), International Conference on

作者： Purushottam Kumar Pranav Tiwari U Srinivasulu Reddy CoE in Artificial Intelligence Machine Learning & Data Analytics Lab National Institute of Technology Trichy India Computer Science and Engineering Indian Institute of Information Technology Tiruchirappalli Trichy India Department of Computer Applications Machine Learning & Data Analytics Lab National Institute of Technology Trichy India

Due to the ever-growing population, rapid urbanization, unusual environmental change, and dwindling water supply, the food production from conventional farming techniques won’t be able to keep up with increasing food demand for long, leading to severe food scarcity. Aquaponics is emerging as a viable alternative to food cultivation techniques but lacks efficiency, hindering its adoption on a large-scale production basis. The adoption of Artificial Intelligence (AI) techniques in smart aquaponic farming can further contribute to increasing the yield in aquaponics. In the study, both linear and non-linear regression algorithms such as linear regression, lasso regression, ridge regression, KNN regression, SVR, and decision tree regression were trained on aquaponic fish pond dataset, evaluated on the basis of multiple metrics like MAE, MSE, RMSE, and RMSLE, and a competitive analysis was done to determine the best algorithm for predicting the harvest weight of the fish in aquaponic ponds. The results showed that KNN regression and decision tree regression algorithms have lowest error and highest accuracy of 99.15% and 99.1% respectively, for predicting the harvest weight of the fish. The results obtained could be scaled up to commercial levels and used in regulating nutrient concentration and aquatic environmental conditions

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Optical Character Recognition (OCR) in Handwritten Characters Using Convolutional Neural Networks to Assist in Exam Reader System 2

Optical Character Recognition (OCR) in Handwritten Character...

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2nd International Conference on Advancement in Computation and Computer Technologies, InCACCT 2024

作者： Lekshmy, P.L. Velmurugan, S. Kumari, Indra Kayalvili, S. Teja Sree, B. Karthik Kumar, P. LBS Institute of Technology for Women Department of Computer Science and Engineering Kerala India T.J.S. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Department of Machine Learning Data Research Applied AI Daejeon Korea Republic of Department of Artificial Intelligence Tamilnadu Erode India S.R.K.R. Engineering College Department of Information Technology Andhra Pradesh Chinaamiram Bhimavaram India Coimbatore India

ISBN: (纸本)9798350371314

This work aimed to develop a character recognition method to facilitate the correction of answer cards in the Multiprova software through the development of a response card analysis flow that would culminate in the recognition of letters written by students and automatic correction of the tests. To isolate and identify the answers written on the answer cards, image segmentation techniques were used based on fixed marks printed on the cards. To recognize the letters and numbers written on the cards, trained three convolutional neural networks (for digits, letters and true or false). The results achieved (98.84% accuracy for digit CNN, 98.38% accuracy for letter CNN and 99.89% accuracy for true or false CNN) point out a great average success rate. © 2024 IEEE.

关键词： Convolutional neural networks

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