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International Conference on Computing and Networking Technology (ICCNT)

作者： G. Rajeswarappa R. Vignesh Ashis Kumar Ratha S. Devi R.A.M. Jainaf Nachiya M. Selvakumar Department of CSE G Pulla Reddy Engineering College Kurnool Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Karunya Nagar Coimbatore Department of Computing and Security School of Computer Science and Engineering IILM University Greater Noida Uttar Pradesh Department of MCA Nandha Engineering College Erode Tamilnadu Crescent School of Pharmacy B.S. Abdur Rahman Crescent Institute of Science and Technology Vandalur Chennai Department of Information Technology Bannari Amman Institute of Technology

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Diabetes is a metabolic disease that is common across the world and has a high yearly risk of complications that can be life-threatening. These problems have the potential to kill several important organs if left unchecked. Preventing severe consequences can be achieved by guaranteeing prompt treatment, contingent upon early identification. Refining datasets for efficient data mining operations necessitates data pretreatment, which frequently entails eliminating extra functions that may jeopardize predicted precision. In statistical machine learning, feature selection is a well-established topic that uses methods like K-means clustering and the greedy stepwise search algorithm. Even with limited capabilities, empirical testing of the SVM classifier on the Pima Indian Diabetes (PID) dataset repeatedly shows that it is more accurate than other approaches. The PID dataset is a popular benchmark for evaluating data mining techniques, demonstrating the usefulness of this dataset for research and development projects.

关键词： Support vector machines machine learning algorithms Refining Clustering algorithms Prediction algorithms Feature extraction Diabetes Classification algorithms Data mining Research and development

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NATURALPROOFS: Mathematical Theorem Proving in Natural Language 35

NATURALPROOFS: Mathematical Theorem Proving in Natural Langu...

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35th Conference on Neural Information Processing Systems - Track on Datasets and Benchmarks, NeurIPS Datasets and Benchmarks 2021

作者： Welleck, Sean Liu, Jiacheng Le Bras, Ronan Hajishirzi, Hannaneh Choi, Yejin Cho, Kyunghyun Paul G. Allen School of Computer Science & Engineering University of Washington United States Allen Institute for Artificial Intelligence United States New York University United States CIFAR Fellow in Learning in Machines & Brains Canada

Understanding and creating mathematics using natural mathematical language – the mixture of symbolic and natural language used by humans – is a challenging and important problem for driving progress in machine learning. As a step in this direction, we develop NATURALPROOFS, a multi-domain corpus of mathematical statements and their proofs, written in natural mathematical language. NATURALPROOFS unifies broad coverage, deep coverage, and low-resource mathematical sources, allowing for evaluating both in-distribution and zero-shot generalization. Using NATURALPROOFS, we benchmark strong neural methods on mathematical reference retrieval and generation tasks which test a system’s ability to determine key results that appear in a proof. Large-scale sequence models show promise compared to classical information retrieval methods, yet their performance and out-of-domain generalization leave substantial room for improvement. NATURALPROOFS opens many avenues for research on challenging mathematical tasks. © 2021 Neural information processing systems foundation. All rights reserved.

关键词： Zero-shot learning

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Development of a Predictive Model for Kidney Stone Formation Using Deep learning Techniques

Development of a Predictive Model for Kidney Stone Formation...

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artificial intelligence and Emerging Technology (Global AI Summit), International Conference on

作者： Faz Mohammad Gajendra Sharma Kritika Khandelwal Abdul Arif M. Sree Vani Cheruku Sathyanarayana Department of Artificial intelligence and Machine learning G. L. Bajaj Institute of Technology and Management Greater Noida India School of Computer Science Engineering & Technology Bennett University Greater Noida India Department of Business Administration Manipal University Jaipur India Mechanical Department Aditya College of Engineering and Technology Surampalem India Department of CSE BVRIT Hyderabad College Of Engineering For Women Hyderabad India Vignana Bharathi Institute of Technology JNTUH Univeristy Hyderabad India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

This research investigates the development of a predictive model for kidney stone formation using deep learning techniques, specifically Convolutional Neural Networks (CNN), VGG 19, and Deep Belief Networks. Utilizing a dataset of 920 CT scans of images with varying sizes and locations, the model is used to predict the presence and location of kidney stones within the scan and the size of the kidney stones when present. The images undergo preprocessing, which includes standardizing the resolution, reducing noise, increasing contrast, and ensuring the spatial registry of all the images. Through this preprocessing, we are able to extract features from the images which, when fed through the models, can detect the subtle patterns that indicate the presence of the kidney stones. The dataset is split into two portions: one which the model is trained upon which contained 70% of the dataset and the other for validation, which encompassed 80% of the dataset. Various performance metrics were used to evaluate the performance of the models including accuracy, precision, recall, F1 score, and Area Under the Receiver Operating Characteristic (ROC) Curve AUC-ROC. The results have shown that the CNN model consistently led to better results than either the VGG 19 or DBN model across either the testing or validation data partitions; it yielded the highest levels in accuracy, precision, recall, and F1 score. The model which trained on 80% of the dataset performance yielded superior results compared to the model which trained on the 70% dataset, which further demonstrates the importance of size in training. The technique used in this research has the potential to revolutionize kidney stone detection and diagnoses, ensuring better planning for clinical decisions in patients with kidney stones.

关键词： Deep learning Training Accuracy Computed tomography Predictive models Feature extraction Data models Convolutional neural networks artificial intelligence Testing

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Big Data Analysis on Intelligent Traffic Surveillance by Incorporating Sensors and IoT Devices on Roads

Big Data Analysis on Intelligent Traffic Surveillance by Inc...

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International Conference on Inventive Computation Technologies (ICICT)

作者： A Devi Akanksha Kapruwan Manish Shrimali Vengalapudi Appalakonda Ramya Maranan Amit Barve Department of Computer Applications Dr. SNS Rajalakshmi College of Arts and Science (Autonomous) Coimbatore Tamil Nadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and IT Janardan Rai Nagar Rajasthan Vidyapeeth (Deemed to be University) Udaipur Rajasthan India Department of Artificial Intelligence and Machine Learning Aditya Engineering College (A) Kakinada Andhra Pradesh India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Department of Computer Science and Engineering Parul Institute of Technology Faculty of Engineering and Technology Parul University Gujarat India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

Rather than relying on public transportation, people are increasingly comfortable driving their automobiles to satisfy their mobility needs. Reasons for this include the automobiles' accessibility and the fact that they can be used whenever needed. Because of this, many cities around the world experience terrible traffic congestion and long wait periods at traffic signals. Smarter and more effective traffic management is possible through the integration of big data, the Internet of Things (IoT), and a cloud platform with a network of linked vehicles and sensors. The objective of this study is to estimate traffic conditions along a route and provide updates to the user. Initially, Deep learning (DL) models such as YOLO (You Only Look Once) and SSD (Single-Shot Object Detection) are developed and tested to detect vehicles. YOLO produces outstanding performance in vehicle detection, with an 89.5% mean Average Precision (mAP) and an 83.98% Intersection over Union (IoU). This model is then pushed to the cloud. A Global Positioning System (GPS) module is used to determine the user's current location. Based on the user's location, the nearest camera is enabled, and the recorded video is sent to the cloud. The cloud-deployed YOLO model is used to detect and count vehicles. To improve the user experience, this study has created a mobile application. The software contains a navigation map function that allows users to specify the maximum number of vehicles expected along their route. The created system counts the number of vehicles on their path, and if the count exceeds the specified threshold, the mobile app notifies the user about traffic conditions.

关键词： YOLO Cloud computing Clouds Roads Urban areas Big Data Mobile applications

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Decoupled Doubly Contrastive learning for Cross Domain Facial Action Unit Detection

arXiv

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arXiv 2025年

作者： Li, Yong Liu, Menglin Cui, Zhen Ding, Yi Zong, Yuan Zheng, Wenming Shan, Shiguang Guan, Cuntai School of Computer Science and Engineering The Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Nanjing210096 China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China School of Artificial Intelligence Beijing Normal University Beijing100875 China Key Laboratory of AI Safety Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China School of Computer Science and Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore Key Laboratory of Child Development and Learning Science of Ministry of Education School of Biological Science and Medical Engineering Nanjing210096 China

Despite the impressive performance of current vision-based facial action unit (AU) detection approaches, they are heavily susceptible to the variations across different domains and the cross-domain AU detection methods are under-explored. In response to this challenge, we propose a decoupled doubly contrastive adaptation (D2CA) approach to learn a purified AU representation that is semantically aligned for the source and target domains. Specifically, we decompose latent representations into AU-relevant and AU-irrelevant components, with the objective of exclusively facilitating adaptation within the AU-relevant subspace. To achieve the feature decoupling, D2CA is trained to disentangle AU and domain factors by assessing the quality of synthesized faces in cross-domain scenarios when either AU or domain attributes are modified. To further strengthen feature decoupling, particularly in scenarios with limited AU data diversity, D2CA employs a doubly contrastive learning mechanism comprising image and feature-level contrastive learning to ensure the quality of synthesized faces and mitigate feature ambiguities. This new framework leads to an automatically learned, dedicated separation of AU-relevant and domain-relevant factors, and it enables intuitive, scale-specific control of the cross-domain facial image synthesis. Extensive experiments demonstrate the efficacy of D2CA in successfully decoupling AU and domain factors, yielding visually pleasing cross-domain synthesized facial images. Meanwhile, D2CA consistently outperforms state-of-the-art cross-domain AU detection approaches, achieving an average F1 score improvement of 6%-14% across various cross-domain scenarios. © 2025, CC BY-NC-SA.

关键词： Contrastive learning

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Innovative Advances: Crafting an Intelligent Controller for Home Energy Management Systems with Seamless Integration of Renewable Sources

Innovative Advances: Crafting an Intelligent Controller for ...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： B Varasree Nv Uma Reddy Suman Avdhesh Yadav Ginni Nijhawan Monica Garg N Sirisha Department of Information Technology Institute of Aeronautical Engineering Hyderabad Telangana India Department of Artificial Intelligence & Machine Learning New Horizon College of Engineering Bangalore India Department of CSE IILM University Greater Noida Uttar Pradesh India Lovely Professional University Phagwara Lloyd Law College Greater Noida Uttar Pradesh India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

This research proposes an improved energy administration and optimization technique that utilizes fuzzy logic algorithms and incorporates many inputs and outputs (I/O) along with a cost-effective method. This technology is used in the administration of hybrid electric power sources, which include PV cell, turbines (wind), and grid-supportive devices. The novelty of this research lies in the precedence of solar PV, wind turbine, and storage system energy sources above the grid network. The grid network is only used in case of bad weather. This system can supply an average household with up to $\mathbf{4, 0 0 0}$ Wh of energy per day. In addition, by using an electrolyzing system, surplus unconventional energy produced during good weather conditions is used to produce hydrogen, which is well-suited for household cooking and heating purposes. Developing an intelligent economic strategy by using fuzzy technology to bolster improved energy management and optimization methods. The fuzzy logic software at the heart of this system has been written on the Arduino 2560 mega microcontroller. It is responsible for allocating events and considering all possible circumstances. This system has been developed using a flowchart that facilitates hybrid system management. In order to maintain uninterrupted housing, it is advisable to use strategies such as methodologies, a parametric search, and simulations to precisely characterize the system components that provide assistance to the power grid. The effectiveness of the recommended method is confirmed by the display of output directives generated by the electronic switches.

关键词： Fuzzy logic Wind Renewable energy sources Microcontrollers Optimization methods Software Power grids Wind turbines MATLAB Informatics

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Game Theory Based Spectrum Sharing Techniques for Cognitive Radio Networks with Multiple Primary Users

Game Theory Based Spectrum Sharing Techniques for Cognitive ...

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Automation and Computation (AUTOCOM), International Conference on

作者： Rangaballav Pradhan Deepak Minhas V Nirmalrani Gadug Sudhamsu Savita Sourav Rampal Centre for Artificial Intelligence and Machine Learning Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India Department of Information Technology Sathyabama Institute of Science and Technology Chennai Tamil Nadu India School of Engineering and Technology Computer Science and Engineering JAIN (Deemed to be University) Bangalore Karnataka Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

The recreation concept has been delivered in cognitive radio networks (CRNs) as an effective approach for spectrum sharing amongst more than one number one user (PUs). This method permits secondary customers (SUs) to utilize the unused spectrum sources of the PUs while minimizing interference to their primary transmissions. This is especially important in CRNs, where the constrained availability of spectrum assets is a first-rate assignment. In this paper, we advocate sport idea-primarily based spectrum sharing techniques for CRNs with multiple PUs. We formulate the problem as a non-cooperative game wherein SUs choose their transmission strategies to maximize their utilities while thinking about the restrictions imposed by the PUs. The proposed techniques consist of coalitional sport-based total power control, software-primarily based negotiation, and cooperative spectrum sharing through aspect charge. Simulation outcomes display that these strategies can achieve better spectrum utilization and equity among SUs compared to standard methods while also ensuring minimum interference to the PUs.

关键词： Video games Analytical models Automation Power control Interference Games Cognitive radio Game theory Standards Sports

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Real-time Human Action Recognition using Deep Convolutional Neural Networks

Real-time Human Action Recognition using Deep Convolutional ...

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International Conference on Computing and Networking Technology (ICCNT)

作者： R. Vignesh J. Balaraju P. Poongothai Vaishali Rajput N. Rajkumar V. G. Pratheep Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Karunya Nagar Coimbatore Department of CSE Anuraag University Hyderabad Department of Computer Science KristuJayanti College(Autonomous) Bangalore Computer engineering Vishwakarma Institute o Technology Pune CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Thiruvalluvar Tamil Nadu Department of Mechatronics Engineering Kongu Engineering College Perundurai Tamil Nadu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

current advances in deep gaining knowledge of have enabled automated systems to understand human moves from movies in actual-time. Deep convolutional neural networks (DCNNs) had been used to allow accurate and efficient real-time movement reputation. with the aid of leveraging the statistics-driven illustration mastering capability of DCNNs, movement popularity has become a powerful device for video analysis applications including robotics, surveillance, entertainment, augmented truth, and healthcare. DCNNs use an aggregate of densely connected convolutional layers and in addition completely linked layers for function extraction from films. The proposed architectures are usually skilled using supervised learning. Convolutional layers are used to understand nearby abstractions in motion pictures, which in turn are used to research worldwide features. diverse techniques together with temporal segmentation, movement-based characteristic extraction, and feature switch gaining knowledge of are also hired by means of DCNNs for achieving higher performance. recent works have investigated exceptional techniques to enhance the accuracy and speed of action reputation the use of DCNNs. This consists of techniques which includes model compression, decoupled inference networks, and efficient convolutional networks. By using combining those strategies, it’s far viable to acquire higher popularity accuracy at the same time as at the equal time preserving an excessive computational speed.

关键词： Accuracy Surveillance Supervised learning Switches Streaming media Feature extraction Motion pictures Real-time systems Convolutional neural networks Human activity recognition

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machine learning on Retail Stores for Customer Churn

Machine Learning on Retail Stores for Customer Churn

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Advanced Computing Technologies (ICoACT), International Conference on

作者： P. Brito Clinton P. Sathiya Priya S. Jagadeesan S. Nagarajan K. Mohanapriya S. Jeya Lakshmi Master of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Computer Science and Engineering Nandha Engineering College Erode Tamil Nadu India Department of Information Technology R.M.K. Engineering College Chennai Tamil Nadu India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

The pace at which customers discontinue engaging in commercial activity with a corporation is known as customer churn, and it frequently indicates discontent or competition. machine learning analyzes customer data to identify patterns, predict churn, and personalize retention strategies. It enables businesses to target at-risk customers and enhance engagement through tailored interventions and offers. A public dataset gathered from Kaggle is used in this research work. Exploratory data analysis is carried over to understand the patterns and trends in the churn rate. The bivariate analysis is held to find the correlation of each feature against the churn rate. With these observations, the importance of marketing strategies is exhibited. Additionally, four distinct models—Logistic Regression, Random Forest, Decision Tree, and the XGB Classifier-are used to train and assess the dataset. From the evaluation of all four models with the help of the ROC score, the XGB is found to outperform and provide better results obtaining 98% accuracy and a 1.00 ROC score.

关键词： computers Data analysis Correlation Computational modeling Market research Classification algorithms Churn Regression tree analysis Random forests Optimization

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Improving AlphaFLOW for Efficient Protein Ensembles Generation

arXiv

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arXiv 2024年

作者： Li, Shaoning Li, Mingyu Wang, Yusong He, Xinheng Zheng, Nanning Zhang, Jian Heng, Pheng Ann Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Medicinal Chemistry and Bioinformatics Center Shanghai Jiao Tong University School of Medicine China National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University China Shanghai Institute of Materia Medica Chinese Academy of Sciences China

Investigating conformational landscapes of proteins is a crucial way to understand their biological functions and properties. AlphaFLOW stands out as a sequence-conditioned generative model that introduces flexibility into structure prediction models by fine-tuning AlphaFold under the flow-matching framework. Despite the advantages of efficient sampling afforded by flow-matching, AlphaFLOW still requires multiple runs of AlphaFold to finally generate one single conformation. Due to the heavy consumption of AlphaFold, its applicability is limited in sampling larger set of protein ensembles or the longer chains within a constrained timeframe. In this work, we propose a feature-conditioned generative model called AlphaFLOW-Lit to realize efficient protein ensembles generation. In contrast to the full fine-tuning on the entire structure, we focus solely on the light-weight structure module to reconstruct the conformation. AlphaFLOW-Lit performs on-par with AlphaFLOW and surpasses its distilled version without pretraining, all while achieving a significant sampling acceleration of around 47 times. The advancement in efficiency showcases the potential of AlphaFLOW-Lit in enabling faster and more scalable generation of protein ensembles. © 2024, CC BY.

关键词： Proteins

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