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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Chandrashekhar, B.N. Mohan, M. Geetha, V. BMS Institute of Technology & Management Artificial Intelligence and Machine Learning Benguluru India Nitte Meenakshi Institute of Technology Information Science & Engineering Benguluru India Reva University School of Management Bengaluru India

ISBN: (纸本)9798350338607

Process scheduling is an essential part of multiprogramming operating systems. Scheduling is a process that allows one process to use the processing unit while the execution of another process is on hold (in a waiting state) due to the unavailability of any resource like I/O, thereby making full use of CPU or GPU. The major issue of scheduling is to make the system efficient, fast, and fair. This work focuses on developing a Forecast model and constructing scheduling strategies to schedule parallel applications on CPU and GPU. During the design of the Forecast model, we will consider the history of the actual execution time set of processes, then we compute the average time of individual sets of processes by considering parameters such as complete execution time, the sum of processes, and the number of threads assigned to individual processes. Then we will evaluate the Prediction time of CPU and GPU for individual sets of processes. By considering parameters such as the average time of the previous set of processes, the weight of processes, and the number of processes. Then based on the prediction time we will develop a scheduling strategy. As the minimum prediction time required set of process resources is assigned to the CPU and the GPU is assigned by the maximum predicted timed resource of the process. In this work we utilized the CPU and GPU resources effectively for stream benchmark application, our experiment shows that less than 20% average percentage prediction error in all cases. © 2023 IEEE.

关键词： Forecasting

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Liver Disorder Diagnosis Using machine learning Technique

Liver Disorder Diagnosis Using Machine Learning Technique

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science, Technology, Engineering and Management (ICSTEM), International Conference on

作者： Aneesh Nemalikanti Ebenezer Jangam Computer Science and Engineering Karunya Institute Of Technology and Sciences Coimbatore India Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350376913

ISBN: (纸本)9798350388473

The utilization of machine learning techniques in diagnosing liver disorders has garnered considerable attention for its potential to enhance accuracy and efficiency. However, in this study, we pivot our focus towards developing a predictive model for detecting liver tumors, leveraging patient information encompassing demographic details, medical history, and laboratory test results. By employing advanced algorithms such as Efficient-Net and LeNet, we aim to classify whether the input data indicates the presence of a liver tumor. This novel approach has demonstrated improved accuracy compared to traditional methods, marking a significant advancement in liver tumor diagnosis. The outcomes demonstrate how well our suggested methodology works to reliably identify liver tumors. thereby potentially aiding healthcare professionals in making timely and precise decisions, ultimately leading to enhanced patient care.

关键词： Accuracy Liver Medical services machine learning Predictive models Prediction algorithms User experience

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Incremental learning Framework for Mining Big Data Stream

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computers, Materials & Continua 2022年第5期71卷 2901-2921页

作者： Alaa Eisa Nora E.L-Rashidy Mohammad Dahman Alshehri Hazem M.El-bakry Samir Abdelrazek Information Systems Department Faculty of Computers and InformationMansoura UniversityMansoura35516Egypt Machine Learning and Information Retrieval Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr El-SheikhEgypt Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

At this current time,data stream classification plays a key role in big data analytics due to its enormous *** of the existing classification methods used ensemble learning,which is trustworthy but these methods are not effective to face the issues of learning from imbalanced big data,it also supposes that all data are *** weakness of current methods is that it takes a long evaluation time when the target data stream contains a high number of *** main objective of this research is to develop a new method for incremental learning based on the proposed ant lion fuzzy-generative adversarial network *** proposed model is implemented in spark *** each data stream,the class output is computed at slave nodes by training a generative adversarial network with the back propagation error based on fuzzy bound *** method overcomes the limitations of existing methods as it can classify data streams that are slightly or completely unlabeled data and providing high scalability and *** results show that the proposed model outperforms stateof-the-art performance in terms of accuracy(0.861)precision(0.9328)and minimal MSE(0.0416).

关键词： Ant lion optimization(ALO) big data stream generative adversarial network(GAN) incremental learning renyi entropy

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Enhancing Career Development: An AI-Driven Mentorship Platform for Student-Mentor Connections

Enhancing Career Development: An AI-Driven Mentorship Platfo...

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Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： Charanya P Nidhish M Prajesh U R Saktheeswar K Sanjana S Pillai Department of Artificial Intelligence and Machine Learning Sri Eshwar College of Engineering Coimbatore India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

Thunai is an AI-powered mentorship platform that connects users with personalized mentors for career guidance. Key features include automated calendar booking, video communication with privacy-enhancing dynamic emojis, and AI-driven mentor matching aligned with mentee goals. The AI assistant, GURU, supports task management, resource recommendations, and LinkedIn-based evaluations to foster growth. Thunai offers subscription plans and partnerships with educational and mental health organizations, enhancing engagement, leadership development, and networking. Leveraging AI technologies like sentiment analysis and feedback, Thunai improves mentor matching, user satisfaction, and mentee engagement for career and emotional well-being.

关键词： Sentiment analysis Technological innovation Engineering profession Organizations Mental health Solids Real-time systems Mentoring artificial intelligence Monitoring

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Abnormal Load Patterns Detection in Smart Grids Using Temporal Convolutional Neural Network Based Gated Recurrent Units Networks with Multi-Head Temporal Attention 4

Abnormal Load Patterns Detection in Smart Grids Using Tempor...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Balassem, Zayd Srikanteswara, Ramya Esakki Madura, E. Anjaneyulu, Madugula Devi, M. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science India Gokaraju Rangaraju Institute of Engineering and Technology Department of Artificial Intelligence and Machine Learning Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350352931

In recent era, the advancement in smart grid technologies and the integration of renewable energy sources have modernized the power distribution landscape. The efficiency and stability of overall system is ensured by the controlling of power converters. In existing, Support Vector machine (SVM) is utilized but it assumes the normal data follows Gaussian distribution which is not be the same for smart grid data. In this research, Temporal Convolutional Neural Network based Gated Recurrent Units Networks with Multi-Head Temporal Attention (TCN-GRU-MTA) is proposed. Initially, the input signals are considered from Smart Grid Stability (SGS) dataset. Then, the signals are further preprocessed with autoencoders to reduce noise and to improve quality of signals. The Siamese Neural Networks (SNN) feature extractor efficiently learns relevant features from smart grid data which are indicative of abnormal behavior. After that, the SNN is introduced for extracting features from preprocessed data. Next, the proposed TCN-GRU-MTA is utilized to detect the abnormal load patterns in smart grid networks. From the results, the proposed-established better results including accuracy (99.10%), precision (89.98%), recall (99.05%) and F1-score (95.20%) when compared to existing Quadratic SVM. © 2024 IEEE.

关键词： Smart power grids

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Discriminative Score Suppression for Weakly Supervised Video Anomaly Detection

Discriminative Score Suppression for Weakly Supervised Video...

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2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Xu, Chen Li, Chunguo Xing, Hongjie College of Mathematics and Information Science Hebei University Hebei Key Laboratory of Machine Learning and Computational Intelligence Baoding071002 China School of Cyber Security and Computer Hebei University Baoding071000 China

ISBN: (纸本)9798331510831

Weakly supervised video anomaly detection (WSVAD) often relies on Multiple Instance learning (MIL). However, selecting only the most discriminative segments for training limits the model's ability to comprehensively detect anomalous events, particularly hard anomalies. To overcome this limitation, we propose the Discriminative Score Suppression (DSS) module. This module suppresses the discriminative scores of the most prominent anomalies, shifting the model's attention to less obvious but important hard anomalies. This approach guides the model to learn the critical features of hard anomalies, enabling a more comprehensive detection of anomalous events. Additionally, the Anomaly Score Refinement (ASR) module constructs a dissimilarity-based classifier by storing normal patterns as prototypes, and integrates this with a neural network classifier. By combining the anomaly scores from both classifiers, more accurate detection of true hard anomalies is achieved. A score-sensitive inner-bag loss function not only adjusts penalties based on anomaly scores but also ensures that the model avoids erroneous selections. Our method accurately detects various anomalies, including challenging and multi-segment anomalies, while minimizing false positives for normal events. Extensive experiments show that the proposed framework outperforms state-of-the-art methods on the UCF-Crime and XD-Violence datasets. © 2025 IEEE.

关键词： Supervised learning

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Multi-Keywords Based Fully Homomorphic Encryption and Data Classification for Cloud Security and Privacy

Multi-Keywords Based Fully Homomorphic Encryption and Data C...

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Emerging Trends in Engineering and Medical sciences (ICETEMS), International Conference on

作者： Kooragayala Sukeerthi R. Kesavan S.A. Kalaiselvan Department of Computer Science & Engineering Saveetha School of Engineering SIMATS Chennai India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai

ISBN: (数字)9798331529352

ISBN: (纸本)9798331529390

When it comes to processing and storing massive amounts of data efficiently, cloud computing has become the dominant paradigm. Both consumers and suppliers of services in cloud settings are understandably worried about the safety of critical data. When it comes to protecting sensitive information during transmission in the cloud, homomorphic encryption has shown to be an effective method. This study presents a new homomorphic encryption method for efficient and safe computations on the cloud, determined by multistage partial homomorphic encryption. The learning using errors (LWE) issue, primarily an altered form of the BVG method, is the basis of the proposed completely homomorphic encryption technique. It is believed to be difficult to resolve the learning with mistakes issue, which is a challenge in machine learning. We were able to implement the technique by making use of MFHE, an encryption method that can process inputs encrypted under different keys. Our expansion of the technique to search for several keywords was based on this. To make data processing in the cloud safe and privacy protecting, the suggested system combines the strengths of multistage encryption with partial homomorphic encryption.

关键词： Cloud computing Data privacy Privacy machine learning Data processing Market research Safety Servers Homomorphic encryption Secure storage

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Stable Heterogeneous Treatment Effect Estimation across Out-of-Distribution Populations 40

Stable Heterogeneous Treatment Effect Estimation across Out-...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Zhang, Yuling Wu, Anpeng Kuang, Kun Du, Liang Sun, Zixun Wang, Zhi Tsinghua University Tsinghua Shenzhen International Graduate School China Zhejiang University College of Computer Science and Technology China Mohamed Bin Zayed University of Artificial Intelligence Machine Learning Department United Arab Emirates Interactive Entertainment Group Tencent Tsinghua University Tsinghua-Berkeley Shenzhen Institute China

ISBN: (纸本)9798350317152

Heterogeneous treatment effect (HTE) estimation is vital for understanding the change of treatment effect across individuals or subgroups. Most existing HTE estimation methods focus on addressing selection bias induced by imbalanced distributions of confounders between treated and control units, but ignore distribution shifts across populations. Thereby, their applicability has been limited to the in-distribution (ID) population, which shares a similar distribution with the training dataset. In real-world applications, where population distributions are subject to continuous changes, there is an urgent need for stable HTE estimation across out-of-distribution (OOD) populations, which, however, remains an open problem. As pioneers in resolving this problem, we propose a novel Stable Balanced Representation learning with Hierarchical-Attention Paradigm (SBRL-HAP) framework, which consists of 1) Balancing Regularizer for eliminating selection bias, 2) Independence Regularizer for addressing the distribution shift issue, 3) Hierarchical-Attention Paradigm for coordination between balance and independence. In this way, SBRL-HAP regresses counterfactual outcomes using ID data, while ensuring the resulting HTE estimation can be successfully generalized to out-of-distribution scenarios, thereby enhancing the model's applicability in real-world settings. Extensive experiments conducted on synthetic and real-world datasets demonstrate the effectiveness of our SBRL-HAP in achieving stable HTE estimation across OOD populations, with an average 10% reduction in the error metric PEHE and 11% decrease in the ATE bias, compared to the SOTA methods. © 2024 IEEE.

关键词： Population distribution

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A Review on Web Data Mining to Detect Online Spread of Terrorism Activity

A Review on Web Data Mining to Detect Online Spread of Terro...

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machine learning and Autonomous Systems (ICMLAS), International Conference on

作者： Saurav Kumar Jha Utkarsha Pacharaney Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Wardha Maharashtra India Department of Artificial Intelligence and Machine learning Faculty of Engineering and Technology Wardha Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

This research study outlines the significant contribution that web data mining can make in discovering and combating the dissemination of online terrorism. With terrorist groups using the internet more and more to deliver propaganda, recruit, and organize, there is a pressing need for efficient monitoring and analysis of web content. This article discusses different data mining methods that can be used to identify and study patterns suggestive of terrorist activity throughout networked digital environments. Specifically, the embedding of machine learning algorithms, i.e., Support Vector machines (SVM), Random Forest and Gradient Boosting, is investigated with regard to its suitability to differentiate between benign and harmful online content. These algorithms can process large volumes of data, where features are extracted, including URL structures, domain ages, and keyword frequencies, to detect anticipate threats. According to the article, feature extraction plays a crucial role in improving detection system accuracy, enabling real-time surveillance of anomalous online activities. In addition, the review discusses the problems that researchers and law enforcement agencies have been encountering in using web data mining for counter-terrorism purposes. Ethical concerns around privacy and civil liberties are addressed and technical challenges, including the quality of data and the dynamic character of online content, are elaborated. Scenarios in which false positives arising in detection systems may occur are also considered, with emphasis on the need for ongoing algorithm and technique refinement.

关键词： Support vector machines Uniform resource locators machine learning algorithms Reviews Law enforcement Terrorism Feature extraction Real-time systems Data mining Internet of Things

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On generalisability of segment anything model for nuclear instance segmentation in histology images

arXiv

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arXiv 2024年

作者： Xu, Kesi Goetz, Lea Rajpoot, Nasir Tissue Image Analytics Centre Department of Computer Science University of Warwick United Kingdom Artificial Intelligence and Machine Learning GSK London United Kingdom

Pre-trained on a large and diverse dataset, the segment anything model (SAM) is the first promptable foundation model in computer vision aiming at object segmentation tasks. In this work, we evaluate SAM for the task of nuclear instance segmentation performance with zero-shot learning and finetuning. We compare SAM with other representative methods in nuclear instance segmentation, especially in the context of model generalisability. To achieve automatic nuclear instance segmentation, we propose using a nuclei detection model to provide bounding boxes or central points of nuclei as visual prompts for SAM in generating nuclear instance masks from histology images. © 2024, CC BY.

关键词： Image segmentation

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