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A Novel Salary Prediction System Using Machine Learning Techniques 9

A Novel Salary Prediction System Using Machine Learning Tech...

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Joint 9th International Conference on Digital Arts, Media and Technology with 7th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications engineering, ECTI DAMT and NCON 2024

作者： Asaduzzaman, Abu Uddin, Md Raihan Woldeyes, Yoel Sibai, Fadi N. Wichita State University Dept of Electrical and Computer Engineering WichitaKS United States Wichita State University School of Computing WichitaKS United States Gulf University for Science and Technology Dept of Electrical and Computer Engineering Hawally Kuwait

ISBN: (纸本)9798350318241

The purpose of this work is to build a salary prediction system using machine learning techniques. The experiments are done using the data from 1994 census database which has 32,561 records of employee data. The techniques used in determining whether an employee salary is less than or greater than $50,000 are: logistic regression, decision tree, Naive Bayes classifier, K-nearest neighbor, and support vector machine. We implement these algorithms using original train data and oversampled train data. The results of these models are analyzed and compared with respect to accuracy. According to the experimental results, decision tree model outperforms the other models with original train data. © 2024 IEEE.

关键词： Decision trees

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Deep Learning and Quantum-Enabled Cloud Platform Approach on Optimized Crop Health Predictions 5th

Deep Learning and Quantum-Enabled Cloud Platform Approach on...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Singh, Jagendra Thakur, Hardeo Kumar Sharma, Neetu Alsahlanee, Abbas Thajeel Rhaif Sahunthala, S. Shaik, Nazeer Alliance School of Advanced Computing Alliance University Bangalore India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Science and Information Technology University of Salahaddin-Erbil Erbil Iraq Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Tamil Nadu Chembarambakkam India Department of Computer Science and Engineering Srinivasa Ramanujan Institute of Technology—Autonomous Anantapur India

ISBN: (纸本)9789819763177

This study is an innovative investigation into the field of plant disease detection and plant health prediction using state-of-the-art machine learning models and Quantum Support Vector Machines (QSVM) for feature extraction. A diverse dataset of 700 images, including a variety of plant species and disease states, served as the foundation for this study. The study evaluated the performance of four machine learning models: CNN (ConvNet), RNN (Recurrent), KNN (k-Nearest Neighbors), and SVM (Support Vector Machine) for accurately identifying and classifying plant diseases. Convolutional neural network (CNN) model unexpectedly prevailed, achieving a remarkable accuracy rate of 98.77%. Accuracy, recall, and F1 score of the model further demonstrated its propensity for disease diagnosis. QSVMs were used to extract intricate features from pictures of sick leaves in order to increase the models’ precision and prognostication ability. The findings have significant impact for agriculture. And the sophisticated toolkit they offer has provided an important step forward in proactive disease management for better crop health. Carrying out an early intervention for disease can result in higher crop output, lower agricultural losses, and improved global food security. The QSVM’s application of quantum computing principles also opens up fresh frontiers for creating machine learning applications in different industries. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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DivGCL: A Graph Contrastive Learning Model for Diverse Recommendation 39

DivGCL: A Graph Contrastive Learning Model for Diverse Recom...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Gong, Wenwen Geng, Yangliao Zhang, Dan Zhu, Yifan Xu, Xiaolong Xiang, Haolong Beheshti, Amin Zhang, Xuyun Qi, Lianyong School of Information and Electrical Engineering China Agricultural University China School of Computer Science & Technology Beijing Jiaotong University China Department of Computer Science and Technology Tsinghua University China School of Computer Science Beijing University of Posts and Telecommunications China School of Software Nanjing University of Information Science and Technology China School of Computing Macquarie University Australia College of Computer Science and Technology China University of Petroleum [East China China State Key Lab. for Novel Software Technology Nanjing University China

ISBN: (纸本)157735897X

Graph Contrastive Learning (GCL), as a primary paradigm of graph self-supervised learning, spurs a fruitful line of research in tackling the data sparsity issue by maximizing the consistency of user/item embeddings between different augmented views with random perturbations. However, diversity, as a crucial metric for recommendation performance and user satisfaction, has received rather little attention. In fact, there exists a challenging dilemma in balancing accuracy and diversity. To address these issues, we propose a new Graph Contrastive Learning model for Diversifying recommendations (DivGCL). Inspired by the excellence of the determinant point process (DPP), DivGCL adopts a DPP likelihood-based loss function to achieve an ideal trade-off between diversity and accuracy, optimizing it jointly with the advanced Gaussian noise-augmented GCL objective. Extensive experiments on four popular datasets demonstrate that DivGCL surpasses existing approaches in balancing accuracy and diversity, with an improvement of 23.47% at T@20 (abbreviation for trade-off metric) on ML-1M. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Self-supervised learning

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Explainable AI for Intrusion Prevention: A Review of Techniques and Applications 7th

Explainable AI for Intrusion Prevention: A Review of Techniq...

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7th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2023

作者： Chandre, Pankaj R. Vanarote, Viresh Patil, Rajkumar Mahalle, Parikshit N. Shinde, Gitanjali R. Nimbalkar, Madhukar Barot, Janki Department of Computer Science and Engineering MIT School of Computing MIT ADT University Loni Kalbhor India Department of Information Technology MIT School of Computing MIT ADT University Loni Kalbhor India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology Kondhawa India Department of Computer Science and Engineering Vishwakarma Institute of Information Technology Kondhawa India Silver Oak University Ahmedabad India

ISBN: (纸本)9789819937578

Intrusion prevention has become a critical concern in today's increasingly complex and interconnected digital systems. Traditional intrusion prevention methods often rely on rule-based approaches that can be inflexible and prone to false positives and false negatives. The emergence of explainable artificial intelligence (AI) techniques has the potential to provide more accurate and interpretable solutions for intrusion prevention. This review paper surveys the current state of the art in explainable AI techniques for intrusion prevention, including their strengths and limitations. We begin by providing an overview of intrusion prevention and the challenges faced by traditional methods. We then explore the different types of explainable AI techniques, such as Local Interpretable Model-Agnostic Explanations (LIME), Shapley Additive Explanations (SHAP), and Integrated Gradients, and their application to intrusion prevention. We also discuss the different types of datasets and evaluation metrics used in explainable AI-based intrusion prevention, including public datasets such as NSL-KDD and UNSW-NB15. Finally, we highlight the future research directions in explainable AI-based intrusion prevention, including the need for larger and more diverse datasets, better evaluation metrics, and more comprehensive explainable AI models. Overall, this review paper provides a comprehensive overview of the state of the art in explainable AI for intrusion prevention, and serves as a valuable resource for researchers and practitioners in the field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Additives

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BiLSTM model with Attention mechanism for multi-label news text classification 4

BiLSTM model with Attention mechanism for multi-label news t...

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4th International Conference on Neural Networks, Information and Communication engineering, NNICE 2024

作者： Duan, Aihua Raga, Rodolfo C. National University College of Computing and Information Technologies Manila0900 Philippines Anhui University of Finance and Economics School of Management Science and Engineering Bengbu233030 China José Rizal University Department of Computer Studies and Engineering Mandaluyong City Philippines

ISBN: (纸本)9798350394375

Multi-label text classification is very important in text research. A news will have multiple classification labels, and the number of labels is uncertain, so how to accurately learn labels from text is difficult. In this paper, GloVe is used for vectorization, and BiLSTM is used to learn the context, Use the Attention mechanism to focus on important words in the sentence, and finally focal loss function is used as the loss function. The experimental results show that the accuracy of this method reaches 84,43%% and AUR rate is 95.98%, which are higher than the evaluation scores of other five models. © 2024 IEEE.

关键词： Classification (of information)

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Identifiable EEG Embeddings by Contrastive Learning from Differential Entropy Features 19th

Identifiable EEG Embeddings by Contrastive Learning from D...

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19th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2023

作者： Zhang, Zhen Liang, Feng Mo, Jiawei Hu, Wenxin Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Shenzhen MSU-BIT University Shenzhen China School of Information Science and Engineering Lanzhou University Lanzhou China School of Computer Science and Engineering Central South University Changsha China

ISBN: (纸本)9783031651250

Encoding EEG data into low-dimension latent embeddings greatly facilitates data analysis and interpretation in neuroscience studies, clinical diagnosis, and human-computer interaction. But generating informative and identifiable latent embeddings that are representative of the origin EEG is not an easy mission. Contrastive learning has the potential to utilize large amounts of unlabelled EEG data and extract informative and identifiable latent embeddings for a wide range of downstream tasks. We explore the feasibility of applying the contrastive learning method to train the EEG latent encoder from the feature of differential entropy of short-time window frequency domain signals. The encoder minimizes the noise-contrastive estimation loss by comparing the embeddings with positive and negative embedding samples, where the distinction of samples is guided by time nearness information or task-specific labels. We test encoders with different output dimensions and the outcome latent embeddings can be identifiable via visualization of a few dimensions. The decoding result also shows that the embeddings preserve information about the original EEG features and can be potentially used for a wide range of downstream tasks. The source code is available at: https://***/liangfengsid/deContrastiveLearning. © ICST Institute for Computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Contrastive Learning

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RP-PGD: Boosting Segmentation Robustness with a Region-and-Prototype Based Adversarial Attack 39

RP-PGD: Boosting Segmentation Robustness with a Region-and-P...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Yuxuan Shi, Zhenbo Wang, Shuchang Yang, Wei Wang, Shaowei Xue, Yinxing School of Computer Science and Technology University of Science and Technology of China China Suzhou Institute for Advanced Research University of Science and Technology of China China Hefei National Laboratory University of Science and Technology of China China Institute of Artificial Intelligence and Blockchain Guangzhou University China Laboratory for Advanced Computing and Intelligence Engineering Wuxi China

ISBN: (纸本)157735897X

Adversarial attack and defense have been extensively explored in classification tasks, but their study in semantic segmentation remains limited. Moreover, current attacks fail to act as strong underlying attacks for adversarial training (AT), making it difficult to achieve segmentation robustness against strong attacks. In this paper, we present RP-PGD, a novel Region-and-Prototype based Projected Gradient Descent attack tailored to fool segmentation models. In particular, we propose a region-based attack, which leverages a spatial-temporal way to separate the pixels into three disjoint regions, and highlights the attack on the crucial True Region and Boundary Region. Moreover, we introduce a prototype-based attack to disrupt the feature space, further enhancing the attack capability. To boost the robustness of segmentation models, we inject adversaries generated by RP-PGD into the clean data and perform AT. Extensive experiments on multiple datasets showcase that RP-PGD generates adversaries with faster convergence and stronger attack effectiveness, surpassing state-of-the-art attacks by a large margin. Consequently, RP-PGD serves as a strong underlying attack for segmentation models to perform AT, assisting them in defending against a variety of strong attacks without incurring additional computational costs during inference. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Semantic Segmentation

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Exploring Machine Learning Applications and Future Prospects in Drug Discovery 10

Exploring Machine Learning Applications and Future Prospects...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Prabhu, M. Ramkumar Nancy, P. Rosaline, R.Anto Arockia Pandian, A. Pasumpon Devipriya, A. Arunagiri, Bhuvaneswari Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Campus Chengalpattu India Care College of Engineering Department of Computer Science and Engineering Trichy India Adhiparasakthi Engineering College Department of Technology Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

This study aims to provide a wide-ranging investigation of the ML-based drug discovery approach by discussing ML methods, results, and outcomes at the drug development process. The methodology covers the steps of data acquisition and preprocessing, feature engineering and selection, model selection and optimization, virtual screening of drugs, prediction of response to drugs, toxicity prediction, integration of multi-omics data, and validation and deployment of an ML model. The study shows superiority of ML-driven methods in terms of speeding up the candidate selection process, prediction of drug target relationships, toxicity prognosis, and integration of multiple molecular datasets for the discovery of new therapeutic targets and biomarkers. While the models might perform seriously in metrics and such, without the model interpretability, generalization, and ethics, there comes necessity to research more and to work together with various experts. A talk is given where a clear link between accurate delivering results, ethical management and regulations as well as a fully transparent reporting as can be done is given for responsible and fair ML technology development in drug discovery is made. If we cast our eye on the future, constant improvements and capital increases in ML based methods are set to achieve a fundamental breakthrough in drug development and individualized medicine and thus ultimately turn patients lives upside down for the better also global scale. © 2024 IEEE.

关键词： Forecasting

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Botanic Alchemy: Cultivating Insight through Extensive Plant Disease Dataset and Reweighted Visual Analysis 2

Botanic Alchemy: Cultivating Insight through Extensive Plant...

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2nd International Conference on Computer, Communication and Control, IC4 2024

作者： Mustafa Nawaz, S.M. Maharajan, K. Anand, J. Vijay Malathi, G. School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu India J.N.N Institutes of Engineeriung Department of Electronics and Communication Engineering Tamil Nadu India Sri Sai Ram Institute of Technology Department of Computer Science and Engineering Kanchipuram Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

Diagnosing plant diseases research tackles the vital challenge of identifying plant diseases in agriculture by focusing on the intricate complexities found in images displaying diverse symptoms, scattered lesions, and complex backgrounds. To aid this endeavor, a substantial dataset of 220,592 images is assembled. The proposed method uses a reweighting strategy that targets both visual regions and loss. Initially, weights for segmented patches are calculated based on their discriminative potential obtained from cluster distributions. These weights are then seamlessly integrated into the training process, assigning them to individual patch-label pairs during weakly-supervised training, aiding in recognizing disease-specific regions. Features extracted from this trained network create a comprehensive feature representation. This integration incorporates the GRU algorithm to encode the sequence of weighted patch features. Thorough evaluations on the newly curated dataset and a public dataset demonstrate the effectiveness and superiority of this methodology. This research significantly advances the field of plant disease recognition in image processing, opening doors for further innovative exploration. © 2024 IEEE.

关键词： Learning algorithms

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Cyberbullying Image Classification using Artificial Intelligence for Safer Online Platform

Cyberbullying Image Classification using Artificial Intellig...

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Pervasive computing and Social Networking (ICPCSN), International Conference on

作者： M. Vamshi Krishna R. Asish Verma P. Kirubanantham Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai Kattankulathur India

ISBN: (数字)9798350386349

ISBN: (纸本)9798350386356

The toll that cyberbullying takes on victims' life is incalculable because a person's approach to it is highly individualized. For some victims, the message might be harsh, but for others, it might be normal. It is quite difficult to find reliable content because of the ambiguities in cyberbullying communications. Several research works have reportedly used a text- content-based methodology to answer this question. However, picture-based cyberbullying detection is now receiving less attention. Enhancing a methodology that permits the prevention of social media posts on image-based cyberbullying issues is the aim of this job. This study provides support for an automated version that searches a malicious social media network for a cyber-symbol image, relying just on switch mastery. By applying fashion knowledge, it is feasible to infer hidden contextual functions from texts containing cyberbullying. Our test includes two photo datasets: pictures of cyberbullying and pictures of non-bullying. The datasets might be helpful to Destiny researchers in expanding their research. It's challenging to find a pleasant and suitable version to recognize bullying photographs, therefore experiment with both transfer the experiment's findings showed that switch learning models are an excellent method for predicting conversations that are only based on images and involve cyberbullying.

关键词： Training Accuracy Image recognition Computational modeling Cyberbullying Switches Safety

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