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Classifying Psychiatric Patients Using Machine Learning 25

Classifying Psychiatric Patients Using Machine Learning

引用

25th International Arab Conference on Information Technology, ACIT 2024

作者： Alrjoob, Hmmam M. Alazaidah, Raed Batyha, Radwan Khafajeh, Hayel Elsoud, Esraa Abu Saeb Al-Sherideh, Ala'a Elrashidi, Ali Alqaraleh, Muhyeeddin Amer, Waleed Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Applied Science Private University Faculty of Information Technology Department of Computer Science Amman Jordan Zarqa University Faculty of Information Technology Department Computer Science Zarqa Jordan Zarqa University Faculty of Information Technology Department of Cyper Security Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Zarqa University Faculty of Information Technology Department of Software Engineering Zarqa Jordan Zarqa University Faculty on Information Technology Department of Cyber Security Zarqa Jordan

ISBN: (纸本)9798331540012

Psychological disorders are considered chronic illnesses that affect a wide range of populations. Some studies in the United States indicate that one in every eight individuals is affected by a psychological disorder. Additionally, individuals with a psychological disorder often have an impact on the nature of the lives of those around them. It is beneficial to detect the illness in its early stages to increase the likelihood of recovery. In this research, thirteen classification algorithms are used. Each algorithm has an interesting approach. It can be used to analyze several datasets where three datasets specific to Psychiatric patients were selected. The datasets have been chosen from UCI Machine Learning Repository. The datasets were re-analyzed after only 50% of the characteristics were approved after being evaluated by the several feature selection techniques. The main objective of this research is to find the best classifier and feature selection strategy in analyzing datasets of Psychiatric patients. After conducting numerous comparisons, it became evident that, the best classifier for the Autism-Adult-Data dataset are Bagging and Random Forest. As for the best learning strategy, it was found to be Functions and Trees, the best feature selection was obtained using Symmetrical Uncourt Attribute Evaluation. Both IBK and KStar classifier proved to be the best classifier for the Parkinson's dataset, Lazy demonstrated to be the most effective learning strategy, Correlation Attribute Evaluation technique yielded the best feature selection. Logistic emerged as the best classifier for planning-relax dataset, the best learning strategy was Bayes, the best feature selection was found to be Symmetrical Uncert Feature Selection. © 2024 IEEE.

关键词： Contrastive Learning

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Learning Multi-scale Representations with Single-stream Network for Video Retrieval

Learning Multi-scale Representations with Single-stream Netw...

引用

2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Wang, Chia-Hui Tseng, Yu-Chee Chiang, Ting-Hui Chen, Yan-Ann National Yang Ming Chiao Tung University Department of Computer Science Taiwan National Yang Ming Chiao Tung University College of AI Taiwan Chunghwa Telecom Laboratories Advanced Technology Laboratory Taoyuan Taiwan Yuan Ze University Department of Computer Science and Engineering Taiwan

ISBN: (纸本)9798350302493

With the explosive growth of video contents in the Internet, video retrieval has become an important issue that can benefit video recommendation and copyright detection. Since the key features of a video may distribute in distant regions of a lengthy video, several works have made a success by exploiting multi-stream, multi-scale architectures to learn and merge distant features. However, a multi-stream network is costly in terms of memory and computing overhead. The number of scales and these scales are handcrafted and fixed once a model is finalized. Further, being more complicated, multi-stream networks are more prone to being overfitting and lead to poorer generalization. This paper proposes a single-stream network with built-in dilated spatial and temporal learning capability. By combining with modern techniques, including Denoising Autoencoder, Squeeze-and-Excitation Attention, and Triplet Comparative Mechanism, our model achieves state-of-the-art performance in several video retrieval tasks on the FIVR200K, CC WEB VIDEO, and EVVE datasets. © 2023 IEEE.

关键词： Excited states

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Code-Switching Text Generation and Injection in Mandarin-English ASR 48

Code-Switching Text Generation and Injection in Mandarin-Eng...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Yu, Haibin Hu, Yuxuan Qian, Yao Jin, Ma Liu, Linquan Liu, Shujie Shi, Yu Qian, Yanmin Lin, Edward Zeng, Michael Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Ai Institute X-LANCE Lab Department of Computer Science and Engineering China Microsoft Corporation United States

ISBN: (纸本)9781728163277

Code-switching speech refers to a means of expression by mixing two or more languages within a single utterance. Automatic Speech Recognition (ASR) with End-to-End (E2E) modeling for such speech can be a challenging task due to the lack of data. In this study, we investigate text generation and injection for improving the performance of an industry commonly-used streaming model, Transformer-Transducer (T-T), in Mandarin-English code-switching speech recognition. We first propose a strategy to generate codeswitching text data and then investigate injecting generated text into T-T model explicitly by Text-To-Speech (TTS) conversion or implicitly by tying speech and text latent spaces. Experimental results on the T-T model trained with a dataset containing 1,800 hours of real Mandarin-English code-switched speech show that our approaches to inject generated code-switching text significantly boost the performance of T-T models, i.e., 16% relative Token-based Error Rate (TER) reduction averaged on three evaluation sets, and the approach of tying speech and text latent spaces is superior to that of TTS conversion on the evaluation set which contains more homogeneous data with the training set. © 2023 IEEE.

关键词： Transducers

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Machine Learning and Ensemble Prediction for Customer Churn Prediction Analysis

Machine Learning and Ensemble Prediction for Customer Churn ...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： B. Krishna Thanishka S. Varnika Reddy V. Shuchir Hariharan Shanmugasundaram R.Karthikeyan AV Senthil Kumar Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India Department of CSE(AI&ML) Vardhaman College of Engineering Hyderabad India Department of MCA Hindusthan College of Arts & Science Coimbatore Tamilnadu India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Customer churn prediction is super important for businesses. It helps them keep customers around make money over time. In this study, we look at ways to predict when customers might leave using machine learning. We use different methods called ensemble techniques like Random Forest Gradient Boosting Machines (GBM). These tools help build strong models that can spot customers who may churn. Our goals are simple: we want better prediction accuracy, strong stable models, and useful tips to improve retention strategies. After testing our ensemble model, we found it works better in accuracy compared to single models or older methods. This means that businesses can understand their customers better. They can also create smart strategies to keep customers happy, which helps lower churn rates and boosts long-term success.

关键词： Accuracy Computational modeling Stacking Predictive models Boosting Ensemble learning Churn Random forests Bagging Testing

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Maximum Entropy Scaled Super-Pixel Segmentation for Effective Text Detection 2

Maximum Entropy Scaled Super-Pixel Segmentation for Effectiv...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Almusawi, Muntather Sowmya, M. Sureshkumar, S. Saganya, V. Nagendar, Y. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Ai & Ds Bengaluru India Kalasalingam Academy of Research and Education Department of Cse Krishnankoil India Sal Institute of Technology and Engineering Research Sola road Ahmedabad India School of Computer Science and Engineering Sr University Warangal India

ISBN: (纸本)9798350373110

Optical Character Recognition OCR systems rely on text detection models to locate text regions within images before performing character recognition. This is crucial for converting scanned documents or images into editable and searchable digital text. In this research facing some major problems like variability in text appearance, Complex Background, and different Language and script. In this paper presents the segmentation based effective text detection using Maximum entropy scaled Super-Pixels' segmentation (MESP) method. This model contains three main stages, firstly the MSRA-TD500 dataset is used to extract the features. Second stage is feature selection;the extracted features are having the unwanted information in this stage remove the unwanted features select the useful features using the convolutional neural network. The final stage plays a major role in this research, in this stage were apply the MESP method for detecting the text based on segmentations approach using the relent features. The numerical validation of the proposed method is Precision of 95.7%, Recall of 86.9%, and F-Measure of 91.6%. The model compares with other existing models such as Reinforcement Shrink-Mask (RSM), Segmentation-based Attention Module (SAM), Improved differentiable Binarisation network for natural scene text detection (IDBNet), Segmentations-based Label Distribution Learning (Seg-LDL) and Convolutional Neural Network-Adaptive Feature Selection (CNN- AFS). © 2024 IEEE.

关键词： Convolutional neural networks

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Hierarchical Pronunciation Assessment with Multi-Aspect Attention

Hierarchical Pronunciation Assessment with Multi-Aspect Atte...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Heejin Do Yunsu Kim Gary Geunbae Lee Graduate School of AI Postech Department of Computer Science and Engineering Postech

Automatic pronunciation assessment is a major component of a computer-assisted pronunciation training system. To provide in-depth feedback, scoring pronunciation at various levels of granularity such as phoneme, word, and utterance, with diverse aspects such as accuracy, fluency, and completeness, is essential. However, existing multi-aspect multi-granularity methods simultaneously predict all aspects at all granularity levels; therefore, they have difficulty in capturing the linguistic hierarchy of phoneme, word, and utterance. This limitation further leads to neglecting intimate cross-aspect relations at the same linguistic unit. In this paper, we propose a Hierarchical Pronunciation Assessment with Multi-aspect Attention (HiPAMA) model, which hierarchically represents the granularity levels to directly capture their linguistic structures and introduces multi-aspect attention that reflects associations across aspects at the same level to create more connotative representations. By obtaining relational information from both the granularity- and aspect-side, HiPAMA can take full advantage of multi-task learning. Remarkable improvements in the experimental results on the speachocean762 datasets demonstrate the robustness of HiPAMA, particularly in the difficult-to-assess aspects.

关键词： Training computer architecture Linguistics Signal processing Multitasking Robustness Encoding

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POINTACL: Adversarial Contrastive Learning for Robust Point Clouds Representation Under Adversarial Attack

POINTACL: Adversarial Contrastive Learning for Robust Point ...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Junxuan Huang Junsong Yuan Chunming Qiao Yatong An Cheng Lu Chen Bai Department of Computer Science and Engineering University at Buffalo Xmotors.ai

Adversarial contrastive learning (ACL) is considered an effective way to improve the robustness of pre-trained models. In contrastive learning, a projector which consists of multilayer perceptron (mlP) will project high dimension 3D point cloud feature into low dimension for calculating contrastive loss during contrastive *** propose a novel method for generating high-quality 3D adversarial examples for adversarial training, which leverages the virtual adversarial loss with the feature representations prior to projection in a contrastive learning framework. To train the self-supervised contrastive learning framework adversarially, we introduce our robust aware loss function. Additionally, we show that incorporating high difference points using the Difference of Normal (DoN) operator as an additional input for adversarial self-supervised contrastive learning can significantly enhance the adversarial robustness of the pre-trained model. Our proposed method, POINTACL, is evaluated on several downstream tasks, including 3D classification and 3D segmentation using multiple datasets. Our experimental results demonstrate that POINTACL achieves state-of-the-art performance in terms of robust accuracy when compared to other contrastive adversarial learning methods.

关键词： Point cloud compression Training Solid modeling Three-dimensional displays Signal processing Multilayer perceptrons Robustness

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Innovative Approaches to Reduce Carbon Footprint and air Pollution: The Role of ai, ml, Cloud Computing, and IoT 4th

Innovative Approaches to Reduce Carbon Footprint and Air Pol...

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4th International Conference on Machine Learning, Advances in Computing, Renewable Energy and Communication, MARC 2023

作者： Ghalwan, Manish Bisht, Sapna Pant, Rajesh Lande, Jayapal Pant, Shivani Swami, Siddharth Department of Civil Engineering Uttaranchal Institute of Technology Uttaranchal University Uttarakhand Dehradun India Uttranchal Institute of Management Uttaranchal University Uttarakhand Dehradun India School of Computer Science and AI SR University Warangal India Department of Computer Science Engineering Uttaranchal Institute of Technology Uttaranchal University Uttarakhand Dehradun India School of Environment and Natural Resources Doon University Uttarakhand Dehradun India

ISBN: (纸本)9789819752300

In recent years, the phrase "carbon footprint" has gained enormous popularity. The environment is being disrupted by severe global warming and its accompanying effects due to increased greenhouse gases in the atmosphere. This review’s objectives are to highlight recent developments in our understanding, summarise the body of available information, and propose research goals. The background material in the article covers the causes, trends in emissions and their effect on the environment. In this regard, the incorporation of carbon footprint and air pollution 4.0 with various technologies (Internet of Things (IoT), artificial intelligence (ai), blockchain, and big data for acquiring data and current industrial breakthroughs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Carbon capture and utilization

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Hybrid Approach of TabNet and Transformer-XGBoost for Predicting Traffic Flow in Smart Cities

Hybrid Approach of TabNet and Transformer-XGBoost for Predic...

引用

Smart and Sustainable Developments in Electrical engineering (SSDEE), IEEE International Conference on

作者： Shreyas Rajendra Hole Vinothkumar Kolluru Shreekant Salotagi Yagnesh Challagundla Sudeep Mungara Mude Nagarjuna Naik Computer Science & Engineering (AI&ML) Dayananda Sagar University Bangalore India Department of data science Stevens Institute of technology Hoboken NJ U.S.A. Computer Science & Engineering Dayananda Sagar University Bangalore India Department of Computer science Herbert Wertheim College of Engineering University of Florida Gainesville Florida USA Masters in Information Systems Stevens Institute of Technology Hoboken NJ U.S.A.

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

A smart city's transportation system can be en-hanced if there is an accurate prediction of the traffic flow which develops in an area as time goes on. This article investigates the relative accuracy of predicting traffic flow at target intersections in a city, taking advantage of two models: Tab Net and a hybrid Transformer-XGBoost. Several metrics including Mean Squared Error (MSE) and Coefficient of determination (R2 score) among others were employed to evaluate the models employed. A sequential deep learning model known as Tab Net triumphing in the competition with MSE, RMSE, and MAE being 34.48, 5.87, and 3.73 respectively, and R2 score as high as 0.917, although performing well while enhancing identification of features such as year and junction. On the other hand, while reporting similar R2 score as low as 0.184, the Transformer-XGBoost hybrid model had structuring limitations where complex interdependencies and time variations were involved recording MSE, RMSE and MAE as 338.52, 18.40 and 13.24 respectively. The graphs of residuals and QQ plots served to uncover that the hybrid model failed making a right prediction during high traffic volume days, as general tabnet results shown in the final graphs are more stable. Following investigation of the models in this study, the researchers suggest implementing TabNet as a better option for traffic management explaining its high accuracy and providing valuable guidance for cities. This would result in improvement of the traffic flow prediction, which is highly needed for effective urban mobility solutions.

关键词： Measurement Hands Adaptation models Accuracy Smart cities Transportation Tail Predictive models Transformers Junctions

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Exploring the Impact of ai-Driven Natural Language Processing in Computational Analysis

Exploring the Impact of AI-Driven Natural Language Processin...

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International Conference on Computing and Networking Technology (ICCNT)

作者： K. Ranjith Singh S Rajarajeswari Rutuja S. Kadam Ravindra Kumar Trapty Agrawal R. Raghavendra Department of Computer Science Karpagam Academy of Higher Education Coimbatore Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Science and Engineering (AI & ML) Vishwakarma Institute of Technology Pune India Department of Civil Engineering Vivekananda Global University Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This paper explores the blessings of applying artificial intelligence and herbal language processing (NLP) strategies to massive-scale computation analysis. Natural language processing is fundamental to considering complex analytics tasks with relative ease using currently available technology. This paper mainly specializes in using NLP in the vicinity of sentiment evaluation. Sentiment evaluation uses numerous algorithms to determine sentiment from text, and it may be used to benefit insights from vast volumes of statistics. An exploration is performed to demonstrate the benefits of utilizing ai-driven NLP while appearing complicated sentiment analysis. The studies offer an overview of the techniques and equipment involved, as well as the demanding situations that should be confronted. Additionally, modern-day and potential programs are mentioned, together with their implications for company and purchaser decision-making. The paper concludes by providing several potential applications for ai-pushed NLP in the analytic space. Through the use of ai-pushed NLP, groups, and agencies can now gather and analyze records to benefit precious insights more effectively than ever before.

关键词： Sentiment analysis Decision making Collaboration Companies Natural language processing Artificial intelligence

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