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Machine Learning Technique to Detect and Classify Mental Illness on Social Media Using Lexicon-Based Recommender System

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Computational Intelligence and Neuroscience 2022年第0期2022卷 5906797页

作者： Sumathy, B. Kumar, Anand Sungeetha, D. Hashmi, Arshad Saxena, Ankur Kumar Shukla, Piyush Nuagah, Stephen Jeswinde Department of Instrumentation and Control Engineering Sri Sairam Engineering College Chennai India School of Computer Science and Applications REVA University Bangalore India Department Electronics and Communication Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai India King Abdulaziz University Jeddah Saudi Arabia Indus Institute of Information and Communication Technology Indus University Gujarat Ahmedabad India Computer Science and Engineering Department University Institute of Technology Rajiv Gandhi Proudyogiki Vishwavidyalaya Technological University of Madhya Pradesh Bhopal462033 India Department of Electrical Engineering Tamale Technical University Tamale Ghana

The emergence of social media has allowed people to express their feelings on products, services, films, and so on. The feeling is the user's view or attitude towards any topic, object, event, or service. Overall, feelings have always influenced people's decision-making. In recent years, emotions have been analyzed intensively in natural language, but many problems still have to be watched. One of the most important problems is the lack of precise classification resources. Most of the research into feeling gradation is concerned with the issue of polarity grading, although, in many practical applications, this relatively grounded feeling measure is insufficient. Design methods are therefore essential, which can accurately classify feelings into a natural language. The principal goal of the research is to develop an overflow of grammatical rules-based classification of Indian language tweets. In this work, three main challenges are identified to classify feelings in Indian language tweets and possible methods for tackling such issues. Firstly, it has been found that the informal nature of tweets is crucial for the classification of feelings. Based on the tweets, the mental illness of the person has been classified. Therefore, to categorize Indian language tweets, a combination of grammar rules based on adjectives and negations is proposed. Secondly, people often express their feelings with slang words, abbreviations, and mixed words. A technique called field tags is used to include nongrammatical arguments such as slang words and diverse words. Thirdly, if a tweet is more complex, the morphological richness of the Indian language results in a loss of performance. The grammar rules are embedded in N-gram techniques and machine learning methods. These methods are grouped into three approaches, which functionally predict Indian language tweets with syntactic words. © 2022 B. Sumathy et al.

关键词： Decision making

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The Gm System for the use of AGMR System

The Gm System for the use of AGMR System

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Advance Computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： Aditya Verma Hassan M. Al-Jawahry H. A. Alsailawi P. Kirubanantham SherinEliyas Osama Nazim Saadoun Manalmoradkarim Doaa Talib Zaidan Department of CSE Graphic Era Hill University Research Scholar Graphic Era Deemed to be University Dehradun Uttarakhand Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq Department of Chemistry and Biochemistry College of Medicine University of Kerbala Kerbala Iraq Department of Computing Technologies School of Computing College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and TechnologySRM Nagar Kattankulathur Chennai India Department of computer applications Hindustan institute of technology and science Law Department Al-kunooze University College Basra Iraq College of Pharmacy National University of Science and Technology DhiQar Iraq Department of Laser and Optical Electronics Engineering Kut University College Al Kut Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Due to its ability to navigate crop fields with ease, mobile robots have become more important in the agricultural industry. This study discusses a number of important topics related to agricultural robot navigation, including mapping, localization, route planning, obstacle detection, and controlled guidance. With laser-based weeding tools installed, the suggested Guiding Manager aims to maximize tracking performance of mobile autonomous robots. Integrating spiral, lateral, and linear speed controls, the system can accommodate a variety of trajectories, including curves and straight lines, that are seen in agricultural settings. To confirm the performance of the controllers under various conditions, such as loose soil, stones, and humidity, a thorough field test is conducted on a tracked mobile platform. With the Lateral Controller, the average angular error is around -0.005 rad, and the average absolute lateral error is approximately 0.09 m. In contrast, the average absolute lateral error and angular inaccuracy displayed by the Spiral Controller are 0.15 m and 0.013 rad, respectively. The system guarantes the safe and accurate implementation of robotic tasks in agriculture, as shown by its ±0.009 rad angular precision and ±0.015 m horizontal accuracy.

关键词： Spirals Accuracy Navigation Service robots Velocity control Soil Planning

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Predicting blood glucose level using salivary glucose and other associated factors: A machine learning model selection and evaluation study

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Informatics in Medicine Unlocked 2024年 48卷

作者： Chopra, Aditi Rao, Rohini R. Kamath, Shobha U. Akhila Arun, Sanjana Shettigar, Laasya Department of Periodontology Manipal College of Dental Sciences Manipal Academy of Higher Education Manipal Manipal Pin: 576 104 Karnataka India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal Manipal Pin: 576 104 Karnataka India Department of Biochemistry Kasturba Medical College and Hospital Manipal Academy of Higher Education Manipal Manipal Pin: 576 104 Karnataka India

Introduction: There is a need for designing non-invasive methods to predict blood glucose levels to ensure timely diagnosis of Diabetes Mellitus. Needle anxiety and bleeding disorders preclude many from undertaking blood tests. Objectives: The primary objective of this study was to assess if biomarkers like saliva can be used to estimate blood glucose levels. The second objective was to develop and evaluate Machine Learning (ML) models to predict blood glucose levels based on salivary glucose and associated features. An insight into the patient's features, which was important for predicting blood glucose levels, was also required. Methods: A cross-sectional study was conducted, and blood and saliva samples, along with patient-related data, were collected from healthy and diabetic patients. ML techniques were applied to the data to develop a tool for predicting blood glucose levels using patient features. The prediction intervals were computed, clinical accuracy was assessed, and important features for the prediction were identified. Results: The Random Forest Regressor Model, with features identified using the wrapper method, was selected as the best, with an average RMSE of 43.28. The prediction intervals were computed for point estimate, MAE = 23.821, and coverage was 100 percent, the clinical accuracy was compared with that of glucometers and continuous monitoring systems. All predicted values are in Zones A and B of the Clarke error grid, and the bias was 6.41. The most important feature for predicting blood glucose level is salivary glucose level, followed by known risk factors like Family History, BMI, etc. The study found that salivary glucose levels are insufficient to classify blood glucose levels as high or normal. Conclusion: The study concluded that salivary glucose with associated patient features could be a potential non-invasive biomarker for predicting blood glucose levels in patients. The developed ML model could be deployed in a device that inputs

关键词： Blood glucose level Confirmatory prediction Diabetes mellitus Feature selection Machine learning model Non-invasive method Random forest regressor Salivary glucose

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Brain MRI Synthesis Using Stylegan2-ADA

Brain MRI Synthesis Using Stylegan2-ADA

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IEEE International Symposium on Biomedical Imaging

作者： Matteo Lai Chiara Marzi Mario Mascalchi Stefano Diciotti Department of Electrical Electronic and Information Engineering "Guglielmo Marconi" - DEI University of Bologna Cesena Italy Alma Mater Research Institute for Human-Centered Artificial Intelligence University of Bologna Bologna Italy Department of Statistics Computer Science Applications "G. Parenti" University of Firenze Firenze Italy Experimental and Clinical Biomedical Sciences "Mario Serio" University of Florence Firenze Italy Clinical Epidemiology Unit Institute for Cancer Research Prevention and Clinical Network (ISPRO) Florence Italy

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Deep learning (DL) applications in the medical field often face challenges related to limited data availability, resulting in issues like overfitting and imbalanced datasets. Synthetic data offers a promising solution to these problems by enabling data augmentation and enhancing the performance of DL models. In this study, we trained the state-of-the-art generative model StyleGAN2-ADA on 1412 images from the Alzheimer’s disease neuroimaging initiative (ADNI) dataset to generate synthetic slices of T1-weighted brain MRI of healthy subjects. The quality of the synthetic images has been evaluated through quantitative and qualitative assessments, including a visual Turing test conducted by an expert observer with 2000 images. The observer achieved an accuracy of 52.95%, indicative of a performance level comparable to random guessing. These results demonstrate the capability of StyleGAN2-ADA to generate anatomically relevant synthetic brain MRI data.

关键词： Neuroimaging Visualization Magnetic resonance imaging Observers Brain modeling Data augmentation Data models

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Evaluating Topological Insulator-Based FETs in Circuit-Level Performance with Advanced Device Simulations

Evaluating Topological Insulator-Based FETs in Circuit-Level...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： A Karthik Asha V T Bhargava Ramu Rupa Rani Sharma Ginni Nijhawan Zahraa Salem Mohsen Institute of Aeronautical Engineering Hyderabad Department of Computer Applications New Horizon College of Engineering Bangalore Department of Electrical and Electronics Engineering MLR Institute of Technology Hyderabad Telangana Dept. of ASH GL Bajaj Institute of Technology & Management Greater Noida U.P. India Lovely Professional University Phagwara College of Engineering Technology National University of Science and Technology Dhi Qar Iraq

In recent years., topological insulators (TIs) have emerged as a promising material class in the realm of advanced electronic devices due to their unique electronic properties. This paper presents a comprehensive evaluation of Field-Effect Transistors (FETs) based on topological insulators through advanced device simulations to understand their potential in circuit-level applications. We have employed state-of-the-art simulation tools to model the behavior of TI-based FETs under various operating conditions and benchmarked their performance against conventional semiconductor-based FETs. Our results indicate that TI-based FETs exhibit superior switching characteristics., reduced leakage currents., and enhanced thermal stability. Furthermore., the inherent spin-momentum locking property of TIs introduces a new dimension in device design., paving the way for spintronic applications. The study also highlights the challenges associated with integrating TI-based FETs in conventional circuits., such as interface quality and material purity. In conclusion., while topological insulator-based FETs hold significant promise for next-generation electronics., meticulous material engineering and device optimization are crucial for their successful integration into mainstream circuitry.

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Predicting the Impacts of the Environment on the Probability and Severity of Electrical Injuries by AIML

Predicting the Impacts of the Environment on the Probability...

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New Frontiers in Communication, Automation, Management and Security (ICCAMS), International Conference on

作者： Araddhana Arvind Deshmukh K Parish Venkata Kumar B.Anjanee Kumar Pramoda Patro Pavitar Parkash Singh N. Ashokkumar Department of Artificial Intelligence and Data science Marathwada Mitra Mandal College of Engineering Pune India Department of Computer Applications Velagapudi Ramakrishna Siddhartha Engineering College (Autonomous) Vijayawada India Electronics and Communication Engineering Dadi institute of engineering and technology Visakhapatnam India Department of Mathematics Koneru Lakshmaiah Education Foundation Hyderabad India Department of Management Lovely Professional University Phagwara India Department of ECE Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati India

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

The acceleration of global warming and environmental degradation has been attributed to industrial growth and fossil fuel utilization. Consequently, this phenomenon has resulted in a rise in the frequency and intensity of natural disasters. Due to their predominantly outdoor installation, generators are subject to significant impacts. The occurrence of natural disasters has led to an increase in accidents associated with electrical equipment. This paper analyses statistical data on electrical fires, electric shock accidents, and electricity-related accidents caused by climate change in domestic settings to determine the associated risk *** this study, we propose a risk assessment model for electrical fires, explicitly focusing on every month. Our approach involves analysing data on electrical hazards in different regional areas, particularly metropolitan cities, considering climatic conditions such as temperature and humidity. Additionally, we aim to evaluate the accident risk rating associated with natural disasters, specifically those related to low and high-voltage equipment. In this study, a risk analysis model has been developed to assess electrical hazards in different regions and equipment types. The model presented herein serves as a foundational predictive tool. Hence, offering electrical security amenities in the forthcoming period is feasible by giving a risk estimation map delineating potential dangers specific to each geographical area and wiring category via web platforms or mobile applications utilizing the investigation data provided. Additionally, it is imperative to enhance the resilience and dependability of electrical components as a proactive measure against electrocute ions resulting from climate change-induced emergencies.

关键词： Wiring Temperature distribution Electric potential Analytical models Disasters Urban areas Humidity

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Leveraging tumor heterogeneity: heterogeneous graph representation learning for cancer survival prediction in whole slide images 24

Leveraging tumor heterogeneity: heterogeneous graph represen...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Junxian Wu Xinyi Ke Xiaoming Jiang Huanwen Wu Youyong Kong Lizhi Shao School of Internet Anhui University and Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Department of Pathology State Key Laboratory of Complex Severe and Rare Disease Molecular Pathology Research Center Peking Union Medical College Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China School of Bioinformatics Chongqing University of Post and Telecommunications Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University and Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University) Ministry of Education China School of Internet Anhui University

ISBN: (纸本)9798331314385

Survival prediction is a significant challenge in cancer management. Tumor microenvironment is a highly sophisticated ecosystem consisting of cancer cells, immune cells, endothelial cells, fibroblasts, nerves and extracellular matrix. The intratumor heterogeneity and the interaction across multiple tissue types profoundly impact the prognosis. However, current methods often neglect the fact that the contribution to prognosis differs with tissue types. In this paper, we propose ProtoSurv, a novel heterogeneous graph model for WSI survival prediction. The learning process of ProtoSurv is not only driven by data but also incorporates pathological domain knowledge, including the awareness of tissue heterogeneity, the emphasis on prior knowledge of prognostic-related tissues, and the depiction of spatial interaction across multiple tissues. We validate ProtoSurv across five different cancer types from TCGA (i.e., BRCA, LGG, LUAD, COAD and PAAD), and demonstrate the superiority of our method over the state-of-the-art methods. Source code is available at https://***/wjx-error/ProtoSurv.

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Unified View of Damage leaves Planimetry & Analysis Using Digital Images Processing Techniques

Unified View of Damage leaves Planimetry & Analysis Using Di...

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Computational Intelligence and Sustainable Engineering Solutions (CISES), International Conference on

作者： Pijush Kanti Kumar DeepKiran Munjal Sunita Rani Anurag Dutta Liton Chandra Voumik A. Ramamoorthy Department of Information Technology Government College of Engineering & Textile Technology Serampore Calcutta India Department of Computer Applications G. L. Bajaj Institute of Technology and Management Greater Noida Uttar Pradesh India Department of Engineering Administration Noida Institute of Engineering and Technology Greater Noida Uttar Pradesh India Department of Economics Noakhali Science and Technology University Noakhali Bangladesh Department of Mathematics Velammal Engineering College Anna University Chennai Tamil Nadu India

The detection of leaf diseases in plants generally involves visual observation of patterns appearing on the leaf surface. However, there are many diseases that are distinguished based on very subtle changes in these visually observable patterns. This paper attempts to identify plant leaf diseases using image processing techniques. The focus of this study is on the detection of citrus leaf canker disease. Canker is a bacterial infection of leaves. Symptoms of citrus cankers include brown spots on the leaves, often with a watery or oily appearance. The spots (called lesions in botany) are usually yellow. It is surrounded by a halo of the leaves and is found on both the top and bottom of the leaf. This paper describes various methods that have been used to detect citrus leaf canker disease. The methods used are histogram comparison and k-means clustering. Using these methods, citrus canker development was detected based on histograms generated based on leaf patterns. The results thus obtained can be used, after consultation with experts in the field of agriculture, to identify suitable treatments for the processes used.

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A multi-stage semi-supervised learning for ankle fracture classification on CT images

arXiv

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arXiv 2024年

作者： Liu, Hongzhi Li, Guicheng Nie, Jiacheng Tang, Hui Yang, Chunfeng Feng, Qianjin Xu, Hailin Chen, Yang The School of Computer Science and Engineering Southeast University Nanjing210096 China The Peking University People's Hospital Beijing100000 China The Guangdong Provincial Key Laboratory of Medical Image Processing School of Biomedical Engineering Southern Medical University Guangzhou510515 China The Key Laboratory of New Generation Artificial Intelligence Technology and its Interdisciplinary Applications Southeast University Ministry of Education China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Nanjing210096 China Jiangsu Key Laboratory of Molecular and Functional Imaging Department of Radiology Zhongda Hospital Southeast University Nanjing210009 China

Because of the complicated mechanism of ankle injury, it is very difficult to diagnose ankle fracture in clinic. In order to simplify the process of fracture diagnosis, an automatic diagnosis model of ankle fracture was proposed. Firstly, a tibia-fibula segmentation network is proposed for the joint tibiofibular region of the ankle joint, and the corresponding segmentation dataset is established on the basis of fracture data. Secondly, the image registration method is used to register the bone segmentation mask with the normal bone mask. Finally, a semi-supervised classifier is constructed to make full use of a large number of unlabeled data to classify ankle fractures. Experiments show that the proposed method can segment fractures with fracture lines accurately and has better performance than the general method. At the same time, this method is superior to classification network in several indexes. Copyright © 2024, The Authors. All rights reserved.

关键词： Image classification

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Non-Variational ADAPT algorithm for quantum simulations

arXiv

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arXiv 2024年

作者： Tang, Ho Lun Chen, Yanzhu Biswas, Prakriti Magann, Alicia B. Arenz, Christian Economou, Sophia E. Department of Physics Virginia Tech BlacksburgVA24061 United States Virginia Tech Center for Quantum Information Science and Engineering BlacksburgVA24061 United States Department of Physics Florida State University TallahasseeFL32306 United States School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States Quantum Algorithms and Applications Collaboratory Sandia National Laboratories AlbuquerqueNM87185 United States

We explore a non-variational quantum state preparation approach combined with the ADAPT operator selection strategy in the application of preparing the ground state of a desired target Hamiltonian. In this algorithm, energy gradient measurements determine both the operators and the gate parameters in the quantum circuit construction. We compare this non-variational algorithm with ADAPT-VQE and with feedback-based quantum algorithms in terms of the rate of energy reduction, the circuit depth, and the measurement cost in molecular simulation. We find that despite using deeper circuits, this new algorithm reaches chemical accuracy at a similar measurement cost to ADAPT-VQE. Since it does not rely on a classical optimization subroutine, it may provide robustness against circuit parameter errors due to imperfect control or gate synthesis. © 2024, CC BY.

关键词： Hamiltonians

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