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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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Evaluation of energy consumption and delay in different modes of data aggregation for wireless sensor networks

Evaluation of energy consumption and delay in different mode...

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2020 International Conference on UK-China Emerging Technologies, UCET 2020

作者： Qayyum, Bushra Li, Guinan Kasil, M. Haque, Bushra Ali, Kamran Lasebae, Aboubaker Raza, Mohsin Errahmadi, Btissam Buitems Department of Computer Science Quetta Pakistan College Zhejiang University Control Science and Engineering China Faculty of Science and Technology Middlesex University London United Kingdom Department of Computer Science Edge Hill University United Kingdom Huawei Technologies Research and Development Edinburgh United Kingdom

ISBN: (纸本)9781728194882

The main challenge in designing a wireless sensor network is the optimization of the energy consumption and the delay in passing the signal from leaf nodes to the base station in different application areas. Examples of sensor networking applications include fire detection, disaster recovery, crowd management and many others. The two parameters;signal delay and energy consumption are very interconnected with a huge tradeoff as trying to control one of them will certainly affect the other adversely. The objective of this paper is to propose a scheme of network architecture where both parameters are optimized resulting in minimum energy consumption and minimum possible signal delay. We propose implementing partial aggregation as opposed to no aggregation or full aggregation. Results obtained from the test carried on an actual network setting have shown that partial aggregation works better compared to the other two techniques. © 2020 IEEE.

关键词： Energy utilization

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A Review of Intelligent Vision Enhancement Technology for Battlefield

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Wu, Yufeng He, Yanjun Li, Ning Wang, Guowei Zhang, Longfei Zhang, Fuquan Zhu, Yifan The 15th Research Institute China Electronics Technology Group Corporation Beijing100083 China Key Laboratory of Digital Performance and Simulation Technology Beijing Institute of Technology Beijing100081 China College of Computer and Control Engineering Minjiang University Fuzhou350108 China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu610021 China Department of Computer Science and Technology Tsinghua University Beijing100081 China

This paper reviews the application of intelligent vision enhancement technology in the battlefield environment and explores new research directions. This paper mainly introduces three parts. First, we introduce a solution for enhancing the battlefield situation and using head-mounted displays for soldier combat. Second, we summarize three core technologies supporting intelligent vision enhancement technology: 3D environment reconstruction, tracking and registration, and situational awareness. Third, we summarize three application directions of intelligent vision enhancement technology on the battlefield. Finally, the problems and challenges in the research work are proposed, including current issues such as accurate battlefield situational awareness by augmented reality technology, multiperson collaborative management and data flow, as well as challenges in future battlefield situation enhancement and perception. The future development trend of intelligent vision enhancement technology on the battlefield has been prospected. Two meanings of this paper are as follows: the first is to review the research status of intelligent vision enhancement technology from the technical level and identify the key technical points that may restrict development in the future;the second is to analyze the advantages and disadvantages of intelligent vision enhancement technology from the level of battlefield application and the roles of users. In addition, this paper proposes how to take the lead and take initiative in future wars. © 2023 Yufeng Wu et al.

关键词： Augmented reality

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Effects of Flow Deviation on Wall Temperature in the Water-Cooled Wall of Power Plant Boiler

SSRN

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SSRN 2022年

作者： Jin, Yingai Zhang, Yiwen Li, Liang Li, Yang State Key Laboratory of Automotive Simulation and Control Jilin University Changchun130025 China College of Automotive Engineering Jilin University Changchun130025 China School of Physics Engineering and Computer Science University of Hertfordshire HatfieldAL10 9AB United Kingdom

Power station boilers have uneven temperatures in their water-cooled wall tubes, leading to accidents like tubes bursting. This study obtains the thermal load distribution through a thermal calculation and a thermal load nonuniformity coefficient of a power station boiler. The IAPWS-IF97 formula can accurately calculate the thermophysical parameters of water and water vapour in five regions: single-phase liquid, single-phase vapour, critical point, two-phase and high-temperature. The temperature distribution of the working medium and heat transfer coefficient are compared on the inner surface of the tube. Results demonstrate that when the forward flow deviation reaches 15%, the temperature of the working medium in the tube rises and then drops under various loads, which affects the overall economics of the boiler. As a result, it is recommended that the actual forward flow deviation be controlled within 13%. Studies have shown that when the negative flow deviation in the water wall is more than 10%, the wall temperature of the water wall will rise rapidly, significantly affecting the safety of the boiler. © 2022, The Authors. All rights reserved.

关键词： Two phase flow

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A Liver Segmentation Algorithm with Interactive Error Correction for Abdominal CT Images: A Preliminary Study 4th

A Liver Segmentation Algorithm with Interactive Error Correc...

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4th IFIP TC 12 International Conference on Computational Intelligence in Data science, ICCIDS 2021

作者： Nayantara, P. Vaidehi Kamath, Surekha Manjunath, K.N. Rajagopal, K.V. Department of Instrumentation and Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Radiodiagnosis and Imaging Kasturba Medical College Manipal Academy of Higher Education Karnataka Manipal576104 India

ISBN: (纸本)9783030925994

An automatic method for segmenting the liver from the portal venous phase of abdominal CT images using the K-Means clustering method is described in this paper. We have incorporated an interactive technique for correcting the errors in the liver segmentation results using power law transformation. The proposed method was validated on abdominal CT volumes of fifteen patients obtained from Kasturba Medical college, Manipal. The average values of the various standard evaluation metrics obtained are as follows: Dice coefficient = 0.9361, Jaccard index = 0.8805, volumetric overlap error = 0.1195, absolute volume difference = 4.048%, average symmetric surface distance = 1.7282 mm and maximum symmetric surface distance = 38.039 mm. The quantitative and qualitative results obtained in our preliminary work show that the K-Means clustering technique along with power law transformation is effective in producing good liver segmentation outputs. As future work, we will attempt to automate the power law transformation technique. © 2021, IFIP International Federation for Information Processing.

关键词： K-means clustering

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Secure and Privacy-preserving Data-sharing Framework based on Blockchain Technology for Al-Najaf/Iraq Oil Refinery

Secure and Privacy-preserving Data-sharing Framework based o...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Samir M. Umran SongFeng Lu Zaid Ameen Abduljabbar Zhi Lu Bingyan Feng Lu Zheng Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Ministry of Industry and Minerals Iraqi Cement State Company Baghdad Iraq Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China College of Education for Pure Sciences University of Basrah Technical Computer Engineering Department Al-Kunooze University College Basrah Iraq School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Wuhan Huazhong Numerical Control Co. Ltd Industrial Internet Research Institute Wuhan China College of Computer Science South-Central University for Nationalities Wuhan China

The Industrial Internet of Things or Industry 4.0 efficiently enhances the manufacturing process in terms of raising productivity, system performance, cost reduction, and building large-scale systems. It enables the connection of numerous heterogeneous devices and sensors into the internet network through the utilization of revolutionary techniques that transfer the manufacturing paradigm to intelligent processes. The traditional industrial internet of things adopts centralized architectures and complex encryption algorithms that suffer from several cyber-security threats and lead to data leakage, privacy disclosure, and high computational and communicational costs. In our work, we proposed a decentralized, efficient, low-power, scalable, secure, privacy-preserving, and trusting architecture based on a private blockchain network/smart contract and interplanetary file system technology for the Al-NajafyIraq oil refinery factory. That enables autonomous working and provides a high level of security authentication and privacy preservation, P2P communication, remote access, immutability and information backtracking. Traditional blockchain technology has storage limitation issues, most recent works adopted external servers or the cloud for storage purposes. Our proposed scheme addresses the limitations of blockchain data storage by adopting an interplanetary file system (IPFS) network; provides an efficient data encryption mechanism at the perceptual layer by adopting a lightweight encryption algorithm, high-performance, and ultra-low-power consumption ARM Cortex-M microcontroller. In addition, our architecture succeeds in merging blockchain technology, IoTs, and an IPFS with the oil refinery as an industrial application area. Moreover, it provides an efficient framework that resists common cyber-security attacks and realizes cyber-security requirements represented by data availability, integrity, and confidentiality.

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DHGT-DTI: Advancing Drug-Target Interaction Prediction through a Dual-View Heterogeneous Network with GraphSAGE and Graph Transformer

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Journal of Pharmaceutical Analysis 2025年

作者： Mengdi Wang Xiujuan Lei Ling Guo Ming Chen Yi Pan School of Computer Science Shaanxi Normal University Xi’an 710119 China College of Life Sciences Shaanxi Normal University Xi’an 710119 China College of Information Science and Engineering Hunan Normal University Changsha 410081 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen 518055 China

Computational approaches for predicting drug-target interactions (DTIs) are pivotal in advancing drug discovery. Current methodologies leveraging heterogeneous networks often fall short in fully integrating both local and global network information. To comprehensively consider network information, we propose DHGT-DTI, a novel deep learning-based approach for DTI prediction. Specifically, we capture the local and global structural information of the network from both neighborhood and meta-path perspectives. In the neighborhood perspective, we employ a heterogeneous graph neural network, which extends Graph Sample and Aggregate (GraphSAGE) to handle diverse node and edge types, effectively learning local network structures. In the meta-path perspective, we introduce a Graph Transformer with residual connections to model higher-order relationships defined by meta-paths, such as "drug-disease-drug", and use an attention mechanism to fuse information across multiple meta-paths. The learned features from these dual perspectives are synergistically integrated for DTI prediction via a matrix decomposition method. Furthermore, DHGT-DTI reconstructs not only the DTI network but also auxiliary networks to bolster prediction accuracy. The experimental results demonstrate that DHGT-DTI achieves AUC values of 0.9603 and 0.9735 on Luo's dataset and Zeng's dataset, respectively, outperforming the baseline methods. Additionally, case studies on six drugs used to treat Parkinson's disease not only validate the practical utility of DHGT-DTI but also highlight its broader potential in accelerating drug discovery for other diseases.

关键词： Drug-target interactions (DTI) Graph Transformer Graph Sample and Aggregate (GraphSAGE) Heterogeneous network

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Communication and control in Collaborative UAVs: Recent Advances and Future Trends

arXiv

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arXiv 2023年

作者： Javaid, Shumaila Saeed, Nasir Qadir, Zakria Fahim, Hamza He, Bin Song, Houbing Bilal, Muhammad The Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201804 China Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China Al Ain15551 United Arab Emirates The School of Engineering Design and Built Environment Western Sydney University Locked Bag 1797 PenrithNSW2751 Australia Embry-Riddle Aeronautical University Daytona BeachFL32114 United States The Department of Computer Engineering Hankuk University of Foreign Studies Gyeonggido Yongin-si17035 Korea Republic of

The recent progress in unmanned aerial vehicles (UAV) technology has significantly advanced UAV-based applications for military, civil, and commercial domains. Nevertheless, the challenges of establishing high-speed communication links, flexible control strategies, and developing efficient collaborative decision-making algorithms for a swarm of UAVs limit their autonomy, robustness, and reliability. Thus, a growing focus has been witnessed on collaborative communication to allow a swarm of UAVs to coordinate and communicate autonomously for the cooperative completion of tasks in a short time with improved efficiency and reliability. This work presents a comprehensive review of collaborative communication in a multi-UAV system. We thoroughly discuss the characteristics of intelligent UAVs and their communication and control requirements for autonomous collaboration and coordination. Moreover, we review various UAV collaboration tasks, summarize the applications of UAV swarm networks for dense urban environments and present the use case scenarios to highlight the current developments of UAV-based applications in various domains. Finally, we identify several exciting future research direction that needs attention for advancing the research in collaborative UAVs. © 2023, CC BY.

关键词： Unmanned aerial vehicles (UAV)

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High Altitude Platforms-Assisted Hierarchical Computing Offloading in Marine-IoT Networks: A Delay Minimization Approach

High Altitude Platforms-Assisted Hierarchical Computing Offl...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Mingqing Li Li Ping Qian Qianru Wang Yuan Wu Bin Lin Xiaoniu Yang College of Information Engineering Zhejiang University of Technology Hangzhou Zhejiang China Zhuhai UM Science and Technology Research Institute Zhuhai China Department of Computer and Information Science University of Macau Macao SAR China State Key Laboratory of Internet of Things for Smart City University of Macau Macao SAR China Department of Communication Engineering Dalian Maritime University Dalian China Science and Technology on Communication Information Security Control Laboratory Jiaxing China Institute of Cyberspace Security Zhejiang University of Technology Hangzhou China

Mobile edge computing has been a promising technology that enables diverse applications of computation-intensive yet latency-sensitive in marine Internet of Things networks. In this paper, we propose a framework of hierarchical computing offloading with the assistance of high altitude platforms (HAPs), and a hybrid transmission scheme of non-orthogonal multiple access (NOMA) and frequency division multiple access (FDMA) is designed for achieving efficient computation offloading. Specifically, the offshore sensing devices (SDs) initially perform computation offloading to the HAPs by forming NOMA groups, and the HAPs further offload partial workload to the onshore base station (BS) via FDMA. For efficient calculation, we aim to minimize the overall delay in completing all the workload processing of these SDs by jointly optimizing the durations of NOMA and FDMA transmission as well as the hierarchical computation offloading workload. Though the problem is in the form of non-convexity, we design an efficient SCA-based algorithm to tackle it. Finally, numerical results demonstrate the optimality and convergence of the proposed algorithm, as well as the performance gains of the proposed scheme.

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Constrained Optimization with Decision-Dependent Distributions

arXiv

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arXiv 2023年

作者： Wang, Zifan Liu, Changxin Parisini, Thomas Zavlanos, Michael M. Johansson, Karl H. Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures StockholmSE-10044 Sweden The Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom The Department of Engineering and Architecture University of Trieste Trieste34127 Italy The KIOS Research and Innovation Center of Excellence University of Cyprus NicosiaCY-1678 Cyprus The Department of Mechanical Engineering and Materials Science Duke University DurhamNC United States

In this paper we deal with stochastic optimization problems where the data distributions change in response to the decision variables. Traditionally, the study of optimization problems with decision-dependent distributions has assumed either the absence of constraints or fixed constraints. This work considers a more general setting where the constraints can also dynamically adjust in response to changes in the decision variables. Specifically, we consider linear constraints and analyze the effect of decision-dependent distributions in both the objective function and constraints. Firstly, we establish a sufficient condition for the existence of a constrained equilibrium point, at which the distributions remain invariant under retraining. Morevoer, we propose and analyze two algorithms: repeated constrained optimization and repeated dual ascent. For each algorithm, we provide sufficient conditions for convergence to the constrained equilibrium point. Furthermore, we explore the relationship between the equilibrium point and the optimal point for the constrained decision-dependent optimization problem. Notably, our results encompass previous findings as special cases when the constraints remain fixed. To show the effectiveness of our theoretical analysis, we provide numerical experiments on both a market problem and a dynamic pricing problem for parking based on real-world data. Copyright © 2023, The Authors. All rights reserved.

关键词： Constrained optimization

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