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The detection of mental health conditions by incorporating external knowledge

Journal of Intelligent Information Systems 2023年第2期61卷 497-518页

作者： Lin, Yun Sheng Tai, Liang Kuang Chen, Arbee L.P. Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering Asia University Taichung Taiwan

Mental health conditions have become a growing problem;it increases the likelihood of premature death for patients, and imposes a high economic burden on the world. However, some studies have shown that if patients are detected and treated early, the social impact and economic costs of mental illness can be reduced. With the popularity of social media, people are sharing their feelings on it, which allows data from social media to be used to study mental health conditions. However, past research had been limited to the optimization of the model or using different types of data available on social media, resulting in models that only rely on data to make decisions. Moreover, people judge things not only by the data collected, but also by background knowledge. Therefore, we considered the diagnostic process of doctors and combined the knowledge of psychological screening tools and diagnostic criteria into the model. In addition, we also tested the effect of combining general knowledge. We retrieve the top m most relevant knowledge segments for each user’s post, and then put both into the prediction model. Experimental results show that our method outperforms previous studies, and the F1-score is increased more than 10% in some situations. Moreover, because the knowledge segments are automatically retrieved, our method does not require additional manual labeling, and the knowledge set can be freely adjusted. These show that our method can help detect mental health conditions and can be continuously optimized in practice. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Diseases

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Performance Analysis of Machine Learning Based On Optimized Feature Selection for Type II Diabetes Mellitus

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Multimedia Tools and Applications 2025年第8期84卷 4945-4964页

作者： Bhat, Salliah Shafi Ansari, Gufran Ahmad Ansari, Mohd Dilshad B. S. Abdur Rahman Crescent Institute of Science and Technology Chennai48 India Department of Computer Science and Software Engineering University of Hail Hail55424 Saudi Arabia Department of Computer Science & Engineering SRM University Delhi-NCR Harayana Sonepat131029 India

Type 2 Diabetes Mellitus (T2DM) is a common chronic illness caused by variations in the secretion of insulin. T2DM will be treated early with the help of an early diagnosis reducing the risk of early death and controlling is course of illness. In this Paper Machine Learning Algorithms (MLA) for T2DM diagnosis are proposed. The major objective of this research is to assess use of MLA and its methods to forecast different illnesses at an initial stage. In this research, we examine most recent proposed strategies with some limitations and potential improvements for future work. The data is collected from Kaggle (University of California) *. Researchers highlight how important is to identify important features that enhance outcomes suggested by recent methods. Six MLA were used in this paper such as Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF) and K-Nearest Neighbour (KNN) were applied in Jupiter Notebook. Feature sets are selected using three feature selection methodologies such as information gain-based selection, correlation-based selection and sequential feature selection. On these feature subsets a variety of MLA are applied and optimal feature subset is chosen based on their performance. Finally a DT is suggested as the top model among the top six performances. The suggested method provides an improved performance label with an average accuracy of 96.10%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature Selection

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A Global Medical data Security and Privacy Preserving Standards Identification Framework for Electronic Healthcare Consumers

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 4379-4387页

作者： Mishra, Vinaytosh Gupta, Kishu Saxena, Deepika Singh, Ashutosh Kumar Thumbay Institute for Ai in Healthcare Gulf Medical University Ajman United Arab Emirates National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804 Taiwan The University of Aizu School of Computer Science and Engineering Aizuwakamatsu965 Japan University of Economics and Human Sciences Department of Computer Science Warsaw01-043 Poland Indian Institute of Information Technology Bhopal Department of Computer Science and Engineering Bhopal462003 India

Electronic Health Records (EHR) are crucial for the success of digital healthcare, with a focus on putting consumers at the center of this transformation. However, the digitalization of healthcare records brings along security and privacy risks for personal data. The major concern is that different countries have varying standards for the security and privacy of medical data. This paper proposed a novel and comprehensive framework to standardize these rules globally, bringing them together on a common platform. To support this proposal, the study reviews existing literature to understand the research interest in this issue. It also examines six key laws and standards related to security and privacy, identifying twenty concepts. The proposed framework utilized K-means clustering to categorize these concepts and identify five key factors. Finally, an Ordinal Priority Approach is applied to determine the preferred implementation of these factors in the context of EHRs. The proposed study provides a descriptive then prescriptive framework for the implementation of privacy and security in the context of electronic health records. Therefore, the findings of the proposed framework are useful for professionals and policymakers in improving the security and privacy associated with EHRs. © 1975-2011 IEEE.

关键词： K-means clustering

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Challenges and opportunities to Enhance Buffer Management for Disaster Area in Delay Tolerant Network: A Review with Performance Analysis

Challenges and opportunities to Enhance Buffer Management fo...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Prasad, Ajay Gurung, Sandeep Sharma, Kalpana Centre for Distance and Online Education Department of Computer Science and Engineering India Department of Computer Science and Engineering Pakyong India

ISBN: (纸本)9798350378092

A form of network called a delay-tolerant network (DTN) enables wireless nodes to communicate in settings where constant end-to-end connectivity is not ensured. In-depth discussion of buffer management inside DTNs is provided in this abstract, with an emphasis on how it might be used in disaster-prone areas. The study examines the difficulties and possibilities of enhancing buffer management techniques, particularly by adding location awareness. This research provides important insights into the performance of buffer management strategies within DTNs, particularly in disaster scenarios, by a thorough analysis of the available literature. This analysis clarifies the complex buffer management environment in DTNs by combining the results of several studies, highlighting its critical function in guaranteeing successful communication in emergency situations. The abstract emphasizes how crucial it is to use location-based information to enhance buffer management strategies. This study offers a performance analysis that aids in the creation of more reliable and effective DTN protocols for disaster management in addition to giving an overview of the present status of the field. © 2024 IEEE.

关键词： Delay tolerant networks

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Hybrid Mobile Cloud Computing Architecture with Load Balancing for Healthcare Systems

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computers, Materials & Continua 2023年第1期74卷 435-452页

作者： Ahyoung Lee Jui Mhatre Rupak Kumar Das Min Hong Department of Computer Science Kennesaw State UniversityMariettaGAUSA Department of Computer Science University of Minnesota DuluthDuluthMNUSA Department of Computer Software Engineering Soonchunhyang UniversityAsanKorea

Healthcare is a fundamental part of every individual’s *** healthcare industry is developing very rapidly with the help of advanced *** researchers are trying to build cloud-based healthcare applications that can be accessed by healthcare professionals from their premises,as well as by patients from their mobile devices through communication *** systems promote reliable and remote interactions between patients and healthcare ***,there are several limitations to these innovative cloud computing-based systems,namely network availability,latency,battery life and resource *** propose a hybrid mobile cloud computing(HMCC)architecture to address these ***,we also evaluate the performance of heuristic and dynamic machine learning based task scheduling and load balancing algorithms on our proposed *** compare them,to identify the strengths and weaknesses of each algorithm;and provide their comparative results,to show latency and energy consumption *** issues for cloudbased healthcare systems are discussed in detail.

关键词： Mobile cloud computing hybrid mobile cloud computing load balancing healthcare solution

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Early Diagnosis of Lung Tumors for Extending Patients’ Life Using Deep Neural Networks

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computers, Materials & Continua 2023年第7期76卷 993-1007页

作者： A.Manju R.Kaladevi Shanmugasundaram Hariharan Shih-Yu Chen Vinay Kukreja Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang Department of Computer Science and Engineering SRM Institute of Science and TechnologyRamapuramChennaiIndia Department of Computer Science and Engineering Saveetha Engineering CollegeChennaiIndia Department of Computer Science and Engineering Vardhaman College of EngineeringHyderabadIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin 64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin 64002Taiwan Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia Department of Computing Science University of AberdeenAberdeenUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh 12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh 11437Saudi Arabia School of Computer&Communication Engineering Changsha University of Science&TechnologyChangshaChina

The medical community has more concern on lung cancer *** experts’physical segmentation of lung cancers is time-consuming and needs to be *** research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning ***-Aided Diagnostic(CAD)system aids in the diagnosis and shortens the time necessary to detect the tumor *** application of Deep Neural Networks(DNN)has also been exhibited as an excellent and effective method in classification and segmentation *** research aims to separate lung cancers from images of Magnetic Resonance Imaging(MRI)with threshold *** Honey hook process categorizes lung cancer based on characteristics retrieved using several *** this principle,the work presents a solution for image compression utilizing a Deep Wave Auto-Encoder(DWAE).The combination of the two approaches significantly reduces the overall size of the feature set required for any future classification process performed using *** proposed DWAE-DNN image classifier is applied to a lung imaging dataset with Radial Basis Function(RBF)*** study reported promising results with an accuracy of 97.34%,whereas using the Decision Tree(DT)classifier has an accuracy of 94.24%.The proposed approach(DWAE-DNN)is found to classify the images with an accuracy of 98.67%,either as malignant or normal *** contrast to the accuracy requirements,the work also uses the benchmark standards like specificity,sensitivity,and precision to evaluate the efficiency of the *** is found from an investigation that the DT classifier provides the maximum performance in the DWAE-DNN depending on the network’s performance on image testing,as shown by the data acquired by the categorizers themselves.

关键词： Lung tumor deep wave auto encoder decision tree classifier deep neural networks extraction techniques

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Improved Markov Decision Process in Wireless Sensor Network for Optimal Energy Consumption 3rd

Improved Markov Decision Process in Wireless Sensor Network ...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Kalnoor, Gauri Metre, Prakash B. Department of Computer Science and Engineering BMSCE Bangalore India Department of Computer Science and Engineering Presidency University Bangalore India

ISBN: (纸本)9789819708918

The research looks at the problem of receiving a precise estimate of the sensor node attribute from the Wireless Sensor Network (WSN) in a certain time frame while using as little energy as possible from the sensors. An estimate of an attribute may be obtained by taking readings from wake-up sensors connected to a sink node that has been placed at random. The sink must take relevant measurements within a certain time constraint. As an added bonus, the sink reduces the amount of power needed for sensors to transmit their readings. The predicted energy consumption of WSN sensor nodes is presented in a closed-form formulation in this work. The research establishes a maximum allowable sensor transmission distance. The Markov Decision Process may be used to determine the timing of sensor transmissions over a given time frame (MDP). A strategy for transmitting-sensor programming is also developed in this work. The simulation is conducted in MATLAB environment, where the MDP schedule offers reduced energy consumption, reduced delay, increase packet delivery ratio, reduced false alarm rate, which finds the attribute of the data sent via a sensor node. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Energy utilization

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Natural Language Processing based classifier for identifying spam emails and SMS messages 3

Natural Language Processing based classifier for identifying...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Rahman, Habibur Chowdhury, Md Tanvir Sobhani, S. M. Sabbir Nabil, Mehedi Hasan Sumon, Monjurul Islam Reza, Ahmed Wasif Emon, Md Yousuf East West University Department of Computer Science and Engineering Dhaka Bangladesh Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350384598

this research explores the development of a sophisticated classifier for screening spam emails and communications using Natural Language Processing (NLP). In today's digital age, where electronic communications continue to pour in, discriminating between beneficial and dangerous content is becoming increasingly important. The classifier uses NLP to analyze the intricacies of language used in Email/SMS and finds spam patterns. The classifier is trained on a dataset of both spam and non-spam messages using machine learning techniques, allowing it to reliably distinguish and categorize emails and SMS. This implementation's highest result for the Naïve Bayes Classifier is Bernoulli 0.98 percent. The goal is to improve the user experience by limiting exposure to irrelevant or potentially harmful content while assuring the seamless delivery of real communications. © 2024 IEEE.

关键词： Adversarial machine learning

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DNA Computing with Water Strider Based Vector Quantization for data Storage Systems

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computers, Materials & Continua 2023年第3期74卷 6429-6444页

作者： A.Arokiaraj Jovith S.Rama Sree Gudikandhula Narasimha Rao K.Vijaya Kumar Woong Cho Gyanendra Prasad Joshi Sung Won Kim Department of Networking and Communications College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science Engineering Aditya Engineering CollegeSurampalemAndhra Pradesh533437India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering Vignan’s Institute of Engineering for WomenVisakhapatnam530049India Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

The exponential growth of data necessitates an effective data storage scheme,which helps to effectively manage the large quantity of *** accomplish this,Deoxyribonucleic Acid(DNA)digital data storage process can be employed,which encodes and decodes binary data to and from synthesized strands of *** quantization(VQ)is a commonly employed scheme for image compression and the optimal codebook generation is an effective process to reach maximum compression *** article introduces a newDNAComputingwithWater StriderAlgorithm based Vector Quantization(DNAC-WSAVQ)technique for data Storage *** proposed DNAC-WSAVQ technique enables encoding data using DNA computing and then compresses it for effective data ***,the DNAC-WSAVQ model initially performsDNA encoding on the input images to generate a binary encoded *** addition,aWater Strider algorithm with Linde-Buzo-Gray(WSA-LBG)model is applied for the compression process and thereby storage area can be considerably *** order to generate optimal codebook for LBG,the WSA is applied to *** performance validation of the DNAC-WSAVQ model is carried out and the results are inspected under several *** comparative study highlighted the improved outcomes of the DNAC-WSAVQ model over the existing methods.

关键词： DNA computing data storage image compression vector quantization ws algorithm space saving

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Enhanced Predictive Modeling Techniques for Early Detection of COPD Utilizing 1D Convolutional Neural Networks 15

Enhanced Predictive Modeling Techniques for Early Detection ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Jha, Sneha Sahu, Prakash Kumar, Santosh CSVTU Dept. of Data Science Chhattisgarh India IIIT-Naya Computer Science & Engineering Raipur India

ISBN: (纸本)9798350370249

Artificial intelligence (AI) breakthroughs have created new opportunities in the field of medical diagnostics, especially for the early identification of respiratory conditions like Chronic Obstructive Pulmonary Disease (COPD), the most prevalent respiratory condition, is characterized by breathing difficulties, mucus (sputum) coughing, and wheezing. Early verification is critical for efficient administration. A one-dimensional convolutional neural network (1D CNN) model for COPD detection, optimized using Adam and RMSprop algorithms is proposed for this purpose. Utilizing the ICBHI 2017 dataset, the performance of each optimizer based on training and validation accuracy is evaluated. The Adam optimizer achieved a training accuracy of 9 4% and a validation accuracy of 9 0%. In comparison, the RMSprop optimizer yielded a training accuracy of 9 2% and a validation accuracy of 8 8%. These results demonstrated that the Adam optimizer surpassed RMSprop in terms of accuracy. It showcased the potential of the proposed 1D CNN model as a reliable diagnostic tool, contributing to the advancement of respiratory health analytics. © 2024 IEEE.

关键词： Convolutional neural networks

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