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Enhancing Mental Health Assessment Through Fuzzy System 16th

Enhancing Mental Health Assessment Through Fuzzy System

16th International Conference on Application of Fuzzy Systems, Soft Computing and Artificial Intelligence Tools, ICAFS 2023

作者： Xian, Ban Li Ahmad, Sharifah Sakinah Syed Yusof, Noor Fazilla Abd Borah, Samarjeet Faculty of Information Communication Technology Universiti Teknikal Malaysia Melaka Melaka Malaysia Department of Computer Applications SMIT Sikkim Manipal University Sikkim India

ISBN: (纸本)9783031762826

During the occurrence of the coronavirus disease (COVID-19), the healthcare system especially mental health is becoming more demanding and beneficial. Besides, Sustainable Development Goal 3 can be achieved as well. Mental Illness Diagnosis Fuzzy System (MIDFS) is built from a fuzzy system to identify the severity level of overall mental health in terms of stress, anxiety, and depression. The fuzzy model consists of fuzzification, inference engine, knowledge base, and defuzzification. It predominantly aids in supporting the traditional mental disorder diagnosis or psychology by accurately classifying mental disorders from confusing symptoms. On top of that, fuzzy logic has been developed in real-life applications. This system has potential as the trend is met since most people feel stressed to face numerous troubles in terms of health, financial, social, or other concerns simultaneously. Apart from that, several people might feel frightened or insecure and query the accuracy of the doctor’s diagnosis. The main goal and its modules are to develop a fuzzy diagnosis model from Depression Anxiety and Stress Scale 21 (DASS-21) with features of a 24/7 chatbot and a dashboard. There will be two major targeted users which are admin and end-users. To recapitulate, this system can help to improve the diagnosis’s accuracy. As a result, the efficiency and effectiveness of the result will rise. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Fuzzy systems

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IDT-Cascade: a novel information dissemination tree model for influential cascade detection in online social networks

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International Journal of information technology (Singapore) 2025年 1-11页

作者： Ganai, Aaquib Hussain Hashmy, Rana Khanday, Hilal Ahmad Bhutani, Monica Department of Computer Applications Mohan Babu University Andhra Pradesh Tirupati India Information Technology Department Central University of Kashmir Ganderbal India Department of Computer Science University of Kashmir Srinagar India Bharati Vidyapeeth’s College of Engineering New Delhi India

Social media are valuable tools for content generation, knowledge exchange, and interaction. Therefore, discovering influential users or seed nodes is critical or important when it comes to enhancing information diffusion in OSNs. In this paper, we propose the IDT-Cascade model, a way to infer distinctive cascades relying on structural characteristics, starring the height of trees of information diffusion. The model incorporates a tool in Python that identifies and analyses information cascades by using Degree centrality and Breadth-first search (BFS). Going by the information in the tables above, a threshold defined on the basis of average cascade height is suggested for declaring influential cascades. The model was then tested using real-world networks such as the Student Government, Galesburg, and Strike structure to show that it could identify significant entertainment cascades. For example, out of two cascades included in the Student Government set, we determined that two of them were influential while demonstrating the suggested model’s applicability. As a result, the proposed IDT-Cascade model is a highly efficient, computationally tractable approach to cascade detection with implications for improving communication plans in organizational and educational settings. This work contributes to the literature by presenting improvements over current methods of cascade modeling as well as offering a reliable apparatus for studying social networks. © Bharati Vidyapeeth's Institute of computer applications and Management 2025.

关键词： Cascade modelling Influential cascades information diffusion information dissemination trees Seed node detection Social network analysis Structural height parameter Target-based learning

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Three Dimensional DenseUNet with CKHA Segmentation Technique for Monkeypox Disease Prediction

Three Dimensional DenseUNet with CKHA Segmentation Technique...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Madhu, H.S. Rallapalli, Sreekanth Thatikonda, Ramya Nitte Meenakshi Institute of Technology Department of Master of Computer Applications Bengaluru India University of the Cumberlands Department of Information Technology CharlotteNC United States

ISBN: (纸本)9798350313413

Since several years ago, the monkeypox virus has been seen in Africa, where it has been connected to the emergence of skin lesions. Following the COVID-19 pandemic, lethal consequences of viral infections have caused public concern and anxiety. Given that COVID-19 has reached pandemic levels, quick detection techniques are essential. The hybrid pre-trained deep learning (DL) models are used in this study's research to identify the monkeypox virus. This method uses the Messy Krill Guide Procedure (CKHA) in conjunction with Kapur's entropy (KE) to segment images. The Remora Optimisation Algorithm (ROA) selects the Long-Short Term Memory (LSTM) weight in the classifier in the best possible way. For this, we first adjust them by adding familiar routine sheets for all of them, and then we examine the outcomes via tetrad familiar-known metrics: Accuracy, Remember, F1notch, and Correctness. With the aid of our suggested segmentation and classification strategy, the study runs its trials on a publically accessible dataset, yielding average values of 96.90% accuracy, 97.84% accuracy, 98.20% remember, and 98.67% F1-notch, respectively. These optimistic outcomes, which exceed cutting-edge techniques, imply that the suggested strategy is appropriate for mass screening by health practitioners. © 2023 IEEE.

关键词： Chaotic Krill Herd Algorithm Deep learning Kapur's entropy Long-short term memory Monkeypox virus detection Remora Optimization Algorithm

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IGHOA Based Modified Convolutional Neural Network for Prediction of Cardiovascular Disease

IGHOA Based Modified Convolutional Neural Network for Predic...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Dileep, M.R. Jaiswal, Riyansh Vishwanatha, C.R. Thatikonda, Ramya Avula, Sashikanth Reddy Nitte Meenakshi Institute of Technology Department of Master of Computer Applications Bengaluru India University of the Cumberlands Department of Information Technology CharlotteNC United States

ISBN: (纸本)9798350313413

Prediction of heart disease is more crucial for reducing mortality rates. Many other conditions can contribute to heart disease, including clogged arteries, heart attacks, chest discomfort, and strokes. Bypass surgery or coronary artery surgery is performed to correct issues with the heart's muscles, valves, or rhythm caused by heart disease. Lack of early diagnosis of heart disease in humans is a leading cause of mortality. Using a pre-processing and classification strategy, accurately forecast cardiac issues. In this study, we utilise the UCI heart disease dataset and a real-time dataset to evaluate deep learning algorithms against conventional approaches. As a first step, we examined the interplay and dependency between a variety of clinical characteristics and coronary heart disease. Finally, we use both the whole set of features and a smaller subset to conduct an in-depth examination of the Modified Convolutional Neural Network (MCNN) classification model. In order to enhance the CNN's classification capabilities, an IGHOA model is applied to it. Accuracy, the ROC curve, and the F1-Score were used to classifiers. The classification outcomes demonstrated that pertinent characteristics had a significant effect on organization precision. When compared to replicas trained on a complete feature set, the presentation of classification models trained with a smaller feature set better melodramatically with less training period. © 2023 IEEE.

关键词： Correlation dependence Deep Learning Heart Disease Improved Grasshopper optimization algorithm Modified Convolutional Neural Network

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Preserve the Medical Data using Secure Partially Homomorphic Encryption with Blockchain technology in Smart Healthcare 2

Preserve the Medical Data using Secure Partially Homomorphic...

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2nd International Conference on Data Science and information System, ICDSIS 2024

作者： Kavitha, S. Pavithra, B.S. Shashikala, A.B. Jagadeesh, B.N. Mageswari, P. Uma Marichi, Dileep Nitte Meenakshi Institute Of Technology Department Of Mca Bengaluru India East West Institute Of Technology Department Of Computer Science And Engineering Bengaluru India Rns Institute Of Technology Department Of Information Science And Engineering Bengaluru India Presidency College Department Of Computer Applications Bangalore India

ISBN: (纸本)9798350381665

Healthcare information is vital for both individuals and service providers, necessitating secure sharing and maintenance of electronic healthcare records (EHR). Traditional EHR systems have relied on centralized architectures, such as the cloud, which may expose sensitive information to undisclosed intelligence. However, these systems have struggled to address the diverse needs of stakeholders while ensuring separation, security, and compliance with legal requirements. Blockchain technology offers a decentralized and distributed ledger system, providing secure, verifiable, and immutable data transfer capabilities. By employing cryptographic techniques like hashes, blockchain eliminates the need for a central authority, making data exploitation challenging and evident. This study proposes a blockchainbased architecture utilizing the Proof of Stake (POS) consensus mechanism to authenticate user identities and facilitate secure EHR exchange across multiple healthcare systems. Additionally, to enhance data security, Secure Partially Homomorphic Encryption (SPHE) is recommended for outsourced data, enabling users to perform operations on encrypted data. The proposed technique surpasses alternatives in terms of energy efficiency, authenticity, and protection of medical data. © 2024 IEEE.

关键词： Blockchain

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Robust Imaging of Speed of Sound Using Virtual Source Transmission

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IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 2023年第10期70卷 1308-1318页

作者： Schweizer, Dieter Rau, Richard Bezek, Can Deniz Kubik-Huch, Rahel A. Goksel, Orcun Computer-Assisted Applications in Medicine Group ETH Zurich Zurich8092 Switzerland Department of Information Technology Uppsala University Uppsala751 05 Sweden Kantonsspital Baden Baden5404 Switzerland

Speed of sound (SoS) is a novel imaging biomarker for assessing the biomechanical characteristics of soft tissues. SoS imaging in the pulse-echo mode using conventional ultrasound (US) systems with hand-held transducers has the potential to enable new clinical uses. Recent work demonstrated that diverging waves (DWs) from a single element (SE) transmit to outperform plane-wave sequences. However, SE transmits have severely limited power and hence produce a low signal-to-noise ratio (SNR) in echo data. We herein propose Walsh-Hadamard (WH) coded and virtual-source (VS) transmit sequences for the improved SNR in SoS imaging. We additionally present an iterative method of estimating beamforming (BF) SoS in the medium, which otherwise confounds SoS reconstructions due to beamforming inaccuracies in the images used for reconstruction. Through numerical simulations, phantom experiments, and in vivo imaging data, we show that WH is not robust against motion, which is often unavoidable in clinical imaging scenarios. Our proposed VS sequence is shown to provide the highest SoS reconstruction performance, especially robust to motion artifacts. In phantom experiments, despite having a comparable SoS root-mean-square error (RMSE) of 17.5-18.0 m/s at rest, with a minor axial probe motion of approx 0.67 mm/s the RMSE for SE, WH, and VS already deteriorate to 20.2, 105.4, and 19.0 m/s, respectively, showing that WH produces unacceptable results, not robust to motion. In the clinical data, the high SNR and motion resilience of VS sequences are seen to yield superior contrast compared to SE and WH sequences. © 2023 IEEE.

关键词： Signal to noise ratio

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Deep Learning Based Automated Lip Reading for Deaf 3

Deep Learning Based Automated Lip Reading for Deaf

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Prathyakshini Prathwini Pratheeksha Hegde, N. Vaishali Rashmi, N. Kumar, Archana Praveen NMAM Institute of Technology Department of Information Science and Engineering Karkala Nitte India NMAM Institute of Technology Department of Master of Computer Applications Karkala Nitte India NMAM Institute of Technology Department of Computer Science and Engineering Karkala Nitte India Manipal Academy of Education Manipal Institute of Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350354171

Speech recognition systems play an integral role in numerous applications, from virtual assistants to accessibility tools. This paper offers a new viewpoint to speech recognition utilizing computer vision and deep learning techniques. The proposed system is trained on a sizable dataset comprising 700 video clips of individuals uttering predefined words, add up to approximately 3 GB of data. Leveraging TensorFlow and Keras, model architecture is designed incorporating convolutional and dense layers. The training process yielded promising results, with a training accuracy of 95.7% and a validation accuracy of 98.5%, indicative of robust classification performance. The integration of computer vision enriches the system's ability to extract meaningful features from audio-visual inputs, enhancing its overall recognition accuracy. Proposed method demonstrates significant potential for real-time speech recognition applications in different areas, like human-computer interaction and assistive technologies. © 2024 IEEE.

关键词： Assistive technology

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Federated Learning in the Era of Privacy Preservation: A Comprehensive Review and Future Directions 2

Federated Learning in the Era of Privacy Preservation: A Com...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Kulshreshtha, Kushagra Goel, Bhawna Kanyal, Hoshiyar Singh Singhal, Vikas Gla University Department of Computer Engineering & Applications UP Mathura India UP Ghaziabad India Abes Institute of Technology Department of Computer Science & Engineering UP Ghaziabad India Greater Noida Institute of Technology Department of Information Technology UP Greater Noida India

ISBN: (纸本)9798350371055

Federated studying is a disbursed system learning method that lets a set of dispensed users, or clients, collaboratively learn a shared predictive model without sharing any statistics. Through taking part in the model education method, every purchaser contributes to the development of a worldwide, predictive model without sacrificing its very own information privacy. This privateness renovation may be achieved through various techniques, which include differential privateness, cozy multi-celebration computation, and information obfuscation. In this way, model schooling isn't always a task exclusively for centralized facts creditors, but as an alternative, a large-scale collaboration amongst multiple entities with disparate hobbies and facts silos. In the technology of privacy protection, federated studying represents a powerful tool that enables the construction of predictive fashions that might be knowledgeable by means of various information resources at the same time as retaining the integrity and privateness of the individual statistics units. © 2024 IEEE.

关键词： Collaboratively Construction Federated Privateness technology

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Integrating AI into Software Engineering: A Critical Review and Future Directions

Integrating AI into Software Engineering: A Critical Review ...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Aditya Pai, H. Sharath, K.R. Babalad, Kavita Guru Prasad, M.S. Chandrappa, S. Kumar, Pratik Mit School of Computing Mit Art Design and Technology University Pune India Department of Computer Science and Engineering Dehradun India School of Computing and Information Technology Reva University Bangalore India Bengaluru India Department of Computer Applications Dehradun India

ISBN: (纸本)9798331527495

The field of Artificial Intelligence (AI) has been witnessing a huge demand in the field of research, tools development, and applications of deployment. There are multiple software companies which are shifting their focus to developing AI systems, many are also deploying the AI paradigms to their current system. Correspondingly, the academic research community has also used AI paradigms to solve the traditional software engineering problems. AI has become very useful to the software engineering community. The paper also uses a survey of the most commonplace methods of using AI into the Software Engineering, that covers the methods for SE phases such as Requirements, Design, Development, Testing, Release, and Maintenance. The paper also aims in answering to the sufficient intelligence in the SE lifecycle, this evaluates the overlapping of the SE and AI domains, and challenge the current conventional wisdom of the state-of-the-art. © 2025 IEEE.

关键词： Software testing

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Fake News Detection Using ARO and LSTM Algorithms

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SN computer Science 2025年第1期6卷 1-11页

作者： Bhagat, Amit Dangi, Dharmendra Suman, Vaibhav Dixit, Dheeraj Kumar Sharma, Suvarna Department of Computer Applications MANIT Madhya Pradesh Bhopal India Department of Information Technology IIIT Madhya Pradesh Bhopal India Department of Computer Science & Engineering LNCT University Madhya Pradesh Bhopal India Department of Computer Science & Engineering MITS Madhya Pradesh Gwalior India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

There has been an exponential increase in social media content over the past few years due to the popularity of social media platforms. However, this has also resulted in the dissemination of intentionally false information, or fake news, which can have significant negative effects on society. As a result, detecting fake news has become an important issue that requires attention. Promoting trust in the global community is crucial to preventing the spread of false information in mass media. A study report suggests a reliable technique for identifying false news in order to remedy this. The author has thoughtfully chosen two distinct datasets: the Buzzfeed dataset, which is an assortment of news articles from Buzzfeed News, and the ISOT dataset, which is a collection of social media postings and articles from other sources. The author thoroughly analyzed a variety of content kinds and assessed the efficacy of various approaches and methods for processing and categorizing them using the datasets. The study found that these datasets have accuracy rates of 99% and 97.8% respectively. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： ARO algorithm Fake news detection LSTM Pre-processing

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