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27th International Conference on Knowledge Based and Intelligent information and Engineering Sytems, KES 2023

作者： Nguyen, Thuy Trinh Pham, Viet Hoang-Quoc Le, Duc-Trong Vu, Xuan-Son Deligianni, Fani Nguyen, Hoang D. School of Computing Science University of Glasgow United Kingdom University of Engineering and Technology Vietnam National University Viet Nam Department of Computing Science Umeå University Sweden School of Computer Science and Information Technology University College Cork Ireland

Recent advancements in machine learning and multimedia technologies have paved new ways for automatic medical diagnosis. In mental health, multimodal inputs such as visual and audible sensing data are promising to investigate the underlying mechanisms of many conditions, such as depression and bipolar disorders. With the increasing burden on healthcare systems, timely diagnosis of mental diseases using multiple modalities might benefit millions of people worldwide. This scoping review provides an exploratory overview of recent multimodal machine learning approaches for mental disorder screening. We also discuss a generalised end-to-end multimodal machine learning pipeline for future research and development of multimodal disease detection. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： bipolar disorders depression mental disorder diagnosis Multimodal machine learning stress disorders

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Bibliometric Review of Diabetes Prediction with Machine Learning Methods 2

Bibliometric Review of Diabetes Prediction with Machine Lear...

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2nd International Conference on Informatics, ICI 2023

作者： Jain, Vipin Yadav, Anil Kumar Singh Rajpoot, Dharmveer Prasad Shukla, Shiv Shankar VIT Bhopal University School of Computing Science and Engineering India Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9798350343830

Diabetes is a serious health condition that can be fatal if left untreated. Artificial intelligence is being used to great effect in the healthcare industry, and diabetes prediction is a rapidly growing research area. Early detection of diabetes can save lives, and numerous publications have been published in recent years. New data and methods are constantly being developed, so diabetes prediction remains an active research topic. In this study, trends in publications, institutions, countries, and authors have been analyzed to provide a comprehensive view of the existing state of diabetes prediction research. © 2023 IEEE.

关键词： Machine learning

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Design of IoT Based VPN Gateway for Home Network

Design of IoT Based VPN Gateway for Home Network

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2022 International Conference on Electronics and Renewable Systems, ICEARS 2022

作者： Raj, Jeberson Retna Srinivasulu, Senduru Sathyabama Institute of Science and Technology School of Computing Department of Information Technology Chennai India

ISBN: (纸本)9781665484251

With the rapid development of technology, individuals can keep with their handy devices like notebooks, tablet computers, and smart cell phones for doing their personal and professional works regardless of the place and location. In addition, they have the potential to connect people to many services on the market to gain access. Security is one of the issues with the user accessing the data through the connected network. In essence, the public network is more vulnerable and the information transferred to systems is not properly encrypted. Furthermore, the network is accessible to anyone those who have the understanding of the computer network. The intruders may use the public network to plough the freedom users from surfing internet by blocking with their websites or advertisements. A Virtual Private Network (VPN) is a network that uses their encryption mechanism to link two different networks together using public telecommunications and to providing services through World Wide Web. In this paper, a prototype model is designed for VPN gateway connection using Raspberry Pi which in turn to connect the house network with the ISP network. Through this a VPN network services like security and scalability is guaranteed to the home systems using the proposed prototype. © 2022 IEEE.

关键词： Virtual private networks

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An Efficient Distributed Energy and Consumption Method for Ensuring Wireless Sensor Network (WSN) Coverage Using the Firefly Algorithm 4th

An Efficient Distributed Energy and Consumption Method for E...

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4th International Conference on Innovative Product Design and Intelligent Manufacturing System, IPDIMS 2022

作者： Uma, K. Ramesh Kumar, C. Shanmugam, Thirumurugan School of Information Technology Vellore Institute of Technology Tamil Nadu Vellore India School of Computing Science and Engineering Galgotias University UP Greater Noida India Department of Information Technology University of Technology and Applied Sciences Suhar Muscat Oman

ISBN: (纸本)9789819916641

In this article, we are talking about the wireless sensor network sub-field of a body area sensor network. The establishment of the wireless body area sensor network has taken place after the refinement of the wireless sensor network was underway. We have introduced an innovative energy-efficient routing system that incorporates a fuzzy logic also bio-inspired firefly algorithm, which considers these limitations. Together, our proposal of correlation region selection then bio-stimulated firefly algorithm centered NBSs use fuzzy logic-based correlations to minimize energy transmission, while our joint firefly NBS proposal makes a valuable contribution by cutting energy consumption. To identify and select enriched regions that contain high energy and detect information, our suggested fuzzy logic-based correlation region selection mechanism is configured to favor those regions. We, however, suggested a bio-stimulated firefly algorithm fitness function for the collection of NBSs. To prevent exhaustion of valuable transmission, the fitness function accounts for the diffusion antiquity and residual energy for NBSs. To confirm the in-depth influence of our suggested scheme, we deportment numerous simulations exhausting the Nano-SIM tool to measure the various aspects involved in conserving energy resources, sinking endwise deferment, and increasing packet delivery ratio. We found that by using an optimized selection for correlated regions besides NBSs in refining network lifespan and packet delivery ratio although dipping the typical endwise delay, our proposed system accomplishes the significant task of determining which regions and NBSs are more important to the network and allows for the lower end-to-end delay. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fuzzy logic

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ASSESSING THE ACCURACY AND ETHICAL IMPLICATIONS OF AUTOMATED CRIMINAL DETECTION USING A DEEP LEARNING APPROACH

ASSESSING THE ACCURACY AND ETHICAL IMPLICATIONS OF AUTOMATED...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Jagadeesan, S. Sujigarasharma, K. Manjula, R. Vetriselvi, T. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India Department of IOT School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350305708

This study examines the accuracy and ethical implications of using convolutional neural networks (CNN) for automated crime detection. A CNN model was trained on a dataset of criminal mugshots to identify potential criminal behaviour based on facial features. This study analyzed the performance of the model and achieved a high accuracy rate in identifying criminals. However, the ethical implications of automated criminal detection are also explored, including bias, privacy and human rights violations. The findings of this study highlight the need for caution and ethical considerations when implementing automated crime detection technologies. It is important to ensure that such technologies are not used to violate the rights of individuals or perpetuate societal biases. © 2023 IEEE.

关键词： CNN Criminal detection Face detection Machine learning

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Exploring swarm intelligence optimization techniques for task scheduling in cloud computing: algorithms, performance analysis, and future prospects

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Iran Journal of Computer science 2024年第2期7卷 337-358页

作者： Prity, Farida Siddiqi Uddin, K. M. Aslam Nath, Nishu Department of Information and Communication Engineering Noakhali Science and Technology University Noakhali 3814 Bangladesh Gianforte School of Computing Montana State University Bozeman MT United States

The advent of the cloud computing paradigm has enabled innumerable organizations to seamlessly migrate, compute, and host their applications within the cloud environment, affording them facile access to a broad spectrum of services with minimal exertion. A proficient and adaptable task scheduler is essential to manage simultaneous user requests for diverse cloud services using various heterogeneous and varied resources. Inadequate scheduling may result in issues related to either under-utilization or over-utilization of resources, potentially causing a waste of cloud resources or a decline in service performance. Swarm intelligence meta-heuristics optimization technique has evinced conspicuous efficacy in tackling the intricacies of scheduling difficulties. Thus, the present manuscript seeks to undertake an exhaustive review of swarm intelligence optimization techniques deployed in the task-scheduling domain within cloud computing. This paper examines various swarm-based algorithms, investigates their application to task scheduling in cloud environments, and provides a comparative analysis of the discussed algorithms based on various performance metrics. This study also compares different simulation tools for these algorithms, highlighting challenges and proposing potential future research directions in this field. This review paper aims to shed light on the state-of-the-art swarm-based algorithms for task scheduling in cloud computing, showing their potential to improve resource allocation, enhance system performance, and efficiently utilize cloud resources. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Cloud computing Meta-heuristics Swarm intelligence Task scheduling

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Predictive Intelligence Enhanced Fuzzy Model for Underwater Network Optimization 5th

Predictive Intelligence Enhanced Fuzzy Model for Underwater ...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Praveena, K.N. Mishra, Awakash Kamalraj, R. Belwal, S. Ankit School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Information Technology Jain University Karnataka Bangalore India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Manduwala Dehradun Navgaon India

ISBN: (纸本)9789819780426

This paper gives a predictive intelligence-enhanced fuzzy version for underwater network optimization. It combines predictive intelligence technology with fuzzy common sense, which can lessen the functionality gap of underwater community optimization. This proposed version is implemented in an underwater cooperative tracking community. With the proposed model, objectives can be tracked with sensors within the network, and their national statistics can also be appropriately anticipated. Similarly, the version has excessive scalability and robustness because of the mixing of predictive intelligence. In the end, simulation experiments suggest that the proposed model correctly improves the precision of tracking challenges and improves the scalability of the network. The Predictive Intelligence superior Fuzzy model (PIE-FUZMO) for underwater network optimization is a singular method developed to cope with the complex optimization issues confronted through underwater networks. The PIE-FUZMO uses a couple of laptops with imaginative and prescient sample recognition techniques and fuzzy good judgment to enhance the underwater community performance while decreasing the running fee. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Scalability

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Unlocking the Power of Long Short-Term Memory Networks: A Text Classification Approach 14

Unlocking the Power of Long Short-Term Memory Networks: A Te...

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14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Pandiaraj, A. Ramshankar, N. Venkatesan, R. SRM Institute of Science and Technology Computing Technologies School of Computing Chennai India RMD.Engineering College Computer Science and Engineering Tamilnadu Chennai India Bannari Amman Institute of Technology Information Technology Tamilnadu Erode India

ISBN: (纸本)9798350335095

Text classification typically suffers from the fundamental issue of latter-day data with a very high dimension, which hinders the model developed because training time grows exponentially with the number of features used and model risk of overfitting increases with increasing number of features. Since the recurrent structure is ideal for processing long varied texts, recurrent neural networks (RNNs) are among the most often used designs in natural language processing (NLP). RNN using the Long Short-Term Memory (LSTM) architecture is one of the deep learning techniques suggested in this study. In natural language processing for English, one element stores the subject's gender, while another element records whether the subject is singular or plural. Long Short-Term Memory (LSTM) supplies elements that are intended to be able to record an input characteristic. LSTM will discover these properties throughout the training procedure. The results of this study are most accurate when variable word sequence characteristics are taken into account. © 2023 IEEE.

关键词： Long short-term memory

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Causality Based Front-door Defense Against Backdoor Attack on Language Models 41

Causality Based Front-door Defense Against Backdoor Attack o...

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41st International Conference on Machine Learning, ICML 2024

作者： Liu, Yiran Xu, Xiaoang Hou, Zhiyi Yu, Yang Institute for Interdisciplinary Information Sciences Tsinghua University Beijing China School of Computer Science and Technology Harbin University of Science and Technology Harbin China Faculty of Computing Harbin Institute of Technology Harbin China School of Economics and Management China University of Petroleum Beijing China

We have developed a new framework based on the theory of causal inference to protect language models against backdoor attacks. Backdoor attackers can poison language models with different types of triggers, such as words, sentences, grammar, and style, enabling them to selectively modify the decision-making of the victim model. However, existing defense approaches are only effective when the backdoor attack form meets specific assumptions, making it difficult to counter diverse backdoor attacks. We propose a new defense framework Front-door Adjustment for Backdoor Elimination (FABE) based on causal reasoning that does not rely on assumptions about the form of triggers. This method effectively differentiates between spurious and legitimate associations by creating a'front door' that maps out the actual causal relationships. The term'front door' refers to a text that retains the semantic equivalence of the initial input, which is generated by an additional, fine-tuned language model, denoted as the defense model. Our defense experiments against various attack methods at the token, sentence, and syntactic levels reduced the attack success rate from 93.63% to 15.12%, improving the defense effect by 2.91 times compared to the best baseline result of 66.61%, achieving state-of-the-art results. Through ablation study analysis, we analyzed the effect of each module in FABE, demonstrating the importance of complying with the front-door criterion and front-door adjustment formula, which also explains why previous methods failed. Our code to reproduce the experiments is available at: https://***/lyr17/FrontdoorAdjustment-Backdoor-Elimination. Copyright 2024 by the author(s)

关键词： Semantics

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Deep Learning for MRI-Based Brain Tumour Identification and Classification 1

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8th International Conference on Data Management, Analytics and Innovation, ICDMAI 2024

作者： Jareena Begum, D. Chokkalingam, S.P. Sundaravadivazhagan, B. Department of Computer Science and Engineering Amrita School of Computing Chennai Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering Amrita School of Computing Chennai Amrita Vishwa Vidyapeetham Chennai India Department of Information Technology University of Technology and Applied Sciences University of Technology and Applied Sciences Al Mussana Oman

ISBN: (数字)9789819732456

ISBN: (纸本)9789819732449

Radiology tumour spotting is complicated and requires medical knowledge. Thus, a lack of doctors should not delay cancer detection programmes. Biomedical image processing software helps find brain tumours in MRI data. This study segmented and detected brain tumours using MRI sequence images. This process is complicated by the similarity of normal tissues and the wide range of tumour tissues in different patients. Brain tumour detection is the main goal. Brain tumour diagnosis requires precise tumour size and position. This paper presents a deep learning-based brain tumour MRI segmentation and classification method. We preprocessed the photos with the Gaussian blur filter and image enhancement tool. Binary thresholding segments. Morphological methods reveal traits. CNN will assess brain MRI regularity. This study uses the Kaggle Dataset. We train with 255 brain MRIs, 155 with tumours and 98 normal, and achieve 97% accuracy. We test the model using Kaggle brain MR datasets. Brain tumour detection, segmentation, and categorization;medical image processing. This study proposed an easy-to-implement way for identifying and classifying bone tumours. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Tumors

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