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Modeling of Weight Perception Based on Subjectivity by Considering Individual Differences 13

Modeling of Weight Perception Based on Subjectivity by Consi...

引用

Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Kushida, Daisuke Nakajima, Kei Tottori University Faculty of Engineering Dept. Electrical Engineering and Computer Science Tottori Japan Graduate School of Sustainability Science Tottori University Dept. Engineering Tottori Japan

ISBN: (纸本)9798350373332

The minimum amount of perception that humans can perceive is called the stimulus threshold. This study aims to clarify the stimulus threshold of weight perception for the biceps brachii muscle based on a method of constant stimuli. In general, the stimulus threshold depends on the individual. The authors consider that physical characteristics are related to individual differences, construct a model of the relationship between physical characteristics and stimulus thresholds, and attempt to estimate stimulus thresholds from physical characteristics. The constructed model is evaluated via cross-validation and found to be able to estimate stimulus thresholds from physical characteristics with an 85% accuracy. © 2024 IEEE.

关键词： individual difference modeling stimuli threshold subjectivity weight perception

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Prediction and Modelling of Suicidal Mental Tendencies 8

Prediction and Modelling of Suicidal Mental Tendencies

引用

8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Guruprasad, Sunitha Rao, Arnav G. Shreeraksha Shivani, K. Varsha, S.N. Yashoda, K. St Joseph Engineering College Dept. of Computer Science & Engineering Mangaluru India Visvesvaraya Technological University Karnataka Belagavi India Ramaiah Institute of Technology Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350350593

Suicide remains a pressing public health concern worldwide, necessitating effective predictive and preventive strategies. Predicting suicidal tendencies is a complex endeavor, as it involves multifaceted interactions between biological, psychological, and environmental factors. Advances in computational modeling and machine learning have provided promising avenues for understanding and forecasting these tendencies. Such models often integrate clinical data, including psychiatric history, symptom severity, and demographic information, with biological markers and psychosocial variables. The objective of our work is to identify patterns and risk factors that may indicate heightened suicidal risk. Ongoing research seeks to refine predictive models, incorporate real-time monitoring, and develop targeted interventions, aiming to reduce the incidence of suicide and provide timely support to those at risk. The application of suicide text analysis extends to user-friendly interfaces, allowing individuals to input text, receive predictions, and access relevant support. The analysis involves preprocessing techniques such as text normalization, stop-word removal, and stemming to enhance the quality of input data. Various machine learning algorithms, ranging from Naive Bayes to sophisticated ensemble methods, are explored to determine their effectiveness in predicting suicidal ideation. © 2024 IEEE.

关键词： Prediction models

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Deadline Constrained Least Laxity Time Based Cloudlet Scheduling

Deadline Constrained Least Laxity Time Based Cloudlet Schedu...

引用

2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Datta, Lipika College of Engineering and Management Dept. of Computer Science and Engineering West Bengal Kolaghat India

ISBN: (纸本)9798331517892

Users send their requests to cloud service providers, who then deliver the services over networked systems. With a high volume of requests, it is crucial to schedule cloudlets effectively to ensure that most meet their specified deadlines for optimal performance. This paper focuses on calculating Both slack time and laxity time for cloudlets across all virtual machines and scheduling them accordingly. It also considers the rearrangement of cloudlets already assigned to virtual machines to accommodate new cloudlets. The CloudSim simulation environment is utilized to schedule cloudlets by evaluating their slack time and laxity time across various virtual machines. This approach helps in selecting the most suitable virtual machine for executing the cloudlets. By incorporating laxity time into the scheduling decisions, the system becomes more balanced, reduces makespan, and thus enhances overall performance. The proposed method has been shown to reduce task rejection by at least 10% and the makespan by an average of 17% when compared to the EFD algorithm. © 2024 IEEE.

关键词： Virtual machine

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The Role of Medical Expert Systems in Diagnosis of Chronic Kidney Diseases 2

The Role of Medical Expert Systems in Diagnosis of Chronic K...

引用

2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Kumar, Kuchipudi Prasanth Bharati, K.F. Jawaharlal Nehru Technological University Dept. of Computer Science and Engineering Andhra Pradesh Ananthapuramu515002 India Jntua College of Engineering Dept. of Computer Science and Engineering Andhra Pradesh Ananthapuramu515002 India

ISBN: (纸本)9798350370188

The ability to forecast kidney diseases in real-time is critical for preserving patients' lives. Early detection of these diseases not only extends the patient's longevity but also reduces treatment costs. It is very advised to develop a reliable expertise system for detecting these problems before they worsen. These smart solutions have the potential to save many lives. This review study assesses the Dice score efficacy characteristics obtained from various methodologies utilized in previous studies for detecting renal abnormalities or chronic kidney diseases. It gives a complete overview of medical expert systems in proposed methodology and emphasizes the most effective methods for identifying chronic kidney disease (CKD). © 2024 IEEE.

关键词： Diagnosis

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Test Suite Minimization Through PSO 11

Test Suite Minimization Through PSO

引用

11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Ahuja, Neeru Bhatia, Pradeep Kumar Guru Jambheshwar University of Science and Technology Dept. of Computer Science and Engineering Haryana Hisar125001 India

ISBN: (纸本)9798350350357

Regression testing is a critical stage that ensures the software changes are not affecting existing functionality. Ideally, conducting regression tests after each software change is essential. However, the exhaustive repetition of full tests for every alteration proves time-consuming, expensive, and demands significant resources. Test suite minimization serves as a solution by eliminating redundant test cases. This research presents an advanced approach to eliminate redundant test cases effectively. The proposed method focuses on identifying a concise yet effective set of test cases that align with requirements and efficiently uncover faults. This streamlined approach not only reduces testing expenses but also contributes to a more efficient testing process. The study demonstrates its effectiveness in minimizing the test suite by employing the Particle Swarm Optimization (PSO) algorithm with varied inertia weight strategies. Results from simulations indicate the success of this approach in reducing the test suite size while maintaining or even improving code coverage. Comparative analysis against Genetic Algorithms (GA) and Artificial Bee Colony (ABC) methods underscores the superior performance of PSO, specifically with tailored inertia weight strategies. This research significantly contributes to optimizing software testing methodologies, ultimately enhancing fault detection efficiency in software development. © 2024 IEEE.

关键词： Software testing

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Deep Learning for Enhanced Consumer Behavior Analysis and Predictive Accuracy

Deep Learning for Enhanced Consumer Behavior Analysis and Pr...

引用

2024 International Conference on Innovations and Challenges in Emerging Technologies, ICICET 2024

作者： Bharti, Jigyasa Dongre, Snehlata G H Raisoni College of Engineering Dept. of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350319019

The accelerating digital transformation in retail necessitates advanced analytical approaches to effectively comprehend and predict consumer behavior. Existing methodologies in retail data mining often fall short in handling the complexity and heterogeneity of data, resulting in suboptimal predictive accuracy and operational efficiency. This work addresses these limitations by introducing a novel deep learning framework that synergizes the capabilities of Graph Neural Networks (GNNs) with Q Learning and VARMAx models. The proposed model adept.y fuses data from diverse sources such as Twitter, Amazon, Facebook, and Flipkart, creating a comprehensive analysis platform. By leveraging the relational data structure processing power of GNNs and the dynamic decision-making proficiency of Q Learning, along with the time series predictive strength of VARMAx models, this approach offers a more holistic understanding of consumer trends. The efficacy of this model is demonstrated through extensive testing on varied data samples encompassing social media, e-commerce platforms, and tweets. The results are compelling: an 8.3% increase in accuracy, 8.5% enhancement in precision, 6.5% improvement in recall, and a significant 10.4% reduction in delay compared to existing classification methods. The impact of this work is far-reaching, offering retailers a robust tool for strategic decision-making, enhanced customer insight, and a competitive edge in the rapidly evolving digital marketplace. This approach not only propels the retail industry forward but also sets a new benchmark in the application of deep learning for consumer behavior analysis. © 2024 IEEE.

关键词： data mining

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Image Segmentation Survey: Classical and Deep Learning Methods 4

Image Segmentation Survey: Classical and Deep Learning Metho...

引用

4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Bachani, Viren Roychowdhury, Anish Dasgupta, Abhijit Ei-Alfy, Hazem BSc. Data Science SP Jain School of Global Management Sydney Australia SP Jain School of Global Management Data Science Dept. Sydney Australia SP Jain School of Global Management Data Science Dept. Mumbai India School of Computer Science University of Sydney Sydney Australia Alexandria University Faculty of Engineering Alexandria Egypt

ISBN: (纸本)9798350395914

Image segmentation is a prerequisite to almost all computer vision applications. It enables the extraction of meaningful information from visual inputs by partitioning images into segments with shared features. This review paper begins by outlining different image segmentation tasks. It then surveys a broad spectrum of segmentation techniques, ranging from classical frameworks to deep-learning (DL) architectures. We perform this through a systematic literature analysis across a handful of applications as part of our methodology. A key contribution of our survey is that it elaborates on a few situations where classical approaches outperform DL-based methods in accuracy, along with the added advantage of computational efficiency. In some cases, these findings also provoke interest in revisiting classical methods, prompting strategies that leverage the strengths of both paradigms. © 2024 IEEE.

关键词： Markov processes

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Implementation of human-following robot system applying virtual delay to posture of a leader 3

Implementation of human-following robot system applying virt...

引用

3rd International Conference on Image Processing and Robotics, ICIPRoB 2024

作者： Takemori, Fumiaki Tottori University Faculty of Engineering Dept. of Electrical Engineering and Computer Science Tottori Japan

ISBN: (纸本)9798350374766

This paper develops an implementation of a measurement and control system in which a vehicle follows its predecessor while maintaining a certain distance. First, we construct a model that virtually delays the reference coordinates of the leading target backwards, and propose a control law that follows the steering angle of the following vehicle to that virtual delay point. Next, a range sensor is used to detect the position and orientation of the leader, and the position and orientation is estimated using a template matching method based on the person's body contour point group measured by the sensor. Finally, the effectiveness (including robustness) of the proposed system is demonstrated in some simulations and human tracking experiments. © 2024 IEEE.

关键词： Template matching

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Identifying Hacking Failures in the IaaS, PaaS, and SaaS Networks to Secure Cloud Applications 6

Identifying Hacking Failures in the IaaS, PaaS, and SaaS Net...

引用

6th Parul International Conference on engineering and Technology, PICET 2024

作者： Bhavsar, Rakesh Thakar, Vishvjit Dept. of Computer Science Engineering School of Engineering Indrashil University Gujarat Mahesana India

ISBN: (纸本)9798350369748

From small businesses to major corporations, Cloud computing is quickly taking over as the preferred platform. due to the obligation to guarantee that cloud apps are safe and can shield data from unauthorized attackers. According to the joint responsibility model of Cloud Security, organizations who utilize these services are also responsible for maintaining their degree of security while service providers are responsible for making sure that their services are safe. A shared responsibility Paradigm for identity and access management transfers part of the client's responsibility for application security. Setting up procedures for identity management and access control as well as carrying out ethical hacking and penetration testing are required to guarantee the greatest degree of data security. Since Cloud Security has developed into a shared duty between the user and the service provider, clients are now required to take accountability for the security of their applications in the cloud. IaaS and PaaS services are the major beneficiaries of this. Advanced security procedures like ethical hacking and penetration testing are necessary to provide a safe virtual environment. Organizations will be able to avoid harmful assaults and data leaks thanks to these efforts. Raising risk visibility, identifying vulnerabilities, minimizing risks, and gathering crucial telemetry data are the greatest ways to enhance security. The primary goal of this study article is to assess the techniques for designing a framework or checklist to identify associated risks & vulnerabilities while at the same time attacking cloud-based applications. As a consequence, malicious actors will be kept out of cloud-based programs. © 2024 IEEE.

关键词： Cloud security

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An Extensive Review of Intelligent Deep Learning Based Anomaly Detection and Classification of 3D MRI Brain Imaging data 3

An Extensive Review of Intelligent Deep Learning Based Anoma...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Hima Bindu, J. Uma Dev, M. Vignan's Foundation for Science Dept. of Computer Science and Engineering Andhra Pradesh Guntur522213 India

ISBN: (纸本)9798350396157

MRI - Magnetic Resonance Imaging is one of the frequently employed imaging modalities for brain anomaly detection. MRI produces a massive quantity of data, which includes a consecutive set of scans taken at several time instants. Since the existence of brain anomalies that exists examined on every MR sequence, manual brain anomaly detection necessitates anatomical knowledge, costly, laborious, and inaccurate due to human error. Automatic brain anomaly segmentation from 3D Magnetic Resonance Image (MRI) is essential to perform proper diagnosis, monitoring, and treatment planning of the disease. However, owing to the structural difficulties such as hazy boundaries with uneven shapes, precise 3D brain tumor demarcation is interesting. With the recent developments of Deep Learning (DL) models, this research study reviews the recent brain anomaly detection and classification models based on DL approach with 3D MRI data. The existing techniques related to brain tumor segmentation, classification, and validations are reviewed. Every reviewed technique is investigated based on the aim, underlying technique used, dataset, and evaluation parameters. Besides, a comparison table is provided by summarizing the reviewed techniques under several aspects. Finally, a quick analysis of the evaluated techniques' results is conducted to gauge their effectiveness. © 2024 IEEE.

关键词： Brain

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