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Cardiac Disease Detection Using Machine Learning Algorithms: A Review

Cardiac Disease Detection Using Machine Learning Algorithms:...

引用

Advancements in Smart, Secure and Intelligent Computing (ASSIC), International Conference on

作者： Soumya Sahoo Sukant Kishoro Bisoy Pradeep Kumar Mallick Dept of Computer Science and Engineering C.V Raman Global University Bhubaneswar India Dept of Computer Engineering KIIT University Bhubaneswar India

Health problems are currently spreading progressively. Particularly prevalent today are cardiovascular disorders. Mortality nowadays is primarily caused by cardiovascular disease in emerging nations. The death rate can be reduced by early detection of cardiovascular infections and ongoing care for doctors. However, because it takes more effort and skill, it is impossible to assess patients consistently and accurately in every situation. The amount of information available in the healthcare fields is enormous. The information that has been stored can be a useful resource for predicting potential future illnesses. There are very few effective tools available to extract information from data sets for scientific disease discovery or other purposes. The numerous patient heart properties may be used with a number of informational mining and machine learning approaches to excerpt crucial data obtained from the set of data. In addition to constructing scientific objectives by utilizing data mining and machine learning approaches. Aim of this work is to summarize current studies on cardiac sickness expectations and related results.

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A Real-Time Approach to Detecting API Abuses Based on Behavioral Patterns

A Real-Time Approach to Detecting API Abuses Based on Behavi...

引用

Cryptography, Security and Privacy (CSP), IEEE International Conference on

作者： Sameeraa Prinakaa Bavanika V Sanjana S Sneha Srinivasan Sarasvathi V Dept. of Computer Science Engineering PES University Bengaluru India

ISBN: (数字)9798350386325

ISBN: (纸本)9798350386332

Over the past two decades, the internet has undergone a remarkable expansion, giving rise to the development of web-based software solutions. This evolution has led to widespread web Application Programming Interface (API) usage, facilitating remote software communication. However, the increased frequency of API calls has unfortunately resulted in a corresponding surge in API misuse instances. Monitoring API traffic becomes imperative to ensure data security, service availability, and system integrity. This paper introduces an API abuse detection system within a Log Storage Application utilizing Spring Boot with OAuth2.0 and JSON Web Token (JWT) based authentication. The proposed system effectively safeguards against critical security threats, such as Economical Denial of Service (EDoS) attacks (OWASP top 4), Unauthorized Access attempts (OWASP top 3), Brute Force Broken User Authentication, and Behavioral API abuses, with alerts promptly sent to the server. The prevalence of API usage has created numerous attack surfaces, offering a rich landscape for further research opportunities.

关键词： Privacy Force Authentication Software Real-time systems Servers Surges

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Optimization Scheduling of Multi-Energy Microgrid Based on the QUBO Quantum Computing Model 19th

Optimization Scheduling of Multi-Energy Microgrid Based on t...

引用

19th Annual Conference of China Electrotechnical Society, ACCES 2024

作者： Wang, Baonan Wang, Hui Zhang, Dan College of Computer Science and Technology Shanghai University of Electric Power Shanghai China State Key Laboratory of Power System Operation and Control Dept. of Electrical Engineering Tsinghua University Beijing China Institute of Logistic Science and Engineering Shanghai Maritime University Shanghai China

ISBN: (纸本)9789819608966

Current optimization methods for microgrid scheduling face issues such as insufficient precision in energy distribution, high operational costs, and inefficiency. In response to these challenges, an optimization scheduling method for multi-energy microgrids based on the QUBO (Quadratic Unconstrained Binary Optimization) quantum computing model is proposed in this paper, alongside a discretization approach based on a grid partitioning strategy. To validate the feasibility of addressing microgrid optimization scheduling problems in a quantum computing environment, a QUBO model is constructed and simulation studies are conducted in the qbsolv environment provided by D-Wave. The results indicate that operational costs are significantly reduced with an increase in the number of grids, particularly a reduction of 4.52% when the number of grids increases from five to ten. This method not only effectively enhances the precision of electricity supply and energy utilization efficiency but also reduces unnecessary electrical waste and economic costs. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Microgrids

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Reducing Client-Side Cost in Secure Inner Product Evaluations 39

Reducing Client-Side Cost in Secure Inner Product Evaluation...

引用

39th International Conference on Information Networking, ICOIN 2025

作者： Hahn, Changhee Koo, Dongyoung Hur, Junbeom Seoul National University of Science and Technology Dept. Electrical and Information Engineering Korea Republic of Hansung University Dept. Convergence Security Korea Republic of Korea University Dept. Computer Science and Engineering Korea Republic of

ISBN: (纸本)9798331506940

In the era of ubiquitous data collection and analysis, preserving privacy while utilizing data, such as inner product evaluations, poses a significant challenge. One such method is inner product functional encryption (IPFE), which enables the computation of inner products on encrypted vectors without exposing the vectors themselves. However, the computational intensity of IPFE decryption poses challenges, particularly for resource-limited client devices and high-dimensional vectors. To facilitate partial decryption, prior IPFE schemes require the client to derive a partial decryption key from an assigned secret key, incurring computation costs linear to the vector dimension. In this paper, we provide a framework for the challenges of IPFE, enabling efficient decryption outsourcing, maintaining data privacy, and facilitating key generation outsourcing in IPFE schemes. In the proposed framework, outsourced decryption using a partial decryption key generates a masked plaintext that only the client can unmask. Furthermore, the process of deriving the partial decryption key can be fully outsourced, thereby ensuring that the client's computational burden remains constant. © 2025 IEEE.

关键词： Sensitive data

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Efficient Compressed Sensing for Real-Time Electrocardiogram Acquisition on Low-Power Medical Devices

Efficient Compressed Sensing for Real-Time Electrocardiogram...

引用

IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Mitchell Terrell Jon Weissman Dept. of Computer Science and Engineering University of Minnesota Minneapolis Minnesota

ISBN: (数字)9798350351552

ISBN: (纸本)9798350351569

Low cost wearable and implantable cardiac monitoring devices (WCM & ICM), combined with increasingly accurate disease and arrhythmia detection algorithms have proven effective to help slow the impact of cardiovascular disease, the worlds leading cause of death in 2022 [1]. Improvements to server-side detection algorithms along with hardware limitations of these devices such as slow processor speeds and minimal battery life has led to a desire to offload data from the devices for later analysis. Paired with limited storage capacity, this has led to a push to decrease the necessary storage for electrocardiogram (EKG) signals without sacrificing disease detection accuracy and device longevity. A promising recent innovation in EKG compression has come from Compressed Sensing (CS), which exposes inherent sparseness in the signal to selectively sample for eventual server-side reconstruction. Many CS approaches have been implemented on WCM devices which demonstrate a high compression ratio (CR) and accurate signal reconstruction, but quickly become impractical for the stricter hardware constraints of ICM devices due to the increased computation on the device and slow reconstruction time. In this paper we propose a CS approach known as Tailored Sensing (TS) which combines on-device prior knowledge, a custom transform basis, and optimal sense location selection to achieve improved CR and reconstruction accuracy while eliminating on-device computational burden and slow reconstruction time. Our approach offers equivalent or better signal reconstruction at previously unfathomable CRs due to our novel signal segmentation scheme. Additionally, our approach boasts a zero overhead on-device sensing strategy and a 93 % reduction in signal reconstruction time.

关键词： Technological innovation Accuracy Medical devices Transforms Electrocardiography Signal reconstruction Hardware Sensors Detection algorithms Compressed sensing

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Deep Dive into Predictive Modeling for Autism Spectrum Disorder using Facial Features and Machine Learning

Deep Dive into Predictive Modeling for Autism Spectrum Disor...

引用

International Conference on Communication, Computing and Internet of Things (IC3IoT)

作者： Chaitra Bhat Gagan Goutham Charvi Bannur Sada Kakarla Hr Mamatha Dept. of Computer Science & Engineering PES University Bengaluru India

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

Autism Spectrum Disorder is a neuro-development behavioural disorder that is classified as a chronic disability leaving individuals unable to accurately grasp their environment and can lead to limited development of communication skills, thinking and other social skills. Prediction of ASD using video recordings is an open-ended critical problem in machine learning to facilitate the process of early intervention which requires high computational resources. This research aims to predict ASD in children using videos by extracting features and converting them into a binary classification problem thereby eliminating the need for high computational resources. This research postulates a study of machine learning techniques like Logistic Regression, Decision Trees and Neural Networks in comparison for the prediction of ASD as well as a novel approach to reduce the computational requirements for the analysis of videos. This research also addresses the lack of publically available video datasets for the analysis of Autism. The highest accuracy metric for the models was using the Neural Network architecture closely followed by the Decision Tree Classifier which yielded accuracies of 99.8% and 99.65% respectively. In this article, we offer an overview of predicting Autism Spectrum Disorder whilst focusing on the importance of video datasets.

关键词： Autism Logistic regression Computational modeling Neural networks computer architecture Predictive models Feature extraction

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Optimizing Cloud Security in Dynamic Environments Through Adaptive Workload Management Algorithm

Optimizing Cloud Security in Dynamic Environments Through Ad...

引用

Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Monika Yadav Garima Sharma Dept. of Computer Science & Engineering The Northcap University Haryana India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

With the growing interest of researchers in the area of cloud computing, security emerges as a major area of concern. As the complexity of cloud environments grows and the dependency on cloud services deepens across various sectors, ensuring robust security measures is crucial. This study proposes an innovative approach to enhance cloud security through efficient workload scheduling strategies. Leveraging insights from existing studies on green cloud computing, clonal selection algorithms, and energy-efficient resource allocation, the research introduces a novel security-aware workload organizing algorithm. The proposed algorithm aims to optimize both energy consumption and security in cloud data centers. Through comprehensive simulations on the Cloud Sim platform, the results demonstrate the algorithm's efficiency in reducing energy consumption while fortifying security measures. Moreover, the present study contributes to the evolving field of cloud security, offering a promising avenue for shielding data in dynamic cloud environments.

关键词： Energy consumption Processor scheduling Cloud computing security Heuristic algorithms Green products Energy measurement Dynamic scheduling

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A Review on Cosmetic Product Recommendation Using Deep Learning

A Review on Cosmetic Product Recommendation Using Deep Learn...

引用

Soft Computing for Security Applications (ICSCSA), International Conference on

作者： Ruchika Chouhan Snehlata Barde Dept. Of Computer Science Engineering Sage University Indore India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Cosmetic products play an important role in every one’s life. Cosmetic products also called as makeup are substance that is applied on the body especially on face. The modern lifestyle can be demanding, leaving little time for self-care. However, it’s crucial to prioritize self-care, including skincare, despite hectic schedules. Indeed, relying solely on best-seller products or in-store recommendations may not always be effective in determining compatibility between a skincare product and an individual’s skin condition. This is because everyone’s skin tone and type of the skin is unique, with varying sensitivities, concerns, and needs. Many people buy their goods online today, using various e-commerce sites. So, to choose the best skincare product during online purchasing is difficult, since it depends entirely on skin type and usage. The main aim of this paper is to survey and explores the existing recommendation systems designed to assist users in selecting cosmetics personalized to their unique skin type, by using advanced algorithms and machine learning. This study has reviewed various methods like face recognition, cosmetic product recognition, tag recommendation, facial skin image classification using convolution neural network, a new examples-rules guided deep neural network approach. Through a comprehensive review of literature and existing research, this paper examines the methodologies, algorithms, and techniques employed in cosmetic recommendation systems. Key factors such as user preferences, skin type, skin concerns, and product attributes are analysed to understand how recommendation systems use this information to generate personalized recommendations.

关键词： Deep learning Machine learning algorithms Convolution Reviews Face recognition Artificial neural networks Skin Classification algorithms Usability Recommender systems

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Accurate and Optimized Labelling of Fashion Products Through Attention Based Spiking Neural Networks

Accurate and Optimized Labelling of Fashion Products Through...

引用

International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： N Vithyatharshana Yedhoti Thrinayani Yogeshwar Nitin Kulkarni Rimjhim Padam Singh Sneha Kanchan Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India Department of Computer Science & Engineering Amrita School of Computing Bengaluru Department of Internet Engineering and Computer science Universiti Tunku Abdul Rahman Malaysia

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

Automation of garment classification has become the backbone of e-commerce, influencing product search, personalization, and customer experience. This paper uses the Fashion MNIST dataset, a rich collection of clothing images to investigate the potential of Spiking Neural Networks (SNNs) for accurate and efficient classification. SNNs are an excellent choice for widespread usage and real-world integration due to their low power consumption and biomimicry. With the intention of revolutionizing product search, personalization, and customer experience, SNNs are made to address style differences, perspectives, and illumination. In order to fully utilize Fashion MNIST, this paper suggests strategic improvements to the SNN architecture. By addressing key challenges and introducing novel optimizations, our research contributes to a more robust and intelligent future for automated fashion product labelling. An SNN model with 93.6% accuracy has been developed as the base model for all the applications for the same. This data-driven approach leads the way to flawless product searches, highly customized recommendations, and a more intelligent fashion technology in future.

关键词： Accuracy Power demand Clothing Lighting Spiking neural networks computer architecture Encoding Labeling Electronic commerce Optimization

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A Rubric-Centric Approach for Automated Test Correction Utilizing RAG and Fine Tuning

A Rubric-Centric Approach for Automated Test Correction Util...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： G. Harshavardhan Kulvinder Singh Dept. of Computer Science & Engineering Chandigarh University Punjab India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Assessments are a popular method to quantify a person's knowledge, skill, or cognitive ability. However, to grade such assessments, particularly subjective questions requires a significant amount of time and effort for the examiners. There is also a degree of variance due to inconsistency from different examiners. Thus institutions often resort to taking objective-type questions, which may not completely capture a person's understanding. Unfortunately, many other proposed solutions, tend to be difficult to implement and scale. To address these issues, this paper proposes an innovative method to automate the grading of subjective assessments by leveraging recent advances in artificial intelligence. This approach aims to replicate the grading style of human experts, offering significant benefits for educational institutions, faculty, and students by enhancing the efficiency and quality of grading. Widespread adoption can help increase the quality of education overall, resulting in more qualified graduates. This method involves using a rubric to fine- tune pre-trained language models, providing them with clear guidelines to ensure consistent and accurate grading. Inspired by how rubrics guide human examiners to minimize variation, this approach will use rubrics to instruct the model on the most relevant grading criteria. Utilizing such clearly defined and precise grading criteria, users can obtain reliable results, comparable to human experts.

关键词： Costs Accuracy Computational modeling Education Organizations Natural language processing Reliability Artificial intelligence Tuning Guidelines

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