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共 25788 条记录，以下是371-380 订阅

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Machine Learning Approach for Parkinson' s disease Prediction through Quantum Computing Techniques 2

Machine Learning Approach for Parkinson' s disease Predictio...

引用

2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Arpitha, K. Lohith, H.R. Nemmadi, H.K. Sujan, K. Yashwanth, C.L. Dept. of Computer Science Engineering BGS Institute of Technology Adichunchanagiri University Mandya India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

One of the most serious illnesses that may impact the neurological system in humans is dementia, of which Parkinson' s disease is a major subtype. Patients with Parkinson' s disease have significant behavioural changes. The suggested system uses quantum computing and machine learning to build a speech signal-based method for Parkinson' s disease prediction. Four main machine learning methods are used in this prediction: Artificial Neural Networks, KNN, Decision Trees, and Naïve Bayes. Furthermore, different ensemble learning models are used to combine numerous models and improve the accuracy. An industry standard dataset from the UCI Machine Learning Repository is used to assess the model' s performance. Ensemble models have proven to outperform extremely efficient techniques like neural networks in terms of accuracy. A multi-layer perceptron, a very significant machine learning optimization technique, is incorporated into the suggested system. © 2024 IEEE.

关键词： Adversarial machine learning

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A Novel Dual Channel Isolated Current Source Gate Driver for Fast Switching MOSFETs 50

A Novel Dual Channel Isolated Current Source Gate Driver for...

引用

50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Elahi, Asjad Youssef, Mohamed Dept. of Electrical Computer and Software Engineering University of Ontario Institute of Technology OshawaON Canada

ISBN: (纸本)9781665464543

A new dual-channel discontinuous mode isolated current source gate driver (ICSGD) is proposed in this paper. The proposed gate driver can provide two isolated gate drive signals for two power MOSFETs in circuits where these switches are in sync. The proposed gate driver can achieve very fast turn on and turn off transitions, due to its current mode nature. In addition, this technique can achieve partial gate energy recovery by recovering a fraction of the RMS (CV2) energy, which is typically dissipated over the gate drive resistors in voltage source gate drive (VSGD) techniques. Thus, provides better efficiency. The proposed circuit consists of six transistors and a small pulse transformer to allow swift operation over a wide range of duty cycles. Current through the transformer is discontinuous to avoid conduction losses due to circulating currents such as in some resonant gate drivers (RGD). Consequently, the proposed ICSGD provides superior performance over its VSGD counterparts, especially in high-frequency (HF) operation. The design is implemented in both PSIM and Typhoon HIL (hardware in the loop) platforms. The model is verified with a match of results from PSIM and Typhoon. The PSIM & HIL results show that the ICSGD achieves a minimum of 20% efficiency improvement. © 2024 IEEE.

关键词： High frequency transformers

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Advancing Ophthalmology through Transfer Learning and Channel-Wise Attention for Retinal Disease Classification 6

Advancing Ophthalmology through Transfer Learning and Channe...

引用

6th International Conference on Electrical Engineering and Information and Communication technology, ICEEICT 2024

作者： Islam, Sudipta Progga Alam, Ashraf Ul Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350385779

The global incidence of ocular disorders, which impacts more than 2.2 billion people, requires innovative approaches to ensure early and accurate diagnosis. This paper explores the integration of artificial intelligence (AI) in ophthal-mology, focusing on retinal diseases like cataracts, glaucoma, and diabetic retinopathy. The utilization of AI enables remote examinations and consultations, providing a crucial advantage in early disease detection. This study introduces a comparative analysis of state-of-the-art models, combining pre-trained convolutional neural networks (CNNs) and incorporating a channel-wise attention mechanism for enhanced discriminative ability. The proposed hybrid model, combining EfficientNetBO and Inception V3 with channel-wise attention mechanism, exhibits a remarkable accuracy of 98.13%, outperforming standalone models and other combinations. The findings underscore the efficacy of combined architectures and the integration of attention mechanisms in advancing the classification of retinal diseases. © 2024 IEEE.

关键词： Ophthalmology

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Multimodal Emotion Recognition Exploiting Audio, Video and Textual Features with Crossmodal Attention

Multimodal Emotion Recognition Exploiting Audio, Video and T...

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2024 IEEE International Women in Engineering (WIE) Conference on Electrical and computer Engineering, WIECON-ECE 2024

作者： Das, Avishek Sarma, Moumita Sen Hoque, Mohammed Moshiul Chittagong University of Eng. & Technology Dept. of Computer Science and Eng Chattogram Bangladesh

ISBN: (纸本)9798331535476

Multimodal emotion recognition (MER) aims to identify and understand human emotions by integrating data from diverse sources such as speech signals, facial expressions, and textual content. This process requires addressing the complexities of merging varied data modalities, each of which presents unique challenges, including noise, variability, and inconsistencies in how emotional cues are expressed and perceived. This paper proposes a unique approach to MER by utilizing three complementary modalities-audio, video, and text-to classify emotions into distinct categories: anger, fear, joy, and sadness. The paper presents pairwise crossmodal attention to enhance the integration of unimodal features, which are then combined and processed through an artificial n eural n etwork (ANN) for classification. A key contribution of this work is the creation of the MAViT (Multimodal Audio Video Text) dataset, comprising 1,002 multimodal samples. The experimental results highlight the superior performance of the multimodal approach, achieving an F1 score of 0.62. Unimodal results were also analyzed in detail, further demonstrating the advantages of integrating data from multiple modalities to provide a more accurate and nuanced understanding of emotions. © 2024 IEEE.

关键词： Emotion Recognition

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Decentralized Cab Booking Application Utilizing Blockchain, Smart Contracts and Oracle Networks

Decentralized Cab Booking Application Utilizing Blockchain, ...

引用

2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Kaviraj, J. Jebaraj, Joesh R. Sabaresan, V. St. Joseph's Institute of Technology Dept. of Computer Science Chennai India

ISBN: (纸本)9798350359688

Traditional cab booking applications suffer from many issues, including centralized control, data vulnerabilities, and high operational costs paid to companies. These limitations necessitate a transition towards decentralized alternatives to address the growing need for more efficient, cost-effective, and secure transportation services. This paper presents an idea that uses blockchain technology to improve the cab booking system. In the proposed system, the aim is to eliminate central authorities, offering an array of benefits such as enhanced security, reduced costs, and increased transparency throughout the booking process. To achieve this, smart contracts are employed to automate and secure transactions. The client-side page is deployed to the InterPlanetary File System (IPFS) for improved accessibility and redundancy. Additionally, the creation and utilization of oracle networks for real-time, trustworthy location data are suggested. The approach represents a paradigm shift in the transportation industry, ensuring data privacy, cost-efficiency, and transparency while eliminating the need for centralized intermediaries. Through the integration of smart contracts, IPFS, and oracle networks, a promising path towards a decentralized and secure cab booking ecosystem is offered. © 2024 IEEE.

关键词： Smart contract

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Multi-Trajectory Drone Surveillance: Amplifying Monitoring Capabilities in Disaster Management

Multi-Trajectory Drone Surveillance: Amplifying Monitoring C...

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2024 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2024

作者： Mourya, Gojja Sree Jami, Abhinav Ajith, Abhinav Seikh, Ayan Habib Kumar, J. Sathish Motilal Nehru National Institute of Technology Dept. of Computer Science and Engineering Allahabad India

ISBN: (纸本)9798350348460

In the disaster-affected area, whether it's a search and rescue operation, mock drill, managing food distribution, issuing warnings or anything else, the key to successfully carrying out these activities is preparation. To ensure that plans are accurate, one needs to know what actually happened in the disaster-affected area and for this purpose thoroughly and quickly the entire area need to be studied. To address this issue, we use UAV or drone navigation autonomously in required trajectories. By using UAVs you can cover surveillance across the vast acres of land quickly and know what happened and plan rescue operations accordingly. In this paper, we presented our work on moving the drones in multiple trajectories and swarm, thus amplifying surveillance capabilities. Each trajectory is maneuvered autonomously and a corresponding trajectory-algorithm running at the back end is presented along with the simulation results. © 2024 IEEE.

关键词： Disaster management Drones Multi-Agent Systems Surveillance Swarm Trajectories Unmanned Aerial Vehicles (UAV)

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An Effective Way to Detect Phishing Website Using Machine Learning 6

An Effective Way to Detect Phishing Website Using Machine Le...

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6th International Conference on Electrical Engineering and Information and Communication technology, ICEEICT 2024

作者： Toyeer-E-Ferdoush Shorab, Mst. Sumaiya Ritu, Saima Akter International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350385779

In today's interconnected digital landscape, phishing represents a significant threat to both individuals and organizations. Phishers cleverly utilize deceptive techniques to extract sensitive information from unsuspecting users, posing a risk to critical account details. Despite various proposed methods to detect phishing websites, the adaptability of phishers continually challenges the efficacy of existing approaches. Recognizing the vital role of Machine Learning (ML) in combating these evolving threats, this research investigates the application of ML techniques. Common characteristics in phishing attacks can be identified through accurate ML analysis. This research employs a range of ML models and transformer models, including Catboost (Cat), XGBoost (XGB), Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), TabNet, K-nearest neighbor (KNN), and Logistic Regression (LR) with grid-search. Among these models, the Catboost classifier achieved a remarkable accuracy of 97%. This study shows dedication to utilizing diverse methodologies to advance the field of phishing website detection. © 2024 IEEE.

关键词： Decision trees

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SPEI Based Drought Prediction and Validation Using Machine Learning Algorithm in Bangladesh from 1981 to 2017

SPEI Based Drought Prediction and Validation Using Machine L...

引用

2024 IEEE International Conference on Signal Processing, Information, Communication and Systems, SPICSCON 2024

作者： Mashuq, Md Farhan Akther, Juthy Das, Krishna Dept. of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh

ISBN: (纸本)9798331510213

A natural event known as a drought is defined by a long period of insufficient rainfall and limited access to water supplies, which means there isn't enough water for many uses, including agriculture, industry and domestic consumption. The Standardized Precipitation Evapotranspiration Index (SPEI) is a frequently utilized indicator in drought monitoring. In this work, from 1981 to 2017, temporal variation at Ishwardi upazila was used to evaluate and analyze the SPEI's performance at 1, 3, 6 and 12-month timeframes. An analysis is carried out to see how well machine learning approach such as Support Vector Machine (SVM), Random-Forest (RF), Decision-Tree (DT), Extreme Gradient Boosting (XGB) and K-Nearest Neighbors (KNN) work in forecasting drought conditions. The prediction outcomes of all five techniques were assessed using four performance measures: the Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and Nash-Sutcliffe Model Efficiency Coefficient (NSE). The proposed methods provided good results and can serve as a quick decision-making tool for water management. © 2024 IEEE.

关键词： Support vector machines

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Evaluating the Performance of Sidecar-based and Sidecarless Cloud-native Service Mesh Solutions 18

Evaluating the Performance of Sidecar-based and Sidecarless ...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Hasanth, Karumuri Meher Nadig, Deepak Purdue University West Dept. of Computer and Information Technology LafayetteIN47907 United States

ISBN: (纸本)9798350391725

The software development landscape is rapidly evolving, marked by the shift from monolithic applications to microservices architectures and the adoption of agile methodologies. While developing microservices has become more effortless, the challenges of maintaining and deploying them persist, specifically with managing the complex dependencies associated with multiple microservice deployments. With an increasing demand for container orchestrators and Kubernetes emerging as the industry standard, networking microservices remains a key challenge. Service mesh, a solution to this problem, consists of a control and a data plane, with the former responsible for managing the components of the latter. Data planes in most service meshes are implemented using sidecar proxies, posing specific challenges when injected into application pods. A potential solution to these challenges lies in the sidecarless approach. This paper aims to explore the eBPF-based service mesh, Cilium, which eliminates the need for a sidecar while encompassing most service mesh capabilities. Further, we benchmark Cilium's performance, analyze its effectiveness, and compare its performance with Istio and Linkerd. Our evaluations show that Linkerd outperforms other solutions with respect to response times, with Linkerd performing 29.85 % and 63.43% better than Cilium and Istio, respectively. © 2024 IEEE.

关键词： Application programs

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Personal Finance Management and Prediction using ML Algorithms 8

Personal Finance Management and Prediction using ML Algorith...

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8th IEEE International Conference on Computational System and Information technology for Sustainable Solutions, CSITSS 2024

作者： Kaushik, Sujay G. Kashyap, Sumukha S. Surabhi, B.V. Chaitra, M. Reddy, Santhosh Anitha, N. Bnm Institute of Technology Affiliated to Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331505462

Effective personal finance management is essential for achieving financial stability and long-term financial health. This paper presents a comprehensive approach to summarizing, analyzing, and show monthly expenses using machine learning models. By collecting user inputs such as age, place of stay, number of dependents, monthly expenses on transport, education, subscriptions, savings, medical expenses, and more, the system uses linear regression, decision trees and random forest algorithms to predict financial values. The system provides detailed information of expenses, savings, and overall financial health, along with personal health analysis based on dietary habits. Users have access to a wide range of financial and lifestyle information, allowing the system to provide personalized recommendations to increase financial security and achieve financial goals. The model also evaluates the impact of recurring expenses and notable investments, helping users understand their spending patterns and make informed financial decisions. The results demonstrate the system's capability to deliver accurate predictions and actionable advice, enable users to better manage their finances. This approach has led to significant advances in personal financial management by integrating machine learning techniques to provide insight into people's financial health. © 2024 IEEE.

关键词： Adversarial machine learning

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