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检索条件"任意字段=Conference on Photonic Devices and Algorithms for Computing V"

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Improving the Cloud Healthcare Services by Introducing AI for Better Performance and Quality

Improving the Cloud Healthcare Services by Introducing AI fo...

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International Youth conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Aisha Hamedi Mohammad Nassr Mohammad Ali Anbar vita v. vlasova Alena A. Zykina Tan K. Sin Information Technology Engineering Deparment University of Tarous Tartous Syria Communication Technology Engineering Department Information and Communication Technology Engineering College Tartous Syria Computer Technology Engineering Department Alayen Iraqi University AUIQ Nasiriyah Iraq Bauman Moscow State Technical University AI Research Institute Moscow Russian Federation Bauman Moscow State Technical University Moscow Russian Federation Faculty Of Business (FOB) Multimedia University Bukit Beruang Melaka Malaysia

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

Cloud computing has gained widespread popularity in various fields, especially in healthcare field where the cloud provide medical services without having to own or maintain the medical devices. This research offers an effective system to ensure the availability of cloud healthcare services by detecting faults in the cloud system to ensure the availability of resources, and making accurate predictions over a short period of time to improve the quality of service and to avoid waste and improve security and privacy. The methodology has been applied by using OpenStack platform for fault injection and Google Colab for data preprocessing. A variety of machine learning algorithms have been used to improve the accuracy of prediction: the Logistic Regression, Support vector Classification, Random Forest, k-Nearest Neighbors and decision Tree. The comparison results showed that the use of the SvC classification gave the best accuracy to predict faults, reaching 98%.

关键词： Cloud computing Accuracy Machine learning algorithms Computational modeling Static vAr compensators Medical services Quality of service vectors Security Artificial intelligence

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Smart Farming: Integrating Soil Monitoring and Weather Analysis for Precision Agriculture

Smart Farming: Integrating Soil Monitoring and Weather Analy...

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Advancements in Power, Communication and Intelligent Systems (APCI), International conference on

作者： Athulya Mol P. Aswathi E. Gokuljith K. Sajesh Kumar U. v. vinod Kumar Department of Electronics & Communication Engineering Government College of Engineering Kannur Kannur India

ISBN: (数字)9798350363289

ISBN: (纸本)9798350363296

Soil and weather conditions are foundational to agriculture, collectively influencing crop growth and productivity. This project proposes a comprehensive system that integrates soil and weather analysis to support precision farming. The system focuses on monitoring essential soil parameters such as moisture content, temperature, electrical conductivity, pH levels, and concentrations of key nutrients like nitrogen, phosphorus, and potassium alongside crucial weather data. Using open source hardware suitable for field deployment, the system allows farmers to remotely access data through mobile devices or PCs. This dual focus on soil and weather enables farmers to make more informed decisions about agricultural practices. Additionally, the system utilizes machine learning algorithms to predict crops based on soil and weather parameters, aiding in optimizing farming strategies and thereby enhancing overall agricultural productivity.

关键词： Productivity Cloud computing Machine learning algorithms Crops Soil Nutrients Monitoring

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Real-Time Prediction of Panic Attacks Through Sweat Analysis in Wearable devices

Real-Time Prediction of Panic Attacks Through Sweat Analysis...

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Women in computing (InCoWoCo), International conference for

作者： Suthir Sriram Shuddhasattwa Majumder Mimansa v Nivethitha Akash Saxena M Thangavel Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India School of Engg. & Technology Central University of Haryana Mahendragarh India College of Information and Technology United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

Panic attack has become a major issue not only in adults but children's too. School going students are suffering from eternal stress conditions which cause them to suffer from panic attacks. A panic attack at the wrong time can cost someone their careers and life. We are proposing hardware or an extension to smart watches equipped with machine learning models to predict if a person is going to have a panic attack. This can help early intervention of panic attack and take measures for easing patients or avoiding the panic attack can be taken. A ring made of semi absorbent strap which can be used to trap palm sweat and get input of it which will be crucial for detection of panic attack. The mathematical model we are proposing mostly revolves around two major indicators, cortisol and folic acid. Concentration of these substances in sweat can be a clear indicator of a possibility of a panic attack and we'll build our model upon this. We will be monitoring sweat rate using real time sweat monitors or humidity monitors which will act as a switch for our algorithm. Once sweat rate reaches Smax then further algorithm is launched based on cortisol and folic acid levels.

关键词： Analytical models Computational modeling Machine learning Humidity Predictive models Prediction algorithms Real-time systems Mathematical models Wearable devices Monitoring

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Atomistic Materials and TCAD Device Modeling and Simulation of Ultrawide Bandgap (UWBG) Materials and UWBG Heterointerfaces

Atomistic Materials and TCAD Device Modeling and Simulation ...

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IEEE Nanotechnology Materials and devices conference (NMDC)

作者： Ugonna Ohiri Xiangyi Tony Guo Northrop Grumman Systems Corporation (NGSC) Linthicum MD USA

ISBN: (数字)9798331541439

ISBN: (纸本)9798331541446

Ultrawide bandgap (UWBG) materials (e.g., diamond, gallium oxide, boron nitride, and aluminum nitride) possess exquisite materials, electronic, and thermal properties. Atomistic materials and TCAD device modeling and simulations are prime accelerators for understanding the exquisite capabilities of UWBG materials, which can therefore guide future experimental UWBG device design. In this study, we explore several electronic properties of UWBG materials and heterointerfaces (i.e., UWBG semiconductor - UWBG semiconductor heterointerfaces and UWBG semiconductor - metal heterointerfaces) using Density Functional Theory (DFT) first-principle approaches in Synopsys Quantum ATK. TCAD device simulations were also performed using Synopsys Sentaurus TCAD, where Transfer Length Method (TLM) currentvoltage (I-v) simulations were performed to extract specific contact resistivity values, which could assist in downselecting materials needed to form compatible Schottky and/or Ohmic contacts for future UWBG-based devices. All UWBG atomistic materials and TCAD device simulations were performed using multi-level parallelization on high-performance computing workstations (> 16 cores) provided by NGSC. Comprehensively, these atomistic materials and TCAD device simulation results show promise for developing the next generation of high-performance UWBG devices and systems for emerging Department of Defense (DoD) applications (e.g., RF, power electronics, and high-temperature environments).

关键词： Performance evaluation Radio frequency Schottky diodes photonic band gap Computational modeling Simulation Discrete Fourier transforms Workstations US Department of Defense Next generation networking

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Optimizing Energy Efficiency in 5G Networks: Integrating Multi-Packet Reception with Reinforcement Learning and Genetic algorithms

Optimizing Energy Efficiency in 5G Networks: Integrating Mul...

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International conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： v. Arun A. Gowtham U. Arul J. Angeljulie Department of Computing Technologies Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of CSE (Cyber Security) Madanapalle Institute of Technology & Science Madanapalle Annamayya District Andhra Pradesh Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai Tamil Nadu India Department of CSE Karpagam Academy of Higher Education Coimbatore Tamilnadu India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

The rapidly expanding data demand and the explosion of connected devices require energy-efficient answers in 5G networks. This study proposes a three-pronged solution that integrates Multi-Packet Reception (MPR), Reinforcement Learning (RL), and Genetic algorithms (GA) to improve energy efficiency in 5G networks. MPR allows base stations to process incoming data more efficiently by receiving multiple packets at once. This not only helps the base station “talk” more efficiently with the devices that are supposed to be connected to it at any moment (which obviously is more inefficient under 5G, the way that MPR can help be more efficient, makes it the prime candidate for meeting the challenge at hand) but also allows the incoming data to be processed at the same rate it could under the old conditions of processing one packet at a time. We use Genetic algorithms to directly tackle what we consider the core problems of the 5G system: how to efficiently (jointly) serve users and how to efficiently allocate power among the serving base stations to minimize power consumption without sacrificing service quality (QoS). We developed an integrated method that demonstrates how to simultaneously achieve three goals: (1) significant energy savings; (2) improved network performance; and (3) enhanced sustainability under 5G conditions…. We assume a base station [BS] model close to real-world. Based on this model, we simulate our method in several scenarios. In these scenarios, we validate the network performance gains, energy savings, and enhanced sustainability that we design our method to achieve.

关键词： Base stations Power demand 5G mobile communication Energy conservation Reinforcement learning Quality of service Performance gain Energy efficiency Sustainable development Genetic algorithms

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Prediction of Diabetes and Recommendation of Insulin Dosage for Diabetic Patients 2nd

Prediction of Diabetes and Recommendation of Insulin Dosage ...

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2nd International conference on Computer vision, High-Performance computing, Smart devices, and Networks, CHSN 2021

作者： Azmath Hussain, Md. Sasi Kiran, N. Ravi Teja, A. Nikhil, Y. venkata Prasanth, G. Meera Durga, M.M. Department of Computer Science and Engineering V R Siddhartha Engineering College Vijayawada India

ISBN: (纸本)9789811698842

Diabetes mellitus is a disorder that occurs when the human body produces insufficient levels of insulin and is not able to maintain normal levels of sugar in the blood. People with diabetes have high chance of getting other diseases related to eyes, heart, kidneys, nerve damage, and so on. Generally, in hospitals, diabetes diagnosis is done by various tests. Healthcare industries collect the data from the patients and they have large data of the patient’s details. By applying this data to the machine learning classification algorithms, we can predict whether a person has diabetes. Here, we showed the accuracies of various machine learning algorithms in predicting the diabetes based on factors like BMI, age, Insulin, blood glucose levels, etc., and we have chosen the model which predicts more accurately than others. Further, we recommend the amount of insulin dosage to be given to the diabetic patients. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Insulin

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Biometric Recognition from Face-voice Using Rough-Neuro-Fuzzy Classifiers 2nd

Biometric Recognition from Face-Voice Using Rough-Neuro-Fuzz...

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2nd International conference on Computer vision, High-Performance computing, Smart devices, and Networks, CHSN 2021

作者： Anil, B.v. Ravikumar, M.S. K.V.G. College of Engineering Karnataka Sullia India

ISBN: (纸本)9789811698842

Reliable and prudent biometric person identification systems achieve authentication of the individual. In this work, eight distinct person identification algorithms are devised based on face recognition (FR), voice recognition (vR) and face-voice recognition (FvR) on video data. The lower face deformation caused by the movable jaw and the lip movement during speech is vectored into local voice-enabled face deformation profile (LvFDP). Feature fusion is employed between principal component analysis (PCA) features or linear discriminant analysis (LDA) with LvFDP and decision level fusion between FR methods, FR and vR methods. The eight algorithms are person identification using (i) vR by MFCC features and rough c-medoid clustering, (ii) vR by MFCC features and fuzzy c-medoid clustering, (iii) FR by PCA over LDA with fuzzy-multi-layer perceptron (fuzzy-MLP) classifier, (iv) FR by PCA over LDA and LvFDP with fuzzy-MLP classifier (v) FvR by PCA over LDA with f-MLP and MFCC with rough c-medoid (vi) FvR by PCA over LDA with f-MLP and MFCC with fuzzy c-medoid (vii) FvR by PCA over LDA and LvFDP with f-MLP and MFCC with rough c-medoid (viii) FvR by PCA over LDA and LvFDP with f-MLP and MFCC with fuzzy c-medoid. It is evident from the experimental results that fusion of LvFDP with facial features achieves increased recognition accuracy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Principal component analysis

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Survey on Security Based Approach to Prevent Attacks in SDN Using IoT Framework

Survey on Security Based Approach to Prevent Attacks in SDN ...

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International conference on Edge computing and Applications (ICECAA)

作者： v. Prabhu P. Balamurugan Department of Computer Science and Engineering School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chengalpattu Tamilnadu India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chengalpattu Tamilnadu India

Network evolution trendincludes Internet of Things (IoT) and Software-Defined Networking (SDN). By separating the data plane from the control plane, the software defined networking architectural framework is used and it will makes the network admins works more easily. SDN mainly focuses on network management and controls at the higher levels to monitoring and sharing the connection between the devices with the help of network connectivity in real time using the Internet of Things concepts. Almost in all the fields, IoT devices used so have to improve the security of IoT devices. The SDN has the centralized control logic, if the number of network traffic increases then centralized controller may not be handle all the network traffic requests at the same time, because the SDN controller is a single point of failure, its failure can bring the entire network down. The Software Defined Network (SDN) is easily exposed to various attacks mostly to DDoS attacks. Majority of the existing research is concentrated only on sensing of HR-DDoS attacks occurring over the control layer and not focusing on LR-DDoS attack occurring over the data layer because the accuracy of attacks on the LR-DDoS attacks are very low. This study mainly focusing on how to design the multi controller SDN architecture to improve the scalability, how to increase the detection accuracy of low-rate DDoS attacks on the SDN data layer and how to detect the attacks using machine learning algorithms.

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Enhancing DDoS Attack Detection: A Hybrid SvM-Decision Tree Ensemble Approach

Enhancing DDoS Attack Detection: A Hybrid SVM-Decision Tree ...

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International conference on computing and Networking Technology (ICCNT)

作者： Debendra Muduli Sandesh Bhatta Shantanu Shookdeb Aayush Adhikari Aaron Sapkota Prabhat Chaturvedi Dept. of Computer Science and Eng. C. V. Raman Global University Bhubaneswar India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

A DDoS attack overwhelms a network or server with an excessive amount of traffic, disrupting its normal operation and rendering it inaccessible to legitimate users. DDoS attacks involve coordinated efforts from multiple compromised devices, known as botnets, flooding the target with traffic simultaneously. This flood of data exhausts the target’s resources, causing service disruptions. Machine learning aids in DDoS attack detection by analyzing network traffic patterns. It learns to differentiate between normal and malicious activity by studying features such as packet rates, traffic volume, and protocol anomalies. By training on labeled datasets containing both benign and attack traffic, machine learning algorithms can identify deviations indicative of DDoS attacks, thereby enhancing network security. The hybrid machine learning model combines the precision of Support vector Machines with the decision-making clarity of Decision Trees to effectively combat DDoS attacks. This innovative model achieves an impressive accuracy rate of $\mathbf{9 9. 9 8 \%}$ against the CICDDoS2019 dataset, demonstrating its exceptional performance in detecting various DDoS attack vectors.

关键词： Support vector machines Training Adaptation models Machine learning algorithms Accuracy Decision making Denial-of-service attack Numerical models Floods Decision trees

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A 748 GOPS/W RISC-v SoC with Reconfigurable Custom Instructions via a Synthesized eFPGA with 1.8μs Configuration Time in 22nm FinFET 45

A 748 GOPS/W RISC-V SoC with Reconfigurable Custom Instructi...

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45th Annual IEEE Custom Integrated Circuits conference, CICC 2025

作者： Mohan, Prashanth Das, Siddharth Mai, Ken Carnegie Mellon University Pittsburgh United States

ISBN: (纸本)9798331517458

Edge computing devices are often tasked with a diverse array of workloads including signal processing, cryptography, and machine learning. These devices must balance escalating computational demands with stringent power constraints, often relying on fixed-function accelerators to meet these requirements. However, the inherent inflexibility of these hardened accelerators poses a significant limitation, as the algorithms and requirements can change throughout the device lifecycle. Designers have investigated various approaches to enhance flexibility while retaining high throughput and energy efficiency. Extending the ISA of open architectures like RISC-v with custom instructions has shown promise [1]-[4], though selecting and evaluating the optimal set of extensions remains challenging. An alternative is integrating an embedded Field Programmable Gate Array (eFPGA) on the SoC as a loosely coupled programmable accelerator [5]-[9], enabling enhanced throughput, energy efficiency, and adaptability. However, prior works rely on generic eFPGAs hard-IP [5], [8], [9] with pre-defined architectures, often lacking sufficient resources to handle compute-heavy workloads, resulting in poor energy efficiency and high area overheads. In this paper, we demonstrate a combination of the two approaches by tightly coupling an eFPGA to the RISC-v CPU and SoC memory while tailoring the eFPGA architecture for compute-heavy workloads to improve the throughput and energy efficiency by 1-2 orders of magnitude over the RISC-v CPU. © 2025 IEEE.

关键词： Reconfigurable architectures

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