標(biāo)題: Titlebook: High Performance Computing, Smart Devices and Networks; Select Proceedings o Ruchika Malhotra,L. Sumalatha,Naresh Babu Muppalan Conference [打印本頁] 作者: Jefferson 時間: 2025-3-21 18:13
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書目名稱High Performance Computing, Smart Devices and Networks網(wǎng)絡(luò)公開度
書目名稱High Performance Computing, Smart Devices and Networks網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱High Performance Computing, Smart Devices and Networks被引頻次
書目名稱High Performance Computing, Smart Devices and Networks被引頻次學(xué)科排名
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書目名稱High Performance Computing, Smart Devices and Networks年度引用學(xué)科排名
書目名稱High Performance Computing, Smart Devices and Networks讀者反饋
書目名稱High Performance Computing, Smart Devices and Networks讀者反饋學(xué)科排名
作者: 持久 時間: 2025-3-21 23:43 作者: 山崩 時間: 2025-3-22 01:46 作者: generic 時間: 2025-3-22 05:49
Warusia Yassin,Azwan Johan,Zuraida Abal Abas,Mohd Rizuan Baharon,Wan Bejuri,Anuar Ismail is obtained and general spectral properties of the Hamiltonian as a cluster operator are demonstrated. The quasi-particle spectrum in the strong coupling limit, the Efimov effect, the current and noncurrent bound states are also discussed.作者: 允許 時間: 2025-3-22 11:58
https://doi.org/10.1007/978-981-99-6690-5Internet of Things; Bio-Informatics; Big Data Analytics; Machine Learning; Computational Intelligence; CH作者: 官僚統(tǒng)治 時間: 2025-3-22 13:04
Ruchika Malhotra,L. Sumalatha,Naresh Babu MuppalanComprises of peer-reviewed papers presented during the 3rd International Conference, CHSN 2022.Presents state-of-the-art connection between computational intelligence, machine learning, and IoT.Serves作者: HERE 時間: 2025-3-22 17:04
Lecture Notes in Electrical Engineeringhttp://image.papertrans.cn/h/image/426400.jpg作者: right-atrium 時間: 2025-3-22 22:13 作者: Certainty 時間: 2025-3-23 04:14
High Performance Computing, Smart Devices and Networks978-981-99-6690-5Series ISSN 1876-1100 Series E-ISSN 1876-1119 作者: STEER 時間: 2025-3-23 07:20 作者: 新手 時間: 2025-3-23 13:19
Subodh Mor,Shikha N. Khera,G. C. Maheshwarithe internal degrees of freedom in the external channels a certain. class of energy-dependent potentials is generated. By means of potential theory modified Faddeev equations are derived both in external and internal channels. We prove the fredholmity of these equations, what provides a sound basis 作者: 摘要記錄 時間: 2025-3-23 16:23
Sai Gopala Swamy Gadde,Kudipudi Pravallika,Kudipudi Srinivasimensional extensions of Kronig Penney‘s model). We discuss the mathematical definition of the Hamiltonian and its spectral properties in the case of perfect crystals, as well as in the case of crystals with deterministic or randomly distributed point defects. We also discuss the connection of such 作者: dainty 時間: 2025-3-23 19:09 作者: placebo-effect 時間: 2025-3-24 01:16 作者: 土產(chǎn) 時間: 2025-3-24 06:26
J. Arun Prakash,C. R. Asswin,K. S. Dharshan Kumar,Avinash Dora,V. Sowmya,Vinayakumar Ravithe internal degrees of freedom in the external channels a certain. class of energy-dependent potentials is generated. By means of potential theory modified Faddeev equations are derived both in external and internal channels. We prove the fredholmity of these equations, what provides a sound basis 作者: 色情 時間: 2025-3-24 08:03
Warusia Yassin,Azwan Johan,Zuraida Abal Abas,Mohd Rizuan Baharon,Wan Bejuri,Anuar Ismail is obtained and general spectral properties of the Hamiltonian as a cluster operator are demonstrated. The quasi-particle spectrum in the strong coupling limit, the Efimov effect, the current and noncurrent bound states are also discussed.作者: 啪心兒跳動 時間: 2025-3-24 10:52 作者: Exuberance 時間: 2025-3-24 17:29 作者: GRIEF 時間: 2025-3-24 22:26 作者: fodlder 時間: 2025-3-25 02:59 作者: Offbeat 時間: 2025-3-25 05:44
Pediatric Pneumonia Diagnosis Using Cost-Sensitive Attention Models,ter alternative to traditional diagnosis methods. Medical experts examine the chest X-ray images to detect the presence of pneumonia; however, the low radiation levels of X-rays in children have made the identification process more challenging leading to human-prone errors. The increasing use of com作者: 賞錢 時間: 2025-3-25 10:42
An Integrated Deep Learning Deepfakes Detection Method (IDL-DDM),ulation via intelligent algorithm contributes to more crucial circumstances as electronic media integrity become a challenging concern. Furthermore, such unauthentic content is being composed and outstretched across social media platforms as detecting deepfakes videos is becoming harder nowadays. Ne作者: Bernstein-test 時間: 2025-3-25 15:11 作者: Obliterate 時間: 2025-3-25 18:41 作者: judiciousness 時間: 2025-3-25 23:01 作者: genuine 時間: 2025-3-26 01:19
Deep Learning-Based Automatic Speaker Recognition Using Self-Organized Feature Mapping,systems, and so on. The performance of these application depends on efficiency of ASR system. However, the conventional ASR systems were developed using standard machine learning algorithms, which resulted in low recognition performance. Therefore, this work is focused on development of deep learnin作者: 誘拐 時間: 2025-3-26 07:50
,Machine Learning-Based Path Loss Estimation Model for a 2.4?GHz ZigBee Network,ing of path loss (PL) for the deployment of a developed WSN system is a crucial task owing to the time-consuming and elegant operation. However, radiofrequency (RF) engineers adopted either deterministic or stochastic empirical models to estimate the PL. In general, empirical models utilize predefin作者: Gossamer 時間: 2025-3-26 08:30
Comparative Analysis of CNN Models with Vision Transformer on Lung Infection Classification,cough and if ignored can lead to the cause of death. Hence, a classification model that helps in the early detection of lung infections can help in avoiding further complications. This paper focuses on techniques to classify lung infections based on different convolution neural network model archite作者: separate 時間: 2025-3-26 14:01 作者: reperfusion 時間: 2025-3-26 20:43
Heart Device for Expectation of Coronary Illness Utilizing Internet of Things,ough wearable innovations, which is alluded to as “telemedicine.” The reason for the curio is to offer continuous checking of constant circumstances like cardiovascular breakdown, asthma, hypotension, hypertension, and so on that are situated a long way from clinical offices, like provincial areas o作者: Ordnance 時間: 2025-3-27 00:06
Parallel Programming in the Hybrid Model on the HPC Clusters,ssage passing interface (MPI) and open multi-processing (OpenMP) standards that provide various software development tools covering a wide range of techniques not strictly limited to shared memory. The theoretical part focuses on a general overview of the current capabilities of MPI and OpenMP as ap作者: 離開就切除 時間: 2025-3-27 05:12 作者: 疲勞 時間: 2025-3-27 08:59
A Review Paper on Progressive Approach to Reduce Context Switching in Round Robin Scheduling Algorier is delayed (on standby) due to a lack of resources such as I/O allowing the CPU to be fully utilized. The goal of CPU scheduling is to improve the system‘s efficiency, speed, and fairness. When the CPU is not being used, the operating system chooses one of the processes in the queue to start. A t作者: GONG 時間: 2025-3-27 11:04 作者: 職業(yè)拳擊手 時間: 2025-3-27 16:30 作者: generic 時間: 2025-3-27 18:04 作者: 欺騙手段 時間: 2025-3-27 22:30 作者: nautical 時間: 2025-3-28 04:58 作者: prostate-gland 時間: 2025-3-28 08:23 作者: Aspiration 時間: 2025-3-28 10:31 作者: cartilage 時間: 2025-3-28 18:34
Kuldeep Vayandade,Ritesh Pokarne,Mahalakshmi Phaldesai,Tanushri Bhuruk,Prachi Kumar,Tanmay Patil作者: Arb853 時間: 2025-3-28 20:32
Lavanya Bagadi,B. Srinivas,D. Raja Ramesh,P. Suryaprasad作者: chronicle 時間: 2025-3-28 23:15 作者: spondylosis 時間: 2025-3-29 03:50 作者: Adrenal-Glands 時間: 2025-3-29 10:30 作者: 代替 時間: 2025-3-29 11:25
Pediatric Pneumonia Diagnosis Using Cost-Sensitive Attention Models,These values are concatenated as a vector and passed through a Tanh activation function. The sum of elements in this vector forms the weights. These weights when used in the weighted average classifier results in an accuracy of 96.79%, precision of 96.48%, recall of 98.46%, F1-score of 97.46%, and a作者: Monolithic 時間: 2025-3-29 17:10
An Integrated Deep Learning Deepfakes Detection Method (IDL-DDM), Perceptron and Convolutional Neural Network (CNN). In addition, the Long Short-Term Memory (LSTM) approach is applied consecutively after CNN in order to grant sequential processing of data and overcome learning dependencies. Using this learning algorithm, several facial region characteristics such作者: 牽連 時間: 2025-3-29 20:18
Reinforcement Learning Based Spectrum Sensing and Resource Allocation in WSN-IoT Smart ApplicationsGI is involved. The role of the state–action–reward–state–action model is developed with an energy-efficient approach for optimizing the channel. Next, the Gittins index is designed to reduce the delay and enhance the accuracy of spectrum access. The simulation results are compared with two state-of作者: 辮子帶來幫助 時間: 2025-3-30 00:37
Deep Learning-Based Automatic Speaker Recognition Using Self-Organized Feature Mapping,ms the feature database. Finally, a test voice sample is applied to the trained DLCNN model, which recognizes the speaker detail. The simulations carried out on Anaconda (TensorFlow) showed that the proposed ASR-Net system resulted in superior recognition performance as compared to conventional syst作者: FIN 時間: 2025-3-30 05:38
,Machine Learning-Based Path Loss Estimation Model for a 2.4?GHz ZigBee Network, experimental setup was designed and tested in line-of-sight (LOS) and non-line-of-sight (NLOS) conditions to collect the influence parameters such as received signal strength indicator (RSSI), frequency, distance, and transmitter antenna gain. Besides that, environmental parameters such as temperat作者: 微生物 時間: 2025-3-30 09:19
Comparative Analysis of CNN Models with Vision Transformer on Lung Infection Classification,d analyzing lung infections by using InceptionV3, ResNet50, VGG16, InceptionResNet, and Vision Transformer. Each model is evaluated on the basis of mean accuracy error (MAE), binary accuracy, and loss. Among all the models, InceptionResNet has obtained a best accuracy of 93.33%. This study signifies作者: 來就得意 時間: 2025-3-30 14:47
,Classification of Alzheimer’s Disease Using Stacking-Based Ensemble and Transfer Learning,different stages. The approach produces efficient and accurate results and is designed to encourage the implementation of deep learning in day-to-day medicine without the need for much human involvement. In the proposed method, we use transfer learning to employ three pre-trained deep learning model作者: Servile 時間: 2025-3-30 17:05
Heart Device for Expectation of Coronary Illness Utilizing Internet of Things,e effect of pressure chemicals on hidden heart sicknesses. Numerous wearable innovations screen commonplace heart working pointers like circulatory strain, glucose level, blood oxygen immersion, and ECG. The proposed framework might gather the fundamental information while barring commotion unsettli作者: employor 時間: 2025-3-30 21:50 作者: 形容詞 時間: 2025-3-31 01:32
An Extensive Study of Frequent Mining Algorithms for Colossal Patterns,mall and mid-sized patterns aren’t mined, mining algorithms for enormous patterns run faster. In this work, an extensive study of colossal patterns, existing mining algorithms with its drawback is mentioned. The definitions of FPM, high utility mining and relation of colossal patterns with others ar作者: exhibit 時間: 2025-3-31 07:52 作者: commonsense 時間: 2025-3-31 12:07
Ensemble Model Detection of COVID-19 from Chest X-Ray Images,d models’ performance is estimated in terms of accuracy, precision, recall, and f1-score parameters and achieved better results for detection purpose. Hence, the proposed model is a promising diagnostic tool for accurate screening of COVID-19 disease.作者: etiquette 時間: 2025-3-31 16:43
The Development of Advanced Deep Learning-Based EoR Signal Separation Techniques, results show that compared with the traditional methods including polynomial fitting and continuous wavelet transform, the EoR signals detected by the proposed deep learning model have better quantitative evaluation indexes of SNR and Pearson correlation coefficient. This property provides a new way to explore the research field of EoR.作者: 觀察 時間: 2025-3-31 20:34 作者: ectropion 時間: 2025-4-1 00:39
Conference proceedings 2024he reader an up-to-date picture of the state-of-the-art connection between computational intelligence, machine learning, and IoT. The papers in this volume are peer-reviewed by experts in related areas. The book will serve as a valuable reference resource for academics and researchers across the globe..作者: curettage 時間: 2025-4-1 05:38 作者: Adulterate 時間: 2025-4-1 07:19
1876-1100 ntelligence, machine learning, and IoT. The papers in this volume are peer-reviewed by experts in related areas. The book will serve as a valuable reference resource for academics and researchers across the globe..978-981-99-6692-9978-981-99-6690-5Series ISSN 1876-1100 Series E-ISSN 1876-1119 作者: FLAIL 時間: 2025-4-1 14:12 作者: misshapen 時間: 2025-4-1 17:10
Melanoma Detection Using Convolutional Neural Networks,nctionality. The HAM10000 dataset has been used for the evaluation. It uses the global average pooling layer which is connected with the fully connected layers. The proposed system can be used to detect whether the disease is melanoma or not. The model has an accuracy rate of 85%.
