检索结果-内蒙古大学图书馆

1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Sugumaran, S. Buvaneswari, B. Senthilkumar, K.S. Jeevitha, R. Ramachandra, A.C. Vishnu Institute of Technology ECE Department Bhimavaram India Panimalar Engineering College Department of Information Technology Chennai India Sri Ramakrishna College of Arts & Science Department of Computer Applications Coimbatore India KPR Institute of Engineering and Technology Computer Science and Engineering Arasur India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350323795

The Underwater Wireless Sensor Network (UWSN) is crucial for private, military, and commercial maritime operations, like environmental monitoring, underwater research, scientific data collection, and underwater wireless communications. The existing protocols for Terrestrial Wireless Sensor Networks (TWSNs) could be better in terms of power efficiency, reliability, and effectiveness. Due to their unique characteristics. The energy-efficient convergent depth variation routing (ECDVR) algorithm is introduced to evaluate and reduce power consumption by calculating the distance between each node point and node depth variation. This algorithm considers the two-hop neighbour depth variation when sending data to a neighbor node with a higher forward angle toward the receiver. It calculates the receive time contribution of the Adaptive Time Difference of Arrival (ATDoA). Adaptive Time Difference of Arrival (ATDoA) The arrival time of a signal makes it simple to estimate the time difference between two signals at a node. The storage node-to-receiver approach is the one that has been proposed for determining the node's remaining power for each information exchange. In the subsequent UWSN transmission, the sink allocates the node's higher power when the node reduces its power. Extending the packet's lifetime and shortening the time it takes to reach its destination boosts system performance. It outperforms the compared algorithms regarding node lifetime, dead node count, power consumption, and propagation delay. © 2023 IEEE.

关键词： Energy efficiency

来源：评论

学校读者我要写书评

暂无评论

Marine Ecosystem Monitoring: Applying Remote Sensing and AI to Track and Predict Coral Reef Health

引用

Remote Sensing in Earth Systems Sciences 2024年第4期7卷 486-499页

作者： Veeranjaneyulu, Rayavarapu Govindarajan, Dinesh Subramanian, Chandramohan Devi, Deva Uma Banerjee, Sudipta Edpuganti, Sai Krishna Upadhyay, Shrikant Department of Computer Science & amp Engineering PACE Institute of Technology and Sciences Andhra Pradesh Ongole India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur (KTR) Chennai India Department of ECE SCSVMV Deemed University Tamil Nadu Kanchipuram India Department of Computer Science and Engineering Gayatri Vidya Parishad College of Engineering (A) Andhra Pradhesh Visakhapatnam India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) (SIU) Pune Campus Pune India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur Dist. India Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India

Coral reefs are essential ecosystems, supporting a diverse range of marine life and offering considerable ecological, economic, and cultural benefits. However, they face increasing threats from climate change, pollution, and other human activities. Thus, effective monitoring and management of coral reefs are crucial for their conservation and sustainability. In this study, we employed a hybrid model, HCNN-SVM, which combines convolutional neural networks (CNNs) for feature extraction and support vector machines (SVMs) for classification. We utilized a coral-reef dataset from Kaggle, containing images from CoralNet and the Moorea Coral Reef Long Term Ecological Research (MCR LTER). To enhance image quality and ensure accurate reflectance values, we applied radiometric, geometric, and water column corrections during preprocessing. Feature extraction involved the use of spectral indices such as the Coral Bleaching Index (CBI) and Normalized Difference Vegetation Index (NDVI) to detect healthy and stressed corals, along with texture analysis to differentiate substrates. The model was trained on this diverse dataset, which captures various environmental conditions and reef types, achieving an accuracy of 98.55%. Cross-validation methods were employed to evaluate model performance, ensuring robustness and generalizability. The HCNN-SVM model demonstrated high accuracy in identifying and mapping coral reef components, making it a powerful tool for monitoring coral reef health and supporting conservation efforts. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks (CNNs) Coral Bleaching Index (CBI) Coral reef monitoring Feature extraction HCNN-SVM Moorea Coral Reef Long Term Ecological Research (MCR LTER) Normalized Difference Vegetation Index (NDVI) Support vector machines (SVMs)

来源：评论

学校读者我要写书评

暂无评论

3D Image Reconstruction and Processing for Augmented and Virtual Reality Applications: A computer Generated Environment 2

3D Image Reconstruction and Processing for Augmented and Vir...

引用

2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Baskar, Radhika Krishnammal, P. Muthu Aeron, Anurag Ali, Syed Sumera Leonid, T. Thomas Arun, M.R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India School of Electronics Engineering VIT-AP University Andhra Pradesh Amaravati India School of Computing Dit University Computer Science and Engineering Dehradun India Csmss Chh Shahu College of Engineering Electronics and Computer Engineering Department Maharashtra Aurangabad India Ece Dept Kcg College of Technology Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Science and Technology Department of Ece Chennai India

ISBN: (纸本)9798350369960

New technologies like AR and VR are changing our online and offline experiences. Augmented and virtual reality applications need 3D picture reconstruction and processing. In augmented and virtual reality, this abstract explains 3D image reconstruction and processing concepts, techniques, and effects. Creating engaging AR and VR experiences starts with 3D data. Depth sensors, stereoscopic images, and photogrammetry are essential for accurate 3D reconstructions of real-world objects and settings. These data streams support spatial cognition and interaction in AR/VR. Reconstruction technologies like point cloud and mesh reconstruction are essential for turning 3D data into models. Virtual features that complement or replace the actual environment in AR experiences or build fully fabricated VR settings are based on these models. By applying 2D textures to 3D geometry, 'texture mapping' enhances scene realism. Realtime augmented and virtual reality processing requires effective rendering, tracking, and physics simulation algorithms. Rendering engines provide beautiful, responsive experiences, while SLAM lets users explore and interact with virtual information in real life. Occlusion culling and level-of-detail rendering are two optimisation methods needed for smooth performance on high-end VR systems and mobile AR devices. Virtual reality is defined by 3D object interaction and control. Natural and engaging interfaces are created using gesture detection and real-time physics simulations. AR and VR software developers encounter new challenges with each iteration. AR requires accurate spatial mapping, dependable tracking, and simple user interfaces to blend the virtual and real worlds. In contrast, virtual reality (VR) immerses users in realistic computer settings. Portability across operating systems is crucial for greater adoption. Developers must optimise programmes for several hardware platforms to reach the most people. © 2023 IEEE.

关键词： 3D image Augmented reality Reconstruction Virtual Reality

来源：评论

学校读者我要写书评

暂无评论

An Effective Hand Gesture Recognition using Convolutional Neural Network with Long Short-Term Memory

An Effective Hand Gesture Recognition using Convolutional Ne...

引用

2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Sunil, G. Saranya, A. Duraimurugan, N. Dineshkumar, R. Ramachandra, A.C. Sr University School of Computer Science & Artificial Intelligence Warangal India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Department of Ece Thandalam India Engineering Nitte Meenakshi Institute of Technology Department of Electronics and Communication Bengaluru India

ISBN: (纸本)9798350313413

A Hand Gesture (HG) constitutes a mode of nonverbal communication or non-vocal interaction, where discernible bodily movements convey specific messages, either independently or in combination with spoken language. Gesture recognition techniques are employed to create systems capable of transmitting information among individuals with disabilities for operating devices. In this paper, we propose the use of Electromyography (EMG) signals for classifying hand gestures through Employing both a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) in combination is being used. The main problem lies in the noise signals produced by the EMG sensor. In this work, we employ a Low Pass Filter for preprocessing the signal images, followed by feature extraction using the CNN model. To reduce the high-dimensional signal and normalize the features for the classification process, we utilize LSTM. This experiment was conducted using the Ninapro DB1 dataset. This model successfully addresses the previous issues and attains an overall accuracy of 95.56%, Precision 91.90%, Recall 89.50%, and F1 score 85.50% 91.90%, then the accurate classification compares to other existing models like Bidirectional Convolutional Gated Recurrent Unit, and Deformable convolutional network models. © 2023 IEEE.

关键词： Convolutional Neural Network Electromyography Hand Gesture Long Short-Term Memory Low Pass Filter

来源：评论

学校读者我要写书评

暂无评论

Study of Whisker Growth from SAC0307-SiC Composite Solder Alloy 46

Study of Whisker Growth from SAC0307-SiC Composite Solder Al...

引用

46th International Spring Seminar on electronics Technology, ISSE 2023

作者： Choi, Halim Skwarek, Agata Hurtony, Tamás Byun, Jaeduk Illés, Balázs Budapest University of Technology and Economics Faculty of Electrical Engineering and Computer Science Department of Electronics Technology Budapest Hungary Lukasiewicz Research Network - Institute of Microelectronics and Photonics Department of Microelectronics Kraków Poland Dankook University College of Science & Technology Department of Physics Cheonan Korea Republic of

ISBN: (纸本)9798350334845

In the current study, tin (Sn) whisker growth was observed on the surface of the SAC0307-SiC composite solder joint. A commercial SAC0307 solder alloys were reinforced with SiC nanophases to produce composite solder joints. SiC nanoparticles and nanowires were mixed with SAC0307 solder paste using the ball milling method. The solder paste was deposited by stencil printing on the test PCBs, and 0805 chip resistors were soldered by IR reflow method. The solder joints facilitated the growth of the Sn whisker through accelerated lifetime tests. The samples were observed by scanning electron microscopy (SEM) every 1000 hours of the test. On the SiC composite solder joints, Sn whiskers and large corrosion spots were observed after 1000 hours of the THB test. In the case of SAC0307 reference samples, only minor corrosion signs were found after 1000 hours of the THB test. The main finding was that the addition of SiC nanophases promoted Sn whisker growth and decreased the corrosion resistance of the composite solder. © 2023 IEEE.

关键词： Silicon carbide

来源：评论

学校读者我要写书评

暂无评论

Leveraging Deep Autoencoders for Security in Big Data Framework: An Unsupervised Cloud Computing Approach 2

Leveraging Deep Autoencoders for Security in Big Data Framew...

引用

2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Roy, Sourav Nijaguna, G.S. Shankar, Smitha Sharath Adnan, Myasar Mundher Umaeswari, P. Department of Electronics and Communication Engineering Bangalore India S.E.A. College of Engineering and Technology Department of Information Science and Engineering Bangalore India Nitte Meenakshi Institute of Technology Dept of Ece Bengaluru India The Islamic University Najaf Iraq R.M.K. Engineering College Department of Computer Science and Business Systems Tamil Nadu Kavaraipettai India

ISBN: (纸本)9798350382952

Abnormalities recognition in bank transaction big data is the number one issue for stability of financial security system. Due to the rate digital transactions are increasing it is vital to have effective ways. Encryption with deep autoencoder model should be explored as it involves trained neural networks that learn such patterns from the complex transaction data. The following paper demonstrates application of anomaly detection using deep autoencoders in the banking big data transactions. It focuses on the theoretical bases, network design, preparedness and the testing measures for deep autoencoders. On the other hand, it solves problems such as high dimensionality and imbalanced dataset. This research paper shows deep autoencoders effectiveness in deep learning and how the network identifies different fraudulent big data transactions, money laundry and unauthorized access. It also encompasses recent developments of cloud environments and future methods using deep autoencoders including the fact that constant search for new possible solutions is a must. The insights delivered contribute to the discourse in financial security community, which incorporates researchers, practitioners, and policymakers involved in anomaly detection in cloud. © 2024 IEEE.

关键词： Cryptography

来源：评论

学校读者我要写书评

暂无评论

Twitter Sentiment Analysis using Ensemble Based Deep Learning Techniques for Analyzing COVID-19 Tweets

Twitter Sentiment Analysis using Ensemble Based Deep Learnin...

引用

2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Kumar, Bura Vijay Monica, K.M. Prabhakar, S. Perumal, Senthil Ramachandra, A.C. Sr University School of Computer Science and Artificial Intelligence Warangal India Vit School of Computer Science and Engineering Chennai India S.A Engineering College Department of Mathematics Chennai India SRMIST-Ramapuram Department of Ece Chennai India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

The COVID-19 is a transportable disease which is rapidly spread and numerous peoples are endures and died from this disease. This paper proposed an ensemble based deep learning techniques for examining COVID-19 tweets. The COVID19 Tweets dataset is utilized in this research which consist of 3100 tweets. The preprocessing is accomplished by including the activities like stemming, tokenization and stop words removal. The preprocessed data features are extracted through Team Frequency-Inverse Document Frequency (TF-IDF). Then, it classified through ensemble classifiers such as Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Bidirectional RNN (BRNN). The model performance was evaluated through accuracy, precision, recall, sensitivity and f1-score. The obtained result shows that the ensemble classifier attains high accuracy of 0.985, precision of 0.982, recall of 0.971, specificity of 0.974 and f1-score of 0.978 which is comparatively higher than existing techniques like Extra Tree Classifier (ETC) and Heterogeneous Support Vector Machine (H-SVM). © 2023 IEEE.

关键词： Ensemble Classifier Gated Recurrent Unit Long Short-Term Memory Recurrent Neural Network Sentiment Analysis Twitter

来源：评论

学校读者我要写书评

暂无评论

Three-Dimensional Convolutional Neural Network for Wheat Rust Disease Classification 2

Three-Dimensional Convolutional Neural Network for Wheat Rus...

引用

2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Jagadeeshan, N. Nagendar, Y. Swarnalatha, K. Al-Attabi, Kassem Ramachandra, A.C. Electronics and Communication Engineering Department Telangana Hyderabad India School of Computer Science and Engineering Sr University Telangana Warangal India Maharaja Institute of Technology Department of Artificial Intelligence and Data Science Thandavapura India The Islamic University Najaf Iraq Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India

ISBN: (纸本)9798350382952

In worldwide, the wheat is a significant crop which generates main source of food for numerous peoples. Through, the development of crop productions is vulnerable through various diseases such as bacterial, viral and fungal infections. These type of disease can cause important damage in crops which leads to diminish the yield production and grain quality. This paper proposed a Three-Dimensional Convolutional Neural Network (3D-CNN) for wheat rust disease classification that learns to recognize the pattern and structure of rust disease using convolution filter layers. The CGIAR dataset is used which contains 1486 images and it is pre-processed by gaussian filter which reduces the noise and smoothens the image. Then, the Discrete Wavelet Transform (DWT) is used for feature extraction which works in discrete timespan that outputs in low computational cost. Then, 3D-CNN is used for the classification of wheat rust disease. The performance of 3D-CNN is estimated by accuracy, recall, f1score and precision. The 3D-CNN attains accuracy 99.83%, recall 98.89%, f1score 98.81 % and precision 98.79% when compared to existing techniques like GNet+FERSPNET-50 and Few-shot learning based EfficientNet. © 2024 IEEE.

关键词： Discrete wavelet transforms

来源：评论

学校读者我要写书评

暂无评论

Decoding Financial Markets: Unleashing the Power of Bi-LSTM in Sentiment Analysis for Cutting-Edge Stock Price Prediction

Decoding Financial Markets: Unleashing the Power of Bi-LSTM ...

引用

2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Aluvala, Srinivas Balamurugan, V. Jeyasudha, J. Kumar, R. Dinesh Pradeep Kumar, S. Sr University School of Computer Science & Artificial Intelligence Warangal India Sathyabama Institute of Science and Technology Department of Ece Chennai India Srm Insitute of Science and Technology Department of Computational Intelligence Chennai India Saveetha School of Engineering Department of Ece Thandalam India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

Predicting stock prices accurately is a challenging task due to various influencing factors. This paper addresses the need for precise stock price prediction by presenting an enhanced sentiment analysis methodology using deep learning, particularly focusing on the Bi-LSTM model. The challenge of accurate sentiment analysis in stock prediction is overcome through advanced data preprocessing techniques tailored to maintain sentiment context effectively. The proposed Bi-LSTM (Bidirectional long Short-Term Memory) and CNN (Convolutional Neural Network) model demonstrates superior performance in classifying sentiments with high precision, recall, F1-score, and accuracy. The numerical validation demonstrates the effectiveness of the Bi-LSTM model in achieving a balance across various metrics, highlighting its potential for practical implementation in stock market prediction. The Bi-LSTM and CNN model achieves remarkable precision 90.00%, recall 90.00%, F1-score 90.00%, and accuracy 91.00%. In comparison to established methods, such as Multivariate Sequential Long Short-Term Memory Autoencoder (MSLSTMA), long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), the Bidirectional long Short-Term Memory (Bi-LSTM) model exhibits its competitive edge in stock price prediction as supported by evaluation metrics. The results emphasize the importance of tailored data preprocessing and deep learning techniques, showcasing the efficiency of the proposed methodology in enhancing stock price prediction accuracy while maintaining high percentages of precision, recall, and F1-score, along with impressive accuracy. © 2023 IEEE.

关键词： Bidirectional long Short-Term Memory Convolutional Neural Network Deep Learning Sentiment Analysis Stock Price Prediction

来源：评论

学校读者我要写书评

暂无评论

LiFiData: A Secured Data Communication over Defense Fields by using Intelligent Visible Light Assisted Transmissions

LiFiData: A Secured Data Communication over Defense Fields b...

引用

2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Chithra, L. Padma, S. Farzhana, I. Saravanan, L. Krishnakumari, S. S.A.Engineering College Department of ECE Chennai India Sona College of Technology Department of EEE Salem India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India PERI Institute of Technology Department of ECE Chennai India Loyola ICAM College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350319200

The electronic industry is currently experiencing a transformation due to the rise of solid state devices. Light-Emitting-Diode (LED) are the quintessential solid-state gadget example. LEDs' are a viable alternative to both incandescent and fluorescent lighting. They produce little to no heat and can save you money. As an added bonus, LEDs have a far longer lifespan than conventional light bulbs. LEDs generate higher-quality light in terms of color temperature than both incandescent and fluorescent bulbs. In addition, they can rapidly cycle across a wide range of light intensities. This system must be safe, cheap, and environmentally benign in order to successfully initiate electronic communication utilizing visible light. The suggested system uses a Arduino microcontroller in the transmitting portion, where a high-brightness LED light serves as the channel for communication and a transistor, BC 547, is employed to transform 5v to *** a microcontroller receives data from a computer, it decrypts the information by first converting it to binary, and only then sending it to the led as a digital pulse. The lightbegins to blink as data is transmitted to the receiver;the photo diode begins to sense light, generating electric pulses;the amplifier processes these pulses into data;the data reaches the Arduino, where it can be viewed in the program's serial monitor. For this transmission, direct line-of-sight is essential;however interference from other sources can improve the results in a dark environment. In the lab, LiFi has reached very high speeds, and it also outperforms Wi-Fi in terms of bandwidth, efficiency, availability, and security. This study provides a clear, experimental perspective on the benefits of LiFi technology as well as its potential future applications. However, the hybrid model that incorporates the WiFi and LiFi technologies is expected to see significant growth in demand in the telecommunications industry. © 2023 IEEE.

关键词： Communication LED LiFi Security Visible Light Data Transmission VLC WiFi Wireless Communication

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：