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3rd IEEE International Conference of Electron Devices Society Kolkata Chapter, EDKCON 2024

作者： Ahamed, Sk. Sohel Pal, Moumita Chakraborty, Pritam Neogi, Biswarup Maity, Niladri Das, Reshmi Future Institute of Engineering and Management Dept. of electronics and communication kolkata India JIS College of Engineering Dept. of electronics and communication Kalyani India Institute of Engineering and Management Dept. of computer science and application Kolkata India NITTTR Dept. of technical education Kolkata India Mizoram University Dept. of electronics and communication Mizoram India

ISBN: (纸本)9798350374643

Sealed capacitive micromachined ultrasonic transducer (CMUT) mostly consists of two parallel electrodes separated by a structural layer of silicon nitride (Si3N4) and a vacuum cavity. Biasing the transducer element generates electrostatic force of attraction where the lines in the field bulge at the boundaries of the layers contributing to fringing effects. These fringing capacitances result in overall increment of the device capacitance. To capture this effect a very simple model of Landau and Lifschitz is incorporated, where the analytical model outputs are well matched with commercially available PZFlex FEM simulated results. Because silicon carbide (SiC) has a higher Young's modulus and lower residual stress than Si3N4, it is used as a structural material in the CMUT. The improvement in device capacitance is approximately 13.1% through adjustments in the thickness of gaps and insulation. However, factoring in the relative dielectric constant and the electrode radius, this improvement reduces to around 7.9%. Additionally, introducing a high-k insulating layer enables the upper electrode to be structured on the lower face of the SiC layer. This modification leads to an augmentation in the device's total capacitance. This article also examines HfO2 materials of varied thicknesses. © 2024 IEEE.

关键词： Silicon nitride

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Accurate Selection of proper network system and concern Cloud Services through computing

Accurate Selection of proper network system and concern Clou...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Amar S. Verma Rajat Kumar Department of Computer Science Engineering Vivekananda Global University Jaipur India Department of Master of Computer Application Chandigarh School of Business

The significant role that networking plays in cloud service provisioning necessitates federated selection of network and cloud services in order to offer the service performance required by distinct applications. In this paper, we describe a strategy based on performance for federated selection of network and cloud services to address the new service selection challenges provided by the Software Defined Cloud Environment (SDCE). The main contribution of this paper is a general interest profile material to different applications, an examination strategy for assessing the reachable presentation of composite organization cloud administrations, and an arrangement for combined choice of organization and cloud administrations in light of execution assessment. The strategy and innovation fostered the critical job that systems administration plays in cloud administration provisioning requires united choice of organization and cloud administrations to offer the help execution expected by particular applications. In this paper, we depict a methodology for execution based federated choice of organization and cloud administrations to address the new help determination challenges given by the Software Defined Cloud Environment (SDCE). The principal commitments of this paper are an examination plan for multiple applications, a general interest profile, and assessing the feasible exhibition of composite organization cloud administrations, and an arrangement for united determination of organization and cloud administrations in light of execution assessment.

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User Mobility Prediction in 5G Networks Using Recurrent Neural Networks

User Mobility Prediction in 5G Networks Using Recurrent Neur...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Shrestha, Anubhav Sharma, Vikalp Hussein, Layth Aishwarya, M. Satyanarayana, A.N. Saimanohar, T. Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India Ies College of Technology Department of Computer Science & Engineering M.P. Bhopal India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Sreenidhi Institute of Science and Technology Ece Department Hyderabad India Saveetha Institute of Medical and Technical Sciences Department CSE-AI Tamilnadu Chennai India

ISBN: (纸本)9798350390759

The use of new possibilities introduced by 5G networks also creates new problems and concerns, specifically in the field of user mobility in wireless communication systems. In this paper, the Authors investigate the effectiveness of Recurrent Neural Networks (RNNs) in the mobility prediction context in order to exploit the temporal component in the users movement data for improved prediction performance. To drive this process, the input datasets are obtained from the GeoLife GPS Trajectories dataset, then follow the data pre-processing steps, constructing an LSTM-based RNN model, and using Python with TensorFlow and Keras. From the analyses, we detected that the accomplishment of using LSTM and hybrid RNN-CNN is significant and higher than the traditional approaches of EN, RFM, and CL. The test results demonstrate that the LSTM model can reduce the MAE and RMSE values by 19. 49% and 14. 82%, respectively. However, the execution of these models has some issues that include compute time, instantaneous prediction that is needed in many real-world applications, and case. Such problems can be solved with the help of such approaches as edge computing and distributed processing. Besides, it is pivotal to put measures in place to maintain the confidentiality and security of user information. This paper highlights the applicative potential of RNNs in redesigning user mobility prediction in 5G networks, and thus helping toward a better ownership of the network players and assets. © 2024 IEEE.

关键词： 5G mobile communication systems

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Transformative Insights: Harnessing Machine Learning for Big Data Analytics

Transformative Insights: Harnessing Machine Learning for Big...

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

作者： Chandradeep Bhatt Divas Sagta Rahul Chauhan Shiv Ashish Dhondiyal Sanjay Sharma Dev Baloni Computer Science and Engineering Graphic Era Hill University Dehradun India Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Computer Application Department SGRR Univeristy Dehradun India Computer Science and Engineering Quantum University Dehradun India

Like oil in the past, data has become a very important resource in the modern world. But just as oil must be refined in order to be valued, data must also be handled and evaluated in order to yield worthwhile insights. Big Data analytics is a discipline that has developed in response to the expanding demand for efficient data analysis and interpretation. In order to find hidden patterns, connections, and insights, it involves examining huge, complicated datasets utilizing cutting-edge tools and methods. Big Data analytics' importance stems from its capacity to decipher the secrets contained in any dataset and glean insightful information. When appropriately analyzed and addressed, these insights can produce beneficial results. Big Data Analytics is extremely important in the context of consumer behavior. This technology is being used by marketers more frequently to analyze customer behaviors more accurately. Marketers can better understand their target market's preferences, demands, and buying habits by analyzing customer behavior. With the use of this information, marketers can make more informed choices and modify their marketing plans to specifically address customer needs. In the end, this may help to improve firm performance outcomes including boosted sales, enhanced client contentment, and general expansion. The aforementioned study seeks to offer a thorough overview of Big Data technology, including its goal, importance, and potential applications. It also looks into how Big Data technologies affect customer behavior and overall business effectiveness. The goal of this study is to shed light on these elements and the potential advantages and consequences of applying big data analytics across different fields.

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A Robust DL Approach for Detection of Invasive Ductal Carcinoma in Whole Slide Images using DenseNet169

A Robust DL Approach for Detection of Invasive Ductal Carcin...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Menaka, C. Sharma, Sunil Naval, Preeti Brar, Khushmeen Anand, Rohit Alabdeli, Haider Pant, Shivani Department Of Computer Science And Information Technology Bangalore India Vivekananda Global University Department Of Computer Science & Application Jaipur India Maharishi University Of Information Technology Maharishi School Of Engineering & Technology Uttar Pradesh India Chandigarh University Department Of Electronics And Communication Engineering Punjab Mohali India G.B Pant Okhla-1 Campus Department Of Ece New Delhi India The Islamic University Najaf Iraq Uttaranchal University Uttaranchal Institute Of Technology Uttarakhand India

ISBN: (纸本)9798350366846

The present research introduces a deep learning method for automatically recognizing and evaluating tissue areas that correspond to invasive ductal carcinoma (IDC) in whole slide images (WSI) of breast cancer (BCa). Learn-from-data techniques which utilize computational simulations of the learning process are called deep learning approaches. It has been demonstrated that these methodologies outperform conventional methods in resolving the challenging issues in a number of domains, particularly object and speech recognition. The main reason why making an invasive breast cancer evaluation is an exhausting and laborious process is that, it necessitates a pathologist to scan a huge number of benign regions with the aim to determine the spots that are malignant. Effectively characterizing IDC in WSI is a prerequisite for evaluating the severity of tumors and predicting patient prognosis. In order to support diagnosis, the DL approach in this study draws out many convolutional neural networks (CNNs) for perceptible semantic examination of tumor locations. A WSI database of 162 patients with IDC diagnoses is exploited to assess the technique, and 170 slides were chosen for training. The objective of the experimental analysis was to assess the accuracy of the classifier in identifying IDC tissue areas in WSI. A fusion of optimization techniques namely Best First and Evolutionary Search has been employed for feature reduction and improvement. When using DenseNet169, our approach produced the highest quantitative outcomes for the computerized identification of IDC sections in WSI with regard to accuracy 87.46% in comparison with an approach using EfficientNetB0 and ML classifiers for invasive tumor categorization employing Random Forest, BayesNet, SVM, Naïve Bayes and Decision Tree with accuracy of 80.65%. © 2024 IEEE.

关键词： Diagnosis

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Language Identification—A Supportive Tool for Multilingual ASR in Indian Perspective 4th

Language Identification—A Supportive Tool for Multilingual ...

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4th International Conference on Advanced Computing and Intelligent engineering, ICACIE 2019

作者： Swain, Basanta Kumar Mohanty, Sanghamitra Department of Computer Science & Engineering Government College of Engineering Kalahandi Bhawanipatna766002 India Department of Computer Science & Application Utkal University Bhubaneswar51004 India

ISBN: (纸本)9789811563522

In this research paper, we have engineered a multilingual automatic speech recognition engine in Indian perspective by employing language identification technique. Our motherland, India is treated as land of many tongues. Our vision is leading to develop a system that would aid in man–machine communication in Indian spoken languages. We have developed two vital models, namely language identification and speech recognition using array of pattern recognition techniques, viz. k-NN, SVM and HMM. We have tackled both LID and multilingual ASR tasks over three Indian spoken languages, namely Odia, Hindi and Indian English. We have experimented over short as well as long durational isolated spoken words for LID task purpose. Finally, we have integrated LID module with multilingual ASR in order to transcribe the words in desired spoken language. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Speech recognition

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WheatNet: Attentional Path Aggregation Feature Pyramid Network for Precise Detection and Counting of Dense and Arbitrary-Oriented Wheat Spikes

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2024年第2期2卷 606-616页

作者： Jiao, Lin Liu, Qihuang Liu, Haiyun Chen, Peng Wang, Rujing Liu, Kang Dong, Shifeng Anhui University School of Internet Hefei230031 China Chinese Academy of Sciences National Engineering Research Center for Agro-Ecological Big Data Analysis and Application Hefei230031 China Chinese Academy of Sciences Institute of Intelligent Machines Hefei Institutes of Physical Science Hefei230031 China University of Science and Technology of China Hefei230031 China Anhui University National Engineering Research Center for Agro-Ecological Big Data Analysis and Application Information Materials and Intelligent Sensing Laboratory of Anhui Province School of Internet Hefei230601 China Anhui University Institutes of Physical Science and Information Technology Hefei230601 China Chinese Academy of Science Institute of Intelligent Machines Hefei Institutes of Physical Science Hefei230031 China University of Sheffield Department of Computer Science SheffieldS10 2TN United Kingdom Institute of Dataspace Hefei230093 China

Achieving the precise and real-time detection of wheat spikes play a crucial role in wheat growth monitoring for precision agriculture community. Machine-learning methods are commonly introduced to automatically detect and count the wheat spikes, which need carefully selected hand-crafted feature descriptors, leading to time-consuming and poor performance. The deep learning has become a promising technology for the accurate detection wheat spikes, owing to its powerful ability of feature extraction. However, the obtained wheat spike images from UAV still have serious overlap, dense distribution, various orientations, and large aspect ratios, leading to poor performance of recent wheat spike detection method. To address the demand of precise and fast detection and counting of wheat spike with dense distribution and arbitrary-orientation, a novel deep learning-based method, WheatNet, has been proposed. The attention mechanism has been introduced the process of feature fusing to highlight the important features of wheat spike as well as inhibit the useless information. Additionally, to optimize the parameters of the network, a loss function with soft dynamic label assignment is adopted to reduce the number of low-quality matches, which provides significant performance gains over other wheat spike detectors. Furthermore, to achieve the precise detection of wheat spike with multi-orientations, a large-scale oriented wheat spike dataset has been constructed, named RoWheat, including 900 images and 50419 annotations with dense distribution and various orientation. Experimental studies demonstrate that the proposed WheatNet achieves a recall of 99.7% and mAP of 91.8%, showing its promising performance gain compared to other state-of-the-art methods. © 2023 IEEE.

关键词： Image annotation

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Emsd-detr:efficient small object detection for UAV aerial images based on enhanced RT-DETR model

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The Journal of Supercomputing 2025年第9期81卷 1-33页

作者： Zhang, Chaofeng Yang, Jingmin School of Computer Science Minnan Normal University Zhangzhou China Key Laboratory of Data Science and Intelligence Application Minnan Normal University Zhangzhou China Department of Electronic Engineering National Taipei University of Technology Taipei China

UAV aerial image object detection studies are affected by imbalances in large fields of view, changes in scale and viewing angle, object aggregation occlusion, and changes in lighting and weather conditions, resulting in poor performance of small object detection algorithms. In this paper, we propose an Efficient Multi-scale Drone DETR (EMSD-DETR), an object detection model for UAV aerial images that aims to overcome specific challenges. To mitigate the loss of information about small objects due to the increase in the number of layers of the convolutional neural networks, the FRE-Block is proposed. Furthermore, we propose a small object sensitive pyramid for cross-scale feature fusion that effectively fuses local spatial coordinate information with global contextual information to avoid missing details of different feature layers. Finally, the Focaler-Shape-IoU is introduced that incorporates consideration of the shape and scale of the bounding box to expedite convergence and enhance detection precision for difficult samples. Experiments on the VisDrone dataset demonstrate that the EMSD-DETR model improves 1.8%, 2.9%, 2.7%, and 1.2% with regard to precision, recall, mAP $$_{50}$$ , and mAP $$_{50:95}$$ , respectively, while decreasing the number of parameters by 8.2%, as compared to RT-DETR. In addition, generalizability experiments on the TinyPerson and NWPU VHR-10 dataset prove the validity of the EMSD-DETR model.

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Landslide Monitoring Using IoT System with Cloud Platform

Landslide Monitoring Using IoT System with Cloud Platform

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IEEE UP Section Conference on Electrical computer and Electronics (UPCON)

作者： Pradeep Singh Rawat Prateek Kumar Soni Aryan Goel Gurmeet Kaur Computer Science & Engg. Department DIT University Dehradun India Computer Engineering & Application GLA University Mathura India

The natural disasters poses the losses in the disaster prone area. Landslides pose great socio-economic threats to regions which geography of the location favors it. The hill states of the India are more prone to the landslides due to the heavy rain fall other environmental challenges. The researchers found that ninety-nine percent of landslides induced due to heavy rain falls. The severity of disasters especially landslides is gradually increasing day by day in scale and frequency, resulting in widespread loss of human lives and infrastructure of the nation. The detection, monitoring and mitigation of the landslides in hill states is the major concern. The mitigation and monitoring of the effects of landslides can be performed using technology integration. This article presented an innovative landslide monitoring system. It is based on the IoT protocols based monitoring system consisting of cloud platform which provides instant alerts of upcoming landslides on the basis of real time data set collected inside the IoT cloud platform. As part of this monitoring system, landslide data is sent directly to the civil protection department and notified to the local population for the awareness and future precautionary safety measures. The results and discussions section shows the real time data visualization of the landslide inducting parameters, including metrological, geophysical and hydrological parameters.

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Adaptive Modulation and Coding in 5G Networks with Deep Learning

Adaptive Modulation and Coding in 5G Networks with Deep Lear...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Gupta, Anjula Reddy, Nirmala Shnain, Ammar Hameed Revathi, K. Gopalakrishnan, S. Verma, Chetan K. Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India Ies College of Technology Department of Computer Science & Engineering M.P. Bhopal India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Hindustan Institute of Technology and Science Department of Information Technology Tamil Nadu Chennai603103 India Cvm University Mbit Gujrat India

ISBN: (纸本)9798350390759

As part of the present work, it aims to examine the use of deep learning in the improvement of AMC in 5G networks. While traditional AMC methods like the ones mentioned above are applicable to previous generations of the mobile networks, they are not optimal taking into consideration the complexities and the constantly evolving nature of 5G systems. Therefore, we suggest the use of convolutional and recurrent neural networks with the aim of employing deep learning in designing an adaptive system that is capable of predicting the optimal modulation and coding schemes given instantaneous CSI information. It covers data gathering, process modeling, and implementation on a platform designed for 5G consumer network. Thus, the improvement in the data throughput rate, the level of errors, and the degree of satisfaction, obtained through the experimental results, can confirm the optimal nature of the proposed approach. It can be seen that the deep learning-based AMC system is computationally efficient and can be embedded for real-time applications, and the performance of the system is further optimised with improving learning. To conclude, this study reveals the effectiveness of deep learning in transforming AMC in 5G networks, by offering a dependable and dynamic solution the wired and wireless telecommunications networks. In a future work more towards detailing of the model and finding new architectures for the system more refined system can be achieved. © 2024 IEEE.

关键词： 5G mobile communication systems

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