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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Kumar, Gaurav Kumar, Ankit Singhal, Manoj Singh, Kamred Udham Kumar, Lalan Singh, Teekam Gla University Department of Computer Engineering & Applications UP Mathura India Gl Bajaj Institute of Technology and Management Department of Information Technology India School of Computing Graphic Era Hill University Dehradun India Gl Bajaj Institute of Technology and Management Department of Master of Computer Application India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350323917

Agricultural productivity plays a vital role in the economy, and disease detection in plants is crucial for the agriculture sector as plant diseases are widespread. Failure to take appropriate care can lead to serious consequences for plant quality, quantity, or productivity. For instance, a disease called little leaf disease has been found in pine trees in the United States, which can severely affect plant health. Automated methods for detecting plant diseases are useful as they reduce the need for manual inspection in large crop fields, and they can identify disease symptoms as soon as they appear on plant leaves. This paper presents an algorithm for image segmentation for the automatic identification and classification of plant leaf diseases. It also discusses various disease classification methods that can be used for plant leaf disease detection. Genetic algorithms are used for image segmentation, which is a crucial aspect of disease detection in plant leaves. The paper focuses on identifying the Powdery Mildew disease in organic grape products, which can cause significant damage if not detected in time. The accurate identification and classification of diseases from leaf images are crucial for preventing significant losses in output and agricultural inputs. The proposed process can help improve the accuracy and efficiency of disease detection in plants and lead to better agricultural productivity. © 2023 IEEE.

关键词： Fuzzy logic

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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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A Real-Time Road Congestion-Based Hybrid Approach for Finding the Optimized Route 4th

A Real-Time Road Congestion-Based Hybrid Approach for Findin...

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4th International Conference on Microelectronics, Computing and Communication Systems, MCCS 2019

作者： Rajak, Biru Tripathi, Aprna Kushwaha, Dharmender Singh Department of Computer Science and Engineering MNNIT Allahabad Prayagraj India IET Department of Computer Engineering and Application GLA University Mathura India

ISBN: (纸本)9789811555459

As cities get overcrowded, commuters are spending most of their time in traveling and insane amount of time gets wasted in unnecessary traffic jams. A large number of countries are adopting various traffic management techniques to deal with traffic congestion. The objective of this research article is to develop a smart and intelligent road navigation system in a cost-effective way that dynamically adapts to the congestion of the route, and hence contribute to the development of smarter cities. Besides finding shortest route, congestion factor is computed to find an optimized route from source to destination. Congestion factor has been evaluated through numerical modeling in dense traffic scenario. Experiment results reveal that the travel time of a vehicle is reduced by up to 33% if it follows optimized route obtained from proposed approach. This can also be helpful for the emergency vehicles to identifying the best route in any emergency conditions. The proposed analytical model is further extended to identifying the optimal route for formation of on-demand green corridor in emergency situation. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Traffic congestion

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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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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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Anomaly Detection Technology for Cloud Manufacturing System based on Data Denoising and Feature Optimization 19

Anomaly Detection Technology for Cloud Manufacturing System ...

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19th IEEE International Conference on Networking, Sensing and Control, ICNSC 2022

作者： Zhao, Longbo Li, Bo Hu Jia, Juan Wu, Tong School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beijing Institute of Computer Technology and Application Department of Intelligent Manufacturing Beijing100854 China

ISBN: (纸本)9781665472432

Aiming at the problem that the traditional anomaly detection method based on threshold cannot effectively detect sensor numerical anomalies in cloud manufacturing system, this work proposes a new method to detect some sensor numerical anomalies form the industrial control system. It is the central part of a cloud manufacturing system. Firstly, this work constructs a Savitzky-Golay (S-G) filter to reduce data noises. Furthermore, an extreme learning machine based on genetic algorithm (GA-ELM) model is proposed to detect sensor numerical anomalies form the industrial control system. The genetic algorithm (GA) is used to reduce feature dimensions from 51 to 10 and the extreme learning machine algorithm (ELM) is used for classification to achieve the purpose of anomaly detection. Finally, using the public dataset called Secure Water Treatment (SWaT), the classification accuracy is 98.96%. It shows a better performance of the proposed method. © 2022 IEEE.

关键词： Genetic algorithms

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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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A Novel Computation Offloading under 6G LEO Satellite-UAV-based IoT 3

A Novel Computation Offloading under 6G LEO Satellite-UAV-ba...

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3rd International Conference on Computation, Automation and Knowledge Management, ICCAKM 2022

作者： Jaiswal, Kriti Dahiya, Anshul Saxena, Surabhi Singh, Virendra Singh, Archita Kushwaha, Arun University of Lucknow Department of Computer Science Lucknow India Mangalmay Institute of Engineering and Technology Department of Computer Science and Engineering Greater Noida India PES University Department of Computer Applications Bengaluru India Babasahab Bhimrao Ambedkar University Department of Computer Engineering Lucknow India Babu Banarasi das University Department of Computer Application Lucknow India Babu Banarasi das University Department of Computer Applications Lucknow India

ISBN: (数字)9781665453196

ISBN: (纸本)9781665453196

The Internet of Things (IoT) has become a fundamental and omnipresent component of modern life, such as the infrastructure of intelligent transport systems and smart cities. All the next-generation IoT systems necessitate cognitive data processing that could be accomplished promptly through satellite communication. The edge server performs data processing in satellite communication, which is predicted to minimize service delay. However, in prevailing satellite technology, substantial transmission latency, significant route loss, and the resource, as well as energy limitations of IoT devices, all pose challenges to the rigorous service demands for latency and throughput in the upcoming 6G age. To tackle these challenges, 6G IoT will rely heavily on technologies such as space-Air-ground integrated networks (SAGINs), edge computing, deep learning, machine learning, blockchain, and energy harvesting. Combining communication technology with Unmanned Aerial Vehicle (UAV) has recently been identified as one of the potential strategies in the future network. UAVs may provide a variety of services;however, the UAV's onboard computational resources and battery backup are constrained. The Researchers in this article examine satellites as well as UAVs to deliver wireless IoT device cloud computing and edge computing services, respectively. Grounded IoT devices can conduct operations locally or offload them to UAV-based edge servers as well as distant cloud servers via satellites. The major emphasis is on the computational offloading dilemma and considering deep learning methods to optimize the task success rate considering energy dynamics and channel circumstances. The suggested AI-based offloading approach outperformed conventional methods, indicating that it can determine the most energy-efficient offloading policy. © 2022 IEEE.

关键词： Satellites

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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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