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

作者： Pawan Bhambu Gursimran Kaur Department of Computer Science Engineering Vivekananda Global University Jaipur India Department of Master of Computer Application Chandigarh School of Business

These days, the topic of cloud computing has become a very popular paradigm for providing services online. For the cloud data center to deliver cloud infrastructure services in response to user demand, a set of virtualized resources are required. In order to lower the degree as a result of resource loss and SLA violations, the necessity for optimum resource management becomes a crucial problem in cloud data centers. These issues are classified as NP-hard, and meta heuristics approaches are well suited to solving them. The article develops a multi-objective hybrid fruit fly optimization (MOHFO)-based method for SLA-conscious dynamic resource management in cloud data centers. For the fruit fly optimization technique, the Bald Eagle Search (BES) optimization behavior search capabilities. To balance resource waste and SLA breaches, the proposed solution employs dynamic consolidation and deployment of virtual machines (VMs) mechanism. The suggested technique utilizing Clouds to simulate different data center designs, using QoS measures to assess the experimental outcomes. The proposed MOHFO technique is assessed and contrasted to existing optimization strategies in terms of resource wastage, energy use, communication costs, and migration frequency in order to provide cloud computing with QoS-aware optimal resource provisioning.

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CNN-VGG16 Hybrid Model for Onion Purple Blotch Disease Severity Multi-Level Grading

CNN-VGG16 Hybrid Model for Onion Purple Blotch Disease Sever...

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Emerging Technology (INCET), International Conference for

作者： Nitish Kumar Ojha Nitin Thapliyal Manisha Aeri Vinay Kukreja Rishabh Sharma Computer Science and Application IIMT University Meerut Uttar Pradesh India Computer Science and Engineering Graphic Era Hill University Dehradun Uttrakhand India Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

The purpose of this study is to introduce a novel strategy for an automatic disease grading system that has been devised for the onion purple blotch disease which prevails in many farmers' fields. In identifying patients' level of disease the overall accuracy was about 96.7% using the hybrid Convolutional Neural Network (CNN)-VGG16 model. Image Preprocessing technologies that are advanced, Normalization as well as augmentation provide high models' reliability and permit the distinction between various degrees of disease expressions, ranging from trivial to severe. The model's accuracy, granularity, as well as its reliability to identify disease severity, are evaluated using comprehensive evaluation metrics consisting of a detailed confusion matrix and performance parameter results, including TP, FP, FN, and so on. The results relate to changing detection technologies and a higher emphasis on enhancing data quality as well as adjustments to specific environments. This study provides grounds for further developments such as multi-sensor data fusion methods and online integration of field data. These undertakings aim at ensuring improved precision of intervention and speed, providing farmers with a reliable and efficient means of quick diagnoses on purple blotch diseases, thereby promoting the sustainability of crops and yield enhancement in agriculture.

关键词： Accuracy Computational modeling Data integrity Data models Real-time systems Agriculture Reliability

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A Study Of Cyclone Tracking Mechanism Using Deep Learning Techniques

A Study Of Cyclone Tracking Mechanism Using Deep Learning Te...

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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： Tushar Paul Dhanalakshmi R Masters of Computer Application School of Computer Science and Engineering Vellore Institute of Technology Chennai India Centre for Cyber Physical Systems School of Computer Science and Engineering Vellore Institute of Technology Chennai India

With over 90 storms emerging worldwide each year, tropical cyclones (TCs) are the most damaging weather systems to emerge over the tropical oceans. For the purpose of providing advanced warning to the impacted areas, fast TC detection and tracking is essential. Remote sensing is essential for the detection of these storms since they originate over open oceans that are far from the continents. Here, we describe an innovative deep learning approach-based automated technique for TC detection from satellite photos. Our research presents a three-phase deep learning architecture for TC detection that consists of three components: 1) a classifier—convolutional neural network (CNN); 2) a wind speed filter; and 3) a detector—Mask convolutional neural network (CNN). Using Bayesian optimization, the hyperparameters of the pipeline as a whole are tuned to display optimal performance. Results show that the suggested method produces test images with specificity (97.59%), high precision (97.10%) and accuracy (86.55%).

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Moth-Flame Optimization for Improving UWSNs Performance

Moth-Flame Optimization for Improving UWSNs Performance

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

作者： Al-Attabi, Kassem Suvidha Aswini, E. Singh, Anil Pratap Maheswari, G. Dwibedi, Rajat Kumar 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 Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Mohali India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Ies University Ies Institute of Technology and Management Department of Computer Science & Engineering M.P. Bhopal India Hindustan Institute of Technology and Science Department of Information Technology Chennai India Department of Ece Aarupadai Veedu Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350390759

This study aims to examine Moth-Flame Optimization (MFO) algorithm by proposing the use in improving the UWSNs system. UWSNs are indispensable for numerous underwater applications;nevertheless, they are confronted with several crucial hindrances, for example, bandwidth constraint, high-latency communication, and limited energy. Since these are complex problems, optimizing them using various techniques Presents a big challenge that could be addressed by bio-inspired optimization algorithms such as the MFO. The motivation for the development of MFO, which emulates the flight pattern moths for navigation, is to aid in the determination of optimal node positioning, routing path, and energy usage in the case of UWSNs. MATLAB workflow includes data collection, algorithm customization, and simulation and forms the basis of the overall methodology applied in the study. A comparison is made with regards to parameters such as network lifetime, energy utilization, data transmission success rate and delay using criteria such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The performance comparisons made between the proposed MFO algorithm and the traditional GA and PSO algorithms prove MFO to be highly effective attaining higher values for all the considered parameters. Thereby, the results demonstrate that the use of MFO has significant promise for increasing the UWSNs lifetime and reliability, making the technique more suitable for underwater surveillance and investigation. It is important for interested researchers and practitioners to understand how some of these bio-inspired optimization techniques can be employed in complex scenarios such as UWSNs, and how this research can inform future work in this area. © 2024 IEEE.

关键词： Genetic algorithms

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Quantum Odyssey: Traversing the NISQ Era's Quantum Terrain

Quantum Odyssey: Traversing the NISQ Era's Quantum Terrain

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

作者： Swati Arya Syed Anas Ansar Shruti Aggarwal Department of Computer Application Babu Banarsi Das University Lucknow India Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab India

The advent of quantum computing has ignited unprecedented anticipation within the realm of IT. This has transcended the limitations of classical computation and revolutionized various facets of technological advancement. At the forefront of this quantum revolution stands the Noisy Intermediate-Scale Quantum (NISQ) era, characterized by a delicate interplay of immense potential and persistent imperfections. This paper delves into the intricacies of NISQ-era quantum computing, unraveling its fundamental principles, challenges, and transformative implications for the IT landscape. Beginning with quantum computing basics-qubits, superposition, entanglement, and gates-the NISQ era's essence addresses error-prone operations and decoherence. Quantum algorithms for NISQ devices, like Grover's search, Shor's factorization, and Variational Quantum Eigensolver (VQE), are scrutinized for their computational potential. Central to NISQ's viability, quantum error correction and mitigation strategies are surveyed, stabilizing quantum computations amidst noise. Examining quantum hardware architectures and performance metrics unveils NISQ devices' potential. applications of NISQ quantum computing in cryptography, optimization, machine learning, and more are explored, showcasing quantum advantage's real-world potential. This paper embarks on a multidimensional exploration of NISQ quantum computing, illuminating its intricate facets, applications, and ethical implications. As quantum computing evolves from a theoretical notion to a tangible paradigm, the insights gleaned herein contribute to a holistic understanding of its role in shaping the next wave of computing.

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Extreme learning with Artifical Electric Field Algorithm (EL-AEFA) for estimation of Short-term Vehicular Traffic System 8

Extreme learning with Artifical Electric Field Algorithm (EL...

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8th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2021

作者： Nayak, Sarat Chandra Nayak, Sanjib Kumar Dash, Alina Cmr College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Vssut Burla Department of Computer Application Sambalpur India Vssut Burla Department of Computer Science and Engineering Sambalpur India

ISBN: (纸本)9781665409629

The faster learning and better approximation ability of extreme learning machine (ELM) made it a popular learning method for neural network training. However, two issues such as probable inclusion of non-optimal parameters because of random selection and size of hidden layer may unfavorably impact the network performance. To address these issues, this article combines the artificial electric field algorithm (AEFA) and ELM to construct a new learning framework called as extreme learning with AEFA (EL-AEFA). Then, EL-AEFA is used to find the optimal model parameters (i.e., weight, bias and number of hidden neurons) of a neural network with single hidden layer. The proposed method is used to estimate the short-term traffic volume through exploitation of data available publicly. The model exhibits minimal structural and computational complexity, follows adaptive training and generates lowest mean absolute percentage of error (MAPE) values compared to few other forecasts developed in a similar way. Therefore, it may be recommended as a promising tool for traffic volume prediction. © 2021 IEEE.

关键词： Electric fields

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Optimizing Anthracnose Severity Grading in Green Beans with CNN-LSTM Integration

Optimizing Anthracnose Severity Grading in Green Beans with ...

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Emerging Technology (INCET), International Conference for

作者： Nitish Kumar Ojha Deepak Upadhyay Manisha Aeri Vinay Kukreja Rishabh Sharma Computer Science and Application IIMT University Meerut Uttar Pradesh India Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

Green beans are a basic food item in the whole world. It's also an important agricultural commodity. However, it faces threats like Anthracnose that destroy both production as well as production quality. Modern agriculture relies heavily on plant disease severity assessment that determines final crop yield and quality. This study explores the creation and verification of an advanced deep-learning model specially designed for accurately grading the degree of plant disease. Accurately, an extensive set of about 7,800 images that represent different stages of the disease were collected for training and validation purposes. The research employed Convolutional Neural Networks (CNNs) to come up with a robust and effective model that would identify subtle differences in the severity of the disease. This showed how reliable it was in determining the difference in severity levels. The model also scored an overall accuracy of 93.9%. A detailed analysis using precision, recall, F1-score, and specificity across different severity classes highlighted the model's credibility and efficiency for disease severity classification. The results of this study also reveal the transformational power of deep learning methods in detecting diseases early on in farming environments which, in turn, provide crucial support toward achieving adaptable and competitive crops in such situations.

关键词： Deep learning Training Plant diseases Adaptation models Accuracy Computational modeling Green products

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Artificial Intelligence Wireless Network Data Security System For Medical Records Using Cryptography Management 2

Artificial Intelligence Wireless Network Data Security Syste...

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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Saxena, Akash Misra, Dipankar Ganesamoorthy, R. Arias Gonzales, Jose Luis Almashaqbeh, Hashem Ali Tripathi, Vikas Compucom Institute of Information Technology and Management Rajasthan Jaipur India University of Engineering & Management Institute of Engineering & Management Department of Computer Science and Engineering and Computer Application Kolkata India Panimalar Engineering College Chennai India University of British Columbia Department of Business Canada Okan University Istanbul Turkey Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9781665437899

So we're going to study here. Wireless technology is amongst the most quickly expanding and active technological disciplines in the world of communication. Connectivity communication that does not need the use of wires, cables, and perhaps other physical media. Encryption algorithm has always been widely used in various these decades to secure information from potential danger and using wireless technologies can also make the information more sheltered therefore these network infrastructure which focuses on providing replaying intervention against many issues, also can analyze the information from almost anywhere through these wireless communication and it will safeguard the health information from potential danger by storing the data in multiple servers in everything using a few real cryptographies This way, only authorized personnel may access the data and no one else can, ensuring that the data is protected from threats. To monitor environmental conditions, wireless sensor networks employ a variety of sensors, which transmit data to a central destination. These wireless sensor nodes are primarily motivated by military applications, and they are now widely used in many automotive and commercial areas, as well as in promising industries such as medicine. Nowadays, remote health monitoring wireless sensor networks unquestionably increase the quality of treatment. dropping and impersonating are two common security vulnerabilities to healthcare applications. © 2022 IEEE.

关键词： Data acquisition

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Implementing CNN and RF Models for Multi-Level Classification: Deciphering Beetroot Aphid Disease Severity

Implementing CNN and RF Models for Multi-Level Classificatio...

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Emerging Technology (INCET), International Conference for

作者： Nitish Kumar Ojha Deepak Upadhyay Manika Manwal Vinay Kukreja Rishabh Sharma Computer Science and Application IIMT University Meerut Uttar Pradesh India Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttrakhand India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

Beetroot aphid infestation is one of the most serious threats to agricultural yield and food security. In this study, we hope to use machine learning models based on Convolutional Neural Networks (CNN) and Random Forest (RF), among others, that can accurately classify the three different severity levels of beetroot aphid disease. A complete dataset was assembled, containing 14,000 images across six levels of severity, and carefully preprocessed for network training. The developed models achieved outstanding results, with an overall accuracy of 97.92 % in identifying levels of severity. The CNN, an image classification expert itself, distinguished very accurately and precisely the complex patterns in these images. Meanwhile, the RF model, using feature extraction techniques, was very accurate and there were differences between levels of severity. By comparing these models, the relative performance subtleties and possible practical applications can be seen. Both models show very high levels of accuracy, but issues resulting from practical considerations suggest that CNN should be used in scenarios where it is important to have visual clues. Meanwhile, assuming limited resources, RF may play a bigger role. These study results greatly facilitate applications of precision agriculture since they create common tools that can be used to make more timely and accurate evaluations of disease severity--information that is essential for effective crop protection plans and operations.

关键词： Radio frequency Precision agriculture Visualization Accuracy Computational modeling Crops Feature extraction

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A blockchain-enabled, trust and location dependent - Privacy preserving system in VANET

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Measurement: Sensors 2023年 30卷

作者： Bala, Kiran Upadhyay, Ramakant Anwar, Syed Rashid Shrimal, Gajendra Department of Computer Science & Engineering Chandigarh Engineering College Jhanjeri Mohali India Department of Computer Science Engineering Sanskriti University Uttar Pradesh Mathura India Department of Engineering and Information Technology ARKA JAIN University Jharkahnd Jamshedpur India Department of Computer Science & Application Vivekananda Global University Jaipur India

Intelligent Transportation System (ITS) heavily relies on the unique mobile ad-hoc network (MANET) known as the vehicular ad-hoc network (VANET). The convenience that the Location Based Service (LBS), security issues arising from VANETs' great portability. Among the most widely used privacy-preserving techniques, distributed k-anonymity does not consider users' reliability, which results in hostile vehicle tracking. Therefore, Blockchain-enabled, Trust and Location dependent-Privacy Preserving (BTLB-PP) authentication system in VANET to overcome issues. Trust Management (TM) based on Dirichlet distribution, client and member will only collaborate with vehicles they confide by explicitly examining various prerequisites of claimed vehicle and collaborative vehicle during construction of unidentified obfuscation region and integrating attributes of these two functions. Using blockchain, a data structure has been proposed to promptly register trust of vehicles on publicly available blocks, allowing any vehicle to retrieve. In the trials, tests on various datasets have been run. Suggested system is resistant to multiple trust model threats, improving the security of vehicles' confidentiality and privacy. The performance evaluation metrics are precision, recall, f-measure and false positive rate (FPR) are used to evaluate the proposed system. Results from the simulation show that the proposed method is efficient and practical in reality. The suggested method was tested in a simulated traffic situation to verify its effectiveness. © 2023

关键词： Vehicular ad hoc networks

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