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9th International Conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Sanchez, Domenic T. Raghuvanshi, Abhishek Biban, Laxmi Prabhu, P. Godara, Sunila Garg, Rachit Cebu Technological University-NEC Cebu Philippines Mahakal Institute of Technology Department of Cse Ujjain India Pt. C.L.S. Govt. College Department of Botany Haryana Karnal India Alagappa University Information Technology Centre For Distance And Online Education Karaikudi India Gjus&t Cse Department Hisar India School of Computer Science And Engineering Lovely Professional University India

ISBN: (纸本)9798350365092

Diagnosing plant diseases is one of the most crucial and essential tasks in agriculture. Visual inspection or microscope is frequently used to identify causes. Visual judgment is subjective, and mistakes, biases, and optical illusions can arise due to the effect of cognitive and psychological processes and the subjective character of the job. Molecular biology, immunology, and pathogen culture-based laboratory studies sometimes take too long and do not produce results quickly enough. In light of this, automated technology's ability to quickly and accurately diagnose illness is essential. For big farms, manual observation is impracticable and costly since it requires constant plant monitoring. This article presents an optimized image enhancement enabled LSTM technique for plant leaf disease detection. Leaf images data set is used as input. The noise in these leaf images is Gotten rid of by employing an adaptive filter during the preprocessing stage of the development process. The Particle Swarm methodology is used by the LSTM optimization method in order to get a finer level of control over the input parameters. Additionally, Grey Wolf Optimization is used in order to enhance the functionality of the input parameters of the LSTM classifier. The approach known as the Gradient Boosting Decision Tree is further used for classification purposes. For the purpose of the experiment, the PlantVillage data set is used. Total 2500 images are selected at random for the experimental work. Two hundred photographs are used in order to train the classification model, and the remaining five hundred images are used in order to test the classification model after it has been trained. In the process of classifying leaf images, the PSO LSTM algorithm is becoming more accurate. PSO LSTM has achieved accuracy of 99 percent. © 2024 IEEE.

关键词： Adaptive filters

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Design of Secure Reversible Select, Cross and Variation (RSCV) Architecture in Quantum Computing 5

Design of Secure Reversible Select, Cross and Variation (RSC...

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5th International Conference on Devices for Integrated Circuit, DevIC 2023

作者： Kundu, Arpita Das, Jadav Chandra De, Debashis Debnath, Bikash Sarkar, Angsuman Institute of Engineering and Management Kolkata India MAKAUT W.B. Department of Information Technology West Bengal Haringhata741249 India MAKAUT W.B. Department of Computer Science and Engineering West Bengal Haringhata741249 India Amity University Department of Information Technology West Bengal Kolkata India Kalyani Govt. Engineering College Department of Electronics and Communication Engineering West Bengal Kalyani India

ISBN: (纸本)9798350347265

The principles of quantum physics serve as the foundation for the power of quantum computing technologies. Since these events lack a classical counterpart, conventional computers cannot provide comparable outcomes. Future studies are dominated by the rapidly expanding field of quantum technology application. Its popularity has increased as a result of several unique features that are missing in traditional domains. Real-Time quantum computing makes it easy to design and apply unique quantum layouts on accessible quantum computing platforms. Quantum machines claim to possess high computing power in the field of cryptography. Expertise in communication security has helped this area grow significantly over time. A novel method of data encryption termed SCV (select, cross, and variation), based on input data halves swapping, is shown here utilizing the reversible quantum logic. Using a variety of quantum gates, we created this technique in this study in IBM Quantum. The developed method is applied to several quantum simulators;the quality is compared concerning frequency, no. of qubits, run time, and quantum volume (QV). © 2023 IEEE.

关键词： Qubits

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Comparative Analysis of Charge Plasma Vertical TFET Structures with H f O2 and SiO2 Gate Dielectric materials

Comparative Analysis of Charge Plasma Vertical TFET Structur...

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： B. Krishna Bharathi N. Ramanjaneyulu Department of ECE JNTUA Anantapuramu Andhra Pradesh India Lecturer in ECE Govt. Polytechnic Tadipatri AndhraPradesh India Department of ECE Rajeev Gandhi Memorial college of Engineering and Technology Nandyal Andhra Pradesh

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

This study investigates the impact of different gate dielectric structures on the performance of charge plasma vertical tunnel field-effect transistors (TFETs). Three TFET configurations were considered: a single material gate (H fo2), a double material gate (H fO2, SiO2), and a stacked gate (H fO2 stacked on Si O2 ). All of the three structures are evaluated using lateral and vertical conduction currents, electron and hole mobilities, and quasi-Fermi levels. The results indicate a major improvement in the devices' performance with the presence of extra dielectric layers in the double and stacked gate structure. The proposed structures allow peak currents, superior carrier mobilities, as well as even more favorable tunneling barriers when compared to the single material gate. The findings suggest that the careful engineering of gate dielectric materials and their stacking can optimize the performance of TFETs, paving the way for their potential applications in future high-performance electronic devices.

关键词： Performance evaluation Silicon compounds TFETs Dielectric materials Stacking Logic gates Tunneling Dielectrics Plasmas Transistors

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Optimal Power Flow Solutions Considering Load and Renewable Power Uncertainty Using MISMA

Optimal Power Flow Solutions Considering Load and Renewable ...

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IEEE India Conference (INDICON)

作者： Arnab Kumar Mondal Parthasarathi Bera Sunita Halder Nee Dey Department of Electrical Engineering Sidhu Kanhu Birsa Polytechnic Keshiary West Bengal India Department of Electrical Engineering Kalyani Govt. Engineering College Kalyani West Bengal India Department of Electrical Engineering Jadavpur University Jadavpur West Bengal India

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

Recently, due to the shortage of conventional energy sources and environmental concerns, the integration of renewable energy sources like wind and photovoltaic (PV) power generators has increased and thus creates difficulties in optimal power flow (OPF) problems due to their uncertainty in nature. Again, the optimal sizes and proper locations of the thermal and renewable power generators (RPG) in the power network are very important and make the OPF problem a mixed integer problem. This work proposes Mixed Integer Slime Mould Algorithm (MISMA) to tackle these challenges for OPF considering load and renewable power uncertainties. In this work, Weibull, lognormal and normal probability distribution are considered for the model of wind, PV and load uncertainties respectively to provide a realistic representation of operating conditions. The proposed method is applied on the standard IEEE 30 bus, IEEE 57 bus and IEEE 118 bus system and comparison is made with similar optimization methods to observe its superiority for minimizing generation cost, transmission loss, voltage deviation and emission.

关键词： Wind energy generation Renewable energy sources Uncertainty Costs Wind energy Voltage Wind power generation Propagation losses Generators Load flow

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HBPSO based Resource Scheduling for Optimization of Cost and Makespan in Cloud Data Centers

HBPSO based Resource Scheduling for Optimization of Cost and...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： A. Sathya Sofia C. P. Thamil Selvi P. Francis Antony Selvi P. Emmanuel Nicholas Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul Tamil Nadu India Department of Artificial Intelligence & Data Science Rathinam Technical Campus Coimbatore Tamil Nadu India Department of Electronics and Electrical Engineering RVS College of Engineering Dindigul Tamil Nadu India Department of Mechanical Engineering Govt. Polytechnic College Chekkanurani Madurai Tamil Nadu India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Now present years, Cloud computing has emerged as a computational model for High-Performance Computing (HPC) applications, which deals with complex and highly intensive data. Cloud computing is a promising new style of scheduling environment which provides an scalable with on-demand supply methods to the consumers. In a cloud context, job or application scheduling is difficult and NP-complete. The objective of this work is to develop an algorithm to schedule for efficiently managing cloud resources besides finishing jobs with scientific applications applied in the Cloud Resource Broker (CRB) framework in Cloud Data Centre (CDC). The proposed HoneyBee based PSO (HBPSO) scheduling algorithm is deliberated for efficient resource allocation and henceforth the suggested optimization method anticipates to accomplish the objectives in minimizing both execution time and cost required to execute the job. It promotes resource allocation and job scheduling functions by balancing the load and reducing the skew, this in turn will eventually decrease cost, make span and resource utilization. Our goal is to transform the cloud datacenter to an efficient and effective datacenter in terms of time, cost and resource utilization so as to enhance the business values. By reducing completion times and costs while increasing the number of applications completed and user satisfaction, the simulation results utilizing Cloudsim and Cloud analyst demonstrate the efficacy of the suggested research work.

关键词： Cloud computing Data centers Schedules Costs Scheduling algorithms Simulation Optimization methods Transforms Scheduling Resource management

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A Survey of Digital twins in Smart Farming Systems

A Survey of Digital twins in Smart Farming Systems

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engineering Informatics, IEEE

作者： S Shashidhar C K Sunil B Vineetha Department of Computer Science Govt. First Grade College Manvi Karnataka India Department of Computer Science and Engineering Indian Institute of Information Technology Dharwad Karnataka India Presidency School of Computer Science and Engineering Presidency University Bangalore Karnataka India

ISBN: (数字)9798331505776

ISBN: (纸本)9798331505783

Digital twins (DTs) have developed as a transformative technology in smart farming, facilitating real-time simulation, monitoring, and optimization of agricultural processes. This survey explores DTs’ definition and their various applications in agriculture, underscoring their ability to enhance decision-making and upsurge farm productivity. It analyses the existing surveys and explains different use cases, highlighting the projects that exemplify the DT’s practical integration in smart farming systems. The role of the Internet of Things (IoT) and machine learning (ML) in DT is also discussed for creating accurate and efficient DTs for precision agriculture. To identify gaps and areas for further research, a comparative analysis of these technologies is presented. This comprehensive survey intends to deliver understandings into the present state of DTs in smart farming, addressing challenges and providing directions for future development to improve agricultural practices.

关键词： Smart agriculture Surveys Accuracy Reviews Scalability Machine learning Real-time systems Digital twins Streams Farming

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Cuscuta Detection and Classification of Blackgram Plant Leaf Diseases Using Deep-Transformation Algorithm

Cuscuta Detection and Classification of Blackgram Plant Leaf...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Nadakuditi Swarna Jyothi Raavi Satya Prasad Acharya Nagarjuna University Guntur AP India Govt. Degree College Andhra Pradesh Department of Computer Science & Engineering Dhanekula Institute of Engineering & Technology Ganguru Vijayawada A.P. India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Cuscuta is a parasitic plant that highly impacts the yielding of black gram (Vigna mungo) by reducing the quality based on required subsistence. Early detection of Cuscuta affliction becomes more complex, which may lead to heavy loss to the framers. This paper proposed a new Deep-Transformation Algorithm (DTA) to overcome various issues in finding the affected regions accurately and effectively classifying Cuscuta on black gram plants. In this context, the pre-trained model VGGNet is used to train on Cuscuta images that obtain the disease patterns using multiple layers. The preprocessing technique, the contrast enhancement model CLAHE (contrast limited adaptive histogram equalization), improves the confined regions and avoids over-saturation. The other segmentation method, such as Watershed segmentation, is used to segment the regions based on Cuscuta stems and leaves. Finally, the proposed DTA is the combination of Pipeline with a fine-grained neural network applied on testing set images, which obtains superior performance in terms of classification accuracy with 99.78 % , which is high compared with other existing models.

关键词： Image segmentation Adaptation models Accuracy Pipelines Watersheds Real-time systems Classification algorithms Reliability Diseases Testing

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A brain stroke detection model using soft voting based ensemble machine learning classifier

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Measurement: Sensors 2023年 29卷

作者： Srinivas, A. Mosiganti, Joseph Prakash Department of Computer Science and Engineering Faculty of Informatics University College of Engineering Osmania University Telangana Hyderabad India SRR Govt. Arts & Science College Telangana Karimnagar India Department of Computer Science and Engineering MCET Telangana Hyderabad India

A brain stroke is a medical emergency that occurs when the blood supply to a part of the brain is disturbed or reduced, which causes the brain cells in that area to die. The process involves training a machine learning model on a large labelled dataset to recognize patterns and anomalies associated with strokes. The proposed model is an ensemble machine learning algorithm, which integrates predictions obtained from several individual classifiers like Random Forest, Extremely Randomized Trees and Histogram-Based Gradient Boosting to make a final prediction. Each classifier provides a probability estimate for each class, final prediction is based on weighted average of these probabilities. The weights assigned to each classifier can be based on their performance on a validation set or can be set uniformly. The proposed soft voting model improved accuracy and robustness of final prediction compared to a single classifier. The limitation of study classification of stroke type can lead to appropriate use of resources and help to reduce healthcare costs. To solve this issue, future introduced a swarm intelligence-based optimization for improve classification accuracy. The proposed model obtained an accuracy of 96.88%. In order to carry out the investigation, the stroke prediction dataset is collected from UCI machine learning repository. © 2023 The Authors

关键词： Forecasting

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Blockchain-based security solutions for IOT networks: Analysing efficacy and implementation challenges

Blockchain-based security solutions for IOT networks: Analys...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Kumar, Gotte Ranjith Chamaraju, Y.S. Mulani, Umar M Naveenkumar, R. Datta, Auadhati Kanwer, Budesh Sr University School of Cs & Ai Telangana Warangal India Govt Cpc Polytechnic Department of Electronics & Communication Engineering Karnataka Mysore570007 India Kj College of Engineering and Management Research Pune India Chandigarh Group of Colleges Jhanjeri Chandigarh Colleges of Engineering Department of Computer Science and Engineering Punjab Mohali140307 India Vignan's Institute of Information Technology Department of Management Studies Andhra Pradesh Duvvada India Poornima Institute of Engineering and Technology Rajasthan Jaipur India

ISBN: (纸本)9798331508685

Blockchain innovation has gathered significant attention from both scholars and industry professionals after years of development. This technology enables the creation of smart contracts, which facilitate automated deal execution among users. Today, blockchain is also employed as a specialized infrastructure in some IT applications, helping prevent data duplication, similar to how Bitcoin and other cryptocurrencies manage transaction records. Bitcoin records, for instance, are exceptionally secure as the currency circulates across hundreds of thousands of servers, making it nearly impossible to alter. For a successful breach, an attacker would need to manipulate records on 51% of these servers simultaneously. In contrast, private data stored on single servers, such as those managed by Amazon and Google, are more vulnerable to cyberattacks. In this paper, the study propose leveraging blockchain to address security concerns in the Internet of Things. The study identifies and categorizes common security challenges, with a focus on data privacy issues, as well as the protocols used for network organization, communication, and management. Following this, the study outline security solutions for IoT and illustrate scenarios in which blockchain is applied within IoT applications. © 2025 IEEE.

关键词： Smart contract

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A Load Balancing Algorithm for Cloud-Fog-based IoT Networks using Bald Eagle Search Optimization

A Load Balancing Algorithm for Cloud-Fog-based IoT Networks ...

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Circuits, Power and Intelligent Systems (CCPIS), International Conference on

作者： Ranumayee Sing Sourav Kumar Bhoi Niranian Panigrahi Faculty of Engineering (Computer Science and Engineering) Biju Patnaik University of Technology Rourkela Odisha India Department of Computer Science and Engineering Parala Maharaja Engineering College (Govt.) Berhampur Odisha India

The exponential growth of Internet of Things (IoT) usage causes the network congestion, delay-response while using cloud computing. Fog computing, a closer cloud service provider near edge network is popular for providing service to latency sensitive real-time data. The united cloud-fog model can be benefited to handle both real-time and latency-delayed services that minimizes cost and energy consumption. The large-scale real-time services to distributed and limited resource-based fog node lead a load balancing problem where nodes are unevenly loaded with underload or overload. Here a Bald Eagle Search based load balance algorithm is proposed that can evenly balance the load as well as optimally allocate resources of fog node. The performance results are evaluated and validated which reduces the energy consumption and increases makespan and percentage of resource utilization.

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