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International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： B. Nancharaiah A. Visva Sangeetha A Brahmareddy Ramaganesh M Nookala Venu R. Vijaya Kumar Reddy Department of Electronics and Communication Engineering Usha Rama College of Engineering and Technology Telaprolu Andra Pradesh India Department of English PSNA College of Engineering and Technology (Autonomous) Dindigul Tamilnadu India Department of Computer Science & Engineering Marri Laxman Reddy Institute of Technology and Management Autonomous Institute Hyderabad Telangana Department of Mechanical Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu India Department of Internet of Things (IoT) Madhav Institute of Technology & Science A Govt. Aided UGC Autonomous Institute Gwalior Madhya Pradesh India Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andra Pradesh India

With the rapid development of modern computer network technology and image storage technology, the number of image data on the Internet has increased dramatically, and there is a phenomenon that image data is extremely rich, but useful image information and knowledge are poor. Therefore, there is an urgent need for a technology for mining useful information in image data, so image mining technology came into being. As an exciting and active branch of cognitive science and computer science, digital image processing technology has received great attention and made great progress in recent years. In particular, the rapid growth of visual information and the ever-increasing need for applications have caused the study of algorithms that can handle a variety of diverse domains and activities to become the primary focus of image processing and fields that are closely related to it. This study makes use of a classification approach known as decision trees, and the decision tree algorithm is the end product of a data classification scheme in which the tree structure plays a central role. The nodes of the tree that are not leaf nodes represent the test of the data characteristics; each branch of the tree represents an output of the test, and each leaf node indicates a classification. By specifying the path that starts at the root node and continues to each leaf node, one can obtain a variety of categorization criteria. According to the findings of the research presented in this article, the algorithm presented in this paper possesses outstanding effectiveness and is suited for widespread use.

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Smart Home Energy Management Considering Worst-Case Scenarios 7

Smart Home Energy Management Considering Worst-Case Scenario...

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7th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2020

作者： Ganguly, Biswarup Paul, Subho Chatterjee, Arunava Meghnad Saha Institute of Technology Department of Electrical Engineering Kolkata700150 India Indian Institute of Technology Roorkee Electrical Engineering Department Roorkee247667 India Raghumathpur Govt. Polytechnic Department of Electrical Engineering Purulia723133 India

ISBN: (纸本)9780738111513

Home energy management system (HEMS) enlarges participation of consumers into the demand response program. Day ahead energy management algorithms are simple and require less calculation effort, but mal-operates if any fluctuation occurs at real time from the day ahead schedule. Therefore day ahead HEMSs need special attention at design level to make them adaptive for any uncertainties. This paper investigates worst-case scenario for any smart home, equipped with different ac and dc domestic loads, under different worst conditions regarding market energy price, rooftop solar generation and islanding from the upper utility. The entire idea is implemented on a smart home, designed as an ac/dc microgrid (MG), having practical appliance data sets to prove the efficacy of the proposed MILP based worst condition management for a smart home. © 2020 IEEE.

关键词： Digital storage

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Optimized Image Enhancement Enabled LSTM Technique for Plant Leaf Disease Detection

Optimized Image Enhancement Enabled LSTM Technique for Plant...

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International Conference on Science Technology engineering and Management (ICONSTEM)

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

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

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.

关键词： Visualization Accuracy Biological system modeling Noise Boosting Classification algorithms Decision trees

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Security and Privacy Challenges in Distributed Database Management Systems

Security and Privacy Challenges in Distributed Database Mana...

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Information Technologies and Communications (GCITC), Global Conference on

作者： Santosh Kumar Kishor Kumar Dash Bhargav Reddy Piduru K. Rajkumar Bandi Bhaskar Mohit Tiwari Department of Computer Science ERA University Lucknow Uttar Pradesh Department of Botany and Journalism & Mass Communication Faculty & Academic Counsellor Balimela College of Science and Technology Berhampur University and Govt. Model Degree College Study Centre Odisha State Open University Hyundai Customer Experience Architect Irvine California (SG)/CSE Department National Engineering College Kovilpatti Tamilnadu India School of CS & AI SR University Warangal Telangana India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi

This research aims to investigate the challenge of recognizing and evaluating distributed database management system (DDBMS) security and privacy challenges and also to describe important danger indicators that may lead to possible data security difficulties in distributing resources, both tangible and functioning. An investigation of the primary DDBMS security and privacy challenges that can be found while developing and managing a standard DDBMS meant for supporting data procedures and offering data services was conducted as a component of this research. The data assessment was produced depending on the findings of surveys and questionnaires administered to DDBS security and privacy professionals with varying degrees of training and emphases in their operations within this expertise field. The findings of the research include a list of primary risk variables based on their significance and frequency in practice, and also a spotlight on the most important security and privacy measures. This article summarizes data on traditional methods to illustrate data security challenges employing statistical, qualitative, and mixed evaluation, and also the most recent techniques depending on smart categorization and examination of DDBS security and privacy challenge factors and functioning with huge amounts of data.

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Design and implementation of an SLA and energy-aware VM placement policy in green cloud computing

Design and implementation of an SLA and energy-aware VM plac...

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IEEE Globecom Workshops

作者： Riman Mandal Manash Kumar Mondal Sourav Banerjee Pushpita Chatterjee Wathiq Mansoor Utpal Biswas Department of Comp. Sc. and Engg University of kalyani Kalyani India Department of Comp. Sc. and Engg Kalyani Govt. Engineering College Kalyani India Department of Computer Science Tennessee State University Nashville TN USA Department of Electrical Engineering University of Dubai Dubai UAE

ISBN: (纸本)9781665459761

The pay-per-use model of cloud computing is booming in every industry. Power-hungry large heterogeneous data centers are providing these cloud services for 24× 7. Green cloud computing offers an energy-efficient and environment-friendly mechanism to offer cloud services while maintaining the quality of service (QoS). QoS is defined in terms of service-level agreement (SLA). VM Consolidation is one of the widely used frameworks that involve Virtual Machine (VM) migration and VM Placement to reduce energy consumption and maintain better QoS. Using the VM Placement policy VMs are allocated to suitable physical machines (PM). Efficient VM placement with better resource management can conserve electricity and provide better QoS. A new SLA and energy-aware VM placement policy have been developed in this research. The VMs are placed in a PM having the least energy consumption and SLA violation (SLAV). Further, the proposed VM placement policy has been implemented using CloudSim and it has been shown that the proposed VM placement policy outperforms state-of-art VM placement policies like PEBFD, MBFD, MFPED, and PEFED.

关键词： Cloud computing Energy consumption Planing Green products Quality of service Virtual machining Planning

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Incorporating Edge-AI in IoT-Cloud Framework for Landslide Surveillance and Forecasting

Incorporating Edge-AI in IoT-Cloud Framework for Landslide S...

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Smart Power Control and Renewable Energy (ICSPCRE), IEEE International Conference on

作者： Kapil Aggarwal Pratibha C. Kaladeep Bhavesh D. Patel C.S. Sasireka J. Arun Kumar Nookala Venu Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram AP India CSE and Controller of Examinations Walchand Institute of Technology Solapur SNPIT & RC Vidyabharti Trust affiliated by GTU Umrakh Department of Computer Science and Engineering Rajiv Gandhi College of Engineering Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Centre for IoT Madhav Institute of Technology & Science (MITS) A Govt. Aided UGC Autonomous Institute Gwalior Madhya Pradesh India

ISBN: (数字)9798350377002

ISBN: (纸本)9798350377019

In landslide-prone regions, the timely detection and precise prediction of impending disasters are paramount for minimizing their devastating impact. By integrating edge devices endowed with AI capabilities into the existing IoT infrastructure, this approach enables real-time data processing and analysis at the source, facilitating swift detection of early warning signs and prompt dissemination of alerts. Augmenting this setup with cloud resources enhances scalability and computational prowess, crucial for conducting comprehensive data analysis and predictive modeling. Anticipated outcomes include the development of a sophisticated monitoring system capable of accurately forecasting landslides, thereby enhancing preparedness and resilience in landslide-prone areas. The project envisions a scalable and adaptable framework that integrates seamlessly into existing infrastructure, offering a cost-effective and reliable solution for landslide surveillance and prediction.

关键词： Landslides Renewable energy sources Surveillance Scalability Predictive models Terrain factors Real-time systems

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PbV mSp: A priority-based VM selection policy for VM consolidation in green cloud computing

PbV mSp: A priority-based VM selection policy for VM consoli...

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International Conference on Signal Processing and Information Security (ICSPIS)

作者： Riman Mandal Manash Kumar Mondal Sourav Banerjee Pushpita Chatterjee Wathiq Mansoor Utpal Biswas Department of Comp. Sc. and Engg. University of kalyani Kalyani India Department of Comp. Sc. and Engg. Kalyani Govt. Engineering College Kalyani India Department of Computer Science Tennessee State University Nashville TN USA Department of Electrical Engineering University of Dubai Dubai UAE

ISBN: (纸本)9781665492669

Cloud computing forms the backbone of the era of automation and the Internet of Things (IoT). It offers computing and storage-based services on consumption-based pricing. Large-scale datacenters are used to provide these service and consumes enormous electricity. Datacenters contribute a large portion of the carbon footprint in the environment. Through virtual machine (VM) consolidation, datacenter energy consumption can be reduced via efficient resource management. VM selection policy is used to choose the VM that needs migration. In this research, we have proposed PbV mSp: A priority-based VM selection policy for VM consolidation. The PbV mSp is implemented in cloudsim and evaluated compared with well-known VM selection policies like gpa, gpammt, mimt, mums, and mxu. The results show that the proposed PbV mSp selection policy has outperformed the exisitng policies in terms of energy consumption and other metrics.

关键词： Measurement Cloud computing Energy consumption Green products Information security Pricing Signal processing

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A machine learning method for predicting disease-associated microRNA connections using network internal topology data

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Healthcare Analytics 2023年 4卷 100215-100215页

作者： Jindal, Latika Sharma, Aditi Prasad, K.D.V. Irshad, Azeem Rivera, Richard Karimovna, Abdurakhimova Dilora Department of Computer Science and Engineering Medi-Caps university Indore India Department of computer science institute of engineering & technology Lucknow India Symbiosis Institute of Business Management Hyderabad India Symbiosis International (Deemed University) Pune India Asghar Mall College Rawalpindi Higher Education Department (HED) Govt. of the Punjab Pakistan Department of Informatics and Computer Science Escuela Politécnica Nacional Quito Ecuador Department of Corporate Finance and Securities Tashkent Institute of Finance Tashkent Uzbekistan

Recent studies have shown that aberrations or mutations of microRNAs (miRNA) can cause various diseases. Therefore, identifying disease-associated microRNAs can aid in diagnosing and treating related disorders. However, getting precise correlations through biological research is expensive and time-consuming. This research suggests a machine learning method (HNDLM) based on network internal topology data to anticipate disease-miRNA connections. HNDLM applies the recently described network embedding technique to biological networks rather than creating similar ones. According to the experimental findings, HNDLM performs better in terms of accuracy and Area Under the ROC Curve (AUC) value when compared to the traditional algorithms MIDPE, MIDP, WBSMDA, RLSMDA, Collective Prediction based on Transductive Learning (CPTL), and Human Disease-related MiRNA Prediction (HDMP). The top 30 potential miRNAs suggested by HNDLM can also be verified by earlier research in the case study. HNDLM can identify probable disease-miRNA correlations, which will aid in the investigation of illness path physiology and advance the field of bioinformatics. © 2023 The Authors

关键词： Bioinformatics Correlations Link prediction Machine learning MicroRNAs

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Responsiveness of FWM with Fraction of Planck HOD Parameters in Presence of Intensity Dependent Phase Matching Factor in Optical Transmission

Responsiveness of FWM with Fraction of Planck HOD Parameters...

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2020 International Conference on Intelligent engineering and Management, ICIEM 2020

作者： Bhatia, Richa Bhatia, Shreya Ambedkar Institute of Advanced Communication Technologies and Research Department of Electronics and Communication Engineering Govt. of NCT of Delhi Delhi India University of Sheffield Department of Computer Science Sheffield United Kingdom

ISBN: (纸本)9781728140971

Four-wave mixing(FWM) induced crosstalk is one of the most restraining nonlinear impairment in dense wavelength division multiplexed optical transmission systems yet this has been useful in variety of applications involving second-generation optical devices and optical device measurement technology. Keeping this in view, we have mathematically analysed Four-wave mixing in presence of intensity dependent phase matching factor with fraction of Planck higher order dispersion (HOD) parameters. Response of FWM power with fraction of Planck HOD terms up to eighth order (8OD) has been examined for a range of channel powers, channel spacing, fiber effective core area and fiber length values. It has been observed that in presence of self-phase and cross-phase modulation the FWM power increases by 2 to3 dB for channel powers above 40 mW. An improved nonlinear tolerance with 9 dB reduction in FWM power is observed for increase in core effective area from 30 to SO 2. For increasing fiber lengths, FWM values followed an oscillatory pattern with decreasing amplitude due to different phase matching conditions for different fiber lengths. Response of FWM power with fraction of Planck HOD parameters can be useful for reducing FWM induced crosstalk as well as for FWM wavelength conversion and fiber optic parametric oscillator applications. © 2020 IEEE.

关键词： Fibers

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Robust and Lightweight Authentication for Securing Communication in the Internet-of-Drones (IoD) Environment

Robust and Lightweight Authentication for Securing Communica...

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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Saeed Ullah Jan Muhammad Bilal Anwar Ghani Salabat Khan Rashid Ahmad Do–Hveun Kim Higher Education Department Govt. College Wari (Dir Upper) Khyber Pakhtunkhwa Pakistan School of Computing and Communications Lancaster University United Kingdom Big Data Research Center Jeju National University Jeju-si South Korea Faculty of Computing and Information Technology Sohar University Sohar Oman Dept. Computer Engineering Jeju National University Jeju-si South Korea

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

Being an essential component of 5G and beyond, the Internet of Drones (IoD) enables a legitimate operator to employ several drones to carry out distinct missions in restricted airspace under the control of the GCS. Communication with the external user (mobile operator), the central server, and other drones is accomplished via a public wireless medium, which is vulnerable to numerous threats and needs robust authentication. Various authentication schemes have been presented, some of which have design issues, excessive communication, or lack of a delicate balance of security and performance due to modular exponentiation. Therefore, this article proposes a secure authentication scheme based on asymmetric cryptography to address the issues above. The security of the proposed scheme is formally scrutinized through the RoR model, ProVerif. Furthermore, the performance is analyzed through computation, communication, and storage overheads. The comparative analysis shows that the proposed scheme balances security with performance and can be implemented in a real-world IoD environment.

关键词： Wireless communication Hash functions Protocols Computational modeling Authentication Product delivery Internet

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