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Computational Intelligence and Computing Applications (ICCICA), International Conference on

作者： Shanu Khare Vinod Prakash Navjot Singh Talwandi Punit Soni Surender Kumar Dept of Computer Science Engineering Chandigarh University Punjab India Department of Computer Science FGM Govt. College Adampur Hissar Haryana India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science Guru Teg Bahadur College Bhawanigarh Sangrur Punjab India

ISBN: (数字)9798350306446

ISBN: (纸本)9798350306453

Smart contracts have emerged as a promising and innovative technology to enhance cyber-security incident response. With the rise of cyber-security incidents, traditional incident response systems often face challenges in terms of efficiency, transparency and reliability. Smart contracts based on blockchain technology provide a way using circuits and an automated approach to streamline incident response activities. This paper examines the potential of smart contracts in cyber- security incident response, discussing their benefits and challenges. Highlight key features of smart contracts that enable safe and effective incident response, such as automated incident reporting, evidence protection, integrated response actions and, in this case, potential limitations and security considerations of smart contracts were addressing. Overall, smart contracts have the potential to transform cyber-security incident response by providing a secure and transparent framework for industry collaboration and automation

关键词： Bridges Technological innovation Codes Prevention and mitigation Smart contracts Transfer functions Transforms

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Efficient Subgraph Isomorphism Finding in Large Graphs using Eccentricity and Limiting Recursive Calls

arXiv

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arXiv 2023年

作者： Ansari, Zubair Ali Abulaish, Muhammad Thoker, Irfan Rashid Jahiruddin Department of Computer Science and Engineering Gheethanjali College of Engineering and Technology Telangana India Department of Computer Science South Asian University New Delhi India Department of Education Govt. of Jammu Kashmir Jammu and Kashmir India New Delhi India

Purpose – The subgraph isomorphism finding problem is a well-studied problem in the field of computer science and graph theory, and it aims to enumerate all instances of a query graph in the respective data graph. Most of the existing algorithms, including RI, QuickSI, and TurboISO minimize the computing cost of subgraph isomorphism finding problem through reducing the number and size of candidate regions in the data graph that possibly contains some instances of the query graph. The purpose of this paper is to propose an efficient method, SubISO, to find subgraph isomorphisms using an objective function, which exploits some isomorphic invariants and eccentricity of the query graph’s vertices. Design/Methodology/Approach – The proposed objective function is used to determine pivot vertex, which minimizes both number and size of the candidate regions in the data graph. SubISO also limits the maximum recursive calls of the generic SubgraphSearch() function to deal with straggler queries for which most of the existing algorithms show exponential behaviour. Findings – The proposed approach is evaluated over three benchmark datasets. It is also compared with three well-known subgraph isomorphism finding algorithms in terms of execution time, number of identified embeddings, and ability to deal with the straggler queries, and it performs significantly better. Research Implications – An attempt is made to develop scalable and effective algorithms for the analysis of huge graphs obtained from social networks, biological networks, and Resource Description Framework (RDF) data. An effective method for finding subgraph isomorphisms is introduced: SubISO. The performance of the suggested algorithm is assessed using the most advanced methods currently available. The suggested algorithm performed better than the alternative methods. Originality/Value – This paper fulfils an identified need to study subgraph isomorphism finding problem. © 2023, CC BY-NC-ND.

关键词： Graph theory

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Harnessing Remote Sensing and Geographic Information Systems to Address Soil Degradation and Water Scarcity in Climate Change Era

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Remote Sensing in Earth Systems Sciences 2024年第2期7卷 139-147页

作者： Ramesh, Janjhyam Venkata Naga Khandekar, Sachin Dadu Baburao, Dharavath Singh, Mangal Biban, Laxmi Muniyandy, Elangovan Desingurajan, Nigitha Department of CSE Graphic Era Hill University Dehradun 248002 India Department of CSE Graphic Era (Deemed to be University) University Dehradun 248002 India Sinhgad College of Engineering Savitribai Phule Pune University Pune India Department of Computer Science and Engineering C.B. Post Vidya Jyothi Institute of Technology (VJIT) Aziz Nagar Gate Telangana Hyderabad 500075 India Department of Electronics and Telecommunication Engg Symbiosis International (Deemed University) Symbiosis Institute of Technology Pune India Department of Botany Pt. C.L.S. Govt. College Haryana Karnal 132001 India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India Department of Civil Engineering School of Engineering SR University Warangal India

Soil deterioration and water shortages threaten sustainable development in the climate change era, requiring new solutions. Remote sensing and GIS are crucial to tackling these interconnected concerns, as this abstract shows. Climate change and human activity worsen soil deterioration, threatening agricultural output, ecological resilience, and carbon sequestration. Remote sensing technologies including satellite imaging, UAVs, and GIS analytics help stakeholders understand soil health, land degradation, and hydrological dynamics. Algorithm development, cloud computing, and high-resolution sensors have improved remote sensing for soil erosion, salinisation, and nutrient depletion monitoring. For soil rehabilitation and nutrient management, nanofertilisers and carbon nanomaterials may improve soil structure and crop yield. Policymakers, academics, and practitioners may develop climate-resilient soil and water management methods using remote sensing, GIS, and nanotechnology. This abstract emphasises multidisciplinary cooperation and stakeholder involvement to demonstrate the transformational power of technology-driven approaches to soil health, water security, and resilience in the Anthropocene. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate change Geographic information systems Remote sensing Soil degradation Water scarcity

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Biosensor in Climate Change and Water Rise Analysis Based on Diverse Biological Ecosystems Using Machine Learning Model

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Remote Sensing in Earth Systems Sciences 2024年第4期7卷 500-511页

作者： Kalpana, V. Durairaj, M. Poonguzhali, S. Tham, Jacquline Srinivas, B. Biban, Laxmi Priyadarsan, Padhyala Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Chennai 600062 India Department of ECE Saveetha School of Engineering SIMATS Chennai India Roever Agriculture College Tamil Nadu Perambalur India Management and Science University Selangor Shah Alam Malaysia Department of AI & amp ML Aditya University AP Surampalem India Department of Botany Pt. C.L.S. Govt. College Haryana Karnal India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Dist Andhra Pradesh Vaddeswaram 522302 India

The stability of environmental conditions around the world has been threatened by climate change in the last few decades. Sea levels are rising more quickly than ever before due to a combination of factors including temperature increases, changes in precipitation patterns, and glacier melting. We describe a machine learning strategy to design coastal sea level fluctuation as well as related uncertainty across a range of timescales by utilising important ocean temperature estimations as proxies for regional thermosteric sea level component. The aim of this research is to propose novel method in climate change with water level rise analysis using biosensor with machine learning techniques in diverse biological ecosystems. Here, the input is collected as biosensor-based climate analysis with water level rise analysis dataset and processed for noise removal, normalisation, and smoothening. Then, climate data analysis is carried out utilising fuzzy adversarial encoder (FAE) model and the water level rise analysis is carried out using recurrent transfer AlexNet neural network (RTAlexNetNN). The classified output shows climate change analysis with water level rise modelling. The experimental analysis has been carried out for various climate data and water rise data in terms of random accuracy, specificity, MSE, F-measure, and normalised cross-correlation. The proposed technique obtained 98% random accuracy, 96% normalised cross-correlation, 94% specificity, 58% MSE, and 92% F-measure. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Biosensor Climate change Diverse biological ecosystems Machine learning techniques Water level rise analysis

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Climate Impact Prediction: Whale-Optimized Conv-XGBoost with Remote Sensing and Sociological Data

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Remote Sensing in Earth Systems Sciences 2024年第4期7卷 443-456页

作者： Jayakarthik, R. Chandrashekhara K.T Sampath, O. Kumar, Deepak Biban, Laxmi Maroor, Jnaneshwar Pai Malluvalasa, S. N. Lakshmi Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Chennai India Department of Information Science and Engineering BMS Institute of Technology and Management Bangalore India Department of CSE RGM College of Engineering and Technology Nandyal India Department of Applied Sciences Manav Rachna International Institute of Research and Studies Faridabad India Department of Botany Pt. C.L.S. Govt. College Haryana Karnal India Department of Business Administration Nitte Institute of Professional Education Nitte (Deemed to be University) Mangaluru Karnataka India Department of Artificial Intelligence & amp Data Science Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur District Andhra Pradesh India

Climate change poses significant challenges globally, impacting ecosystems, economies, and societies. Accurate prediction of climate impacts is crucial for developing effective mitigation strategies and informed policy decisions. Traditional predictive models often struggle to capture the complex interactions between environmental and socio-economic factors, necessitating advanced approaches that leverage diverse data sources and sophisticated algorithms. This study presents a robust predictive framework integrating convolutional neural networks (CNN) and XGBoost, optimized by the whale optimization algorithm (WOA). The proposed model leverages high-resolution satellite imagery and socio-economic data, combining spatial feature extraction with advanced machine learning techniques to enhance prediction accuracy and efficiency. We evaluate the model’s performance against baseline models and various optimization techniques, including particle swarm optimization (PSO) and genetic algorithm (GA). The integrated CNN and XGBoost model optimized by WOA significantly outperforms these benchmarks, achieving an accuracy of 98%, with substantial improvements in precision, recall, F1-score, and redundant phrases, and focuses directly on the core points to enhance clarity such as mean squared error (MSE), area under the curve (AUC), specificity, sensitivity, mean absolute error (MAE), and R². The results highlight the efficacy of the proposed approach in capturing complex environmental and socio-economic interactions, providing a powerful tool for climate impact assessment and decision-making. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate Impact Prediction CNN Convolutional Neural Networks GA Genetic Algorithm Machine Learning Model Optimization Particle Swarm Optimization PSO Spatial Feature Extraction Whale Optimization Algorithm WOA XGBoost

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An Innovative Way to Increase the Speed and Efficiency of Reverse Mapping of Elliptic Curve Points

An Innovative Way to Increase the Speed and Efficiency of Re...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Ryan Saptarshi Ray Debendranath Das Aritro Sengupta Pankaj Kumar Arun Kumar Sahani Information Technology Govt. College Of Engineering & Ceramic Technology Kolkata India Cryptology and Security Research Unit Indian Statistical Institute Kolkata India Department of Computer Science & Engineering Lingaya's Vidyapeeth Faridabad Haryana India

Elliptic curve cryptography is a widely recognized method of using a public key cryptosystem for encrypting and decrypting data. The message must be transformed to points on an Elliptic Curve using a mapping algorithm in order to send the message. This process of mapping the message to the point on the elliptic curve is done by applying a well-known mapping algorithm. Elliptic curve cryptography is widely acknowledged to have simpler mathematical operations compared to other cryptographic methods. However, researchers in the field of cryptography have faced challenges in mapping plain messages onto points on an elliptic curve and reversing the process at the receiver's end for decryption. Recent advancements have led to the development of mapping techniques that are resistant to frequency analysis. In this paper, we have discussed a well-known mapping scheme that has been used to map plain text messages to points on elliptic curves. However, the time taken to map a message and encrypt the points takes a substantial amount of time. This latency can be reduced if we parallelize the mapping process. In this paper, we have parallelized the reverse mapping phase of the scheme at the receiver's end to reduce the total communication time. Performance results after parallelization have been recorded and provided in the later section of the paper in tabular and graphical form.

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Path Planning and Controlling of Omni-Directional Robot Using Cartesian Odometry and PID Algorithm

Path Planning and Controlling of Omni-Directional Robot Usin...

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2019 International Conference on Computing, Power and Communication Technologies, GUCON 2019

作者： Chaudhari, Tejas Jha, Mayank Bangal, Rohit Chincholkar, Ganesh Govt. College of Engineering and Research Department of Instrumentation and Control Engineering Pune India Govt. College of Engineering and Research Department of Computer Engineering Pune India

ISBN: (纸本)9789353510985

Omni direction is the concept of existence of motion in every direction. Here, we have controlled the resulting vectors of individual wheels of a three wheel omni robot that are ultimately responsible for a stabilized motion of the robot in any direction. This phenomenon is termed as 'Holonomic Drive' and it provides higher efficiency and controllability to non-holonomic drives, when used with odometry and PID algorithms. Odometry basically is method of acquiring the real time positional data of a system in a world frame and using it for path planning. PID algorithm when applied on any system reduces the transient response and helps to attend steady state conditions with less settling time. Here, we have successfully designed and implemented the algorithms for path planning with the help of quadrature encoders and heading control of three wheel omni robot using PID, implemented on IMU(Inertial Measurement Unit). © 2019 Galgotias University.

关键词： Motion planning

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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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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Kumar, Santosh Dash, Kishor Kumar Piduru, Bhargav Reddy Rajkumar, K. Bhaskar, Bandi Tiwari, Mohit Era University Department of Computer Science Uttar Pradesh Lucknow India Balimela College of Science and Technology Berhampur University and Govt. Model Degree College Study Centre Odisha State Open University Faculty & Academic Counsellor Department of Botany and Journalism & Mass Communication India Customer Experience Architect Hyundai IrvineCA United States /CSE Department Tamilnadu Kovilpatti India Sr University School of Cs & Ai Telangana Warangal India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350308167

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. © 2023 IEEE.

关键词： and Distributed database management system Challenges Distributed database system Privacy Security

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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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2022 IEEE GLOBECOM Workshops, GC Wkshps 2022

作者： Mandal, Riman Mondal, Manash Kumar Banerjee, Sourav Chatterjee, Pushpita Mansoor, Wathiq Biswas, Utpal University of Kalyani Department of Comp. Sc. and Engg Kalyani India Kalyani Govt. Engineering College Department of Comp. Sc. and Engg Kalyani India Tennessee State University Department of Computer Science NashvilleTN United States University of Dubai Department of Electrical Engineering Dubai United Arab Emirates

ISBN: (纸本)9781665459754

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. © 2022 IEEE.

关键词： Quality of service

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Enhanced Signal Processing through Frequency Normalization using Fourier Transform Techniques

Enhanced Signal Processing through Frequency Normalization u...

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Intelligent Technologies (ACOIT), Asian Conference on

作者： Neeraj Bhargava Pramod Singh Rathore Ankur Goswami Apoorva Bhowmick Avanindra Vasuki Vyas School of Engineering & System Sciences MDS University Ajmer India Department of Computer and Communication Engineering Manipal University Jaipur India Department of Computer Applications Govt. Engineering College Bikaner India United Nations Secretariate New Yourk NY USA United Nations Secretariate Baghdad Iraq MDS University Ajmer India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

In the realm of signal processing, achieving accurate frequency normalization is pivotal for the effective analysis and interpretation of signals across various applications. This paper presents a novel approach to frequency normalization utilizing Fourier Transform techniques to enhance signal processing capabilities. By leveraging the mathematical robustness of the Fourier Transform, the proposed method systematically adjusts and normalizes frequency components, thereby improving the clarity and precision of signal analysis. Author addresses common challenges such as signal distortion and noise interference, providing a more reliable framework for applications in communications, biomedical engineering, and audio signal processing. Experimental results demonstrate the efficacy of our method in maintaining signal integrity and enhancing processing performance, particularly in complex and noisy environments. This research offers significant advancements in the field of signal processing, paving the way for more accurate and efficient frequency normalization techniques.

关键词： Fourier transforms Accuracy Noise reduction Noise Reliability engineering Robustness Machine listening Noise measurement Signal analysis Signal integrity

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