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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Sakshi Sharma, Chetan Kishore, Jaydeep Bhardwaj, Vivek Amity Institute of Information Technology Amity University Noida India UpGrad Education Private Limited Bangalore India School of Computer Science and Engineering Manipal University Jaipur Jaipur India

ISBN: (纸本)9798350377972

The escalation of the global temperature and the pressure on the fields is at an all-time high. To feed the rising population, insecticides, and pesticides are likewise at an all-time high. With the growing temperature due to global warming and the increasing employment of pesticides to feed the people, one class of creatures suffers the direct hit: insects. On the other hand, the growing warmth leaves them vulnerable to death due to bad environmental circumstances. On the other hand, the increasing employment of pesticides extirpates innocuous insects with hazardous insects. There is a continual requirement to monitor the growth and condition of such insects to maintain the sustainability of the environmental system. However, accomplishing such a big work manually is not only impossible but fruitless, too, since few insects of various classes provide identical looks. To cope with such challenges related to this work, a transfer learning-based ResNet50 network is presented to automatically detect, categorize, and consequently track the insect populations to take future measures. The model obtains a recognition accuracy of 91.45%. © 2024 IEEE.

关键词： Invertebrates

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Indian Currency Classification Using Deep Learning Techniques 12

Indian Currency Classification Using Deep Learning Technique...

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12th International Conference on Cloud Computing, Data science and engineering, Confluence 2022

作者： Swami, Rohit Khurana, Smiti Singh, Shubham Thakur, Sanjeev Sajjala, Pavan Kumar Reddy Lovely Professional University Computer Science And Engineering Delhi India Amity School Of Engineering And Technology Computer Science And Engineering Delhi India Delhi Technical Campus Computer Science And Engineering Delhi India Amity School Of Engineering And Technology Dept. Of Computer Science And Engineering Delhi India

ISBN: (纸本)9781665437011

Progression and evolution of technology has superseeded mechanical human workload in almost every domain with the operation of machines. The currency paper recognition is applicable in various domains of automatic selling goods systems and in banking systems. In the modern transition world for the automatic current recurring systems, the precise identification of paper currency notes is indeed an essential need. Machines often find it difficult in identifying and recognising the currencies in the market when the currency notes have turned bleary and damaged. It is hard for visually disabled people without any technological support or assistance to predict and analyze genuine currency notes. The accuracy of currency notes analysis identification have been refined and boosted throughout with the assistance of these models. Our research methodologies are in line and meeting the desired expectations. This paper presents an Indian Paper Currency Prediction Analysis, proposes an optimized model to recognise the currencies effectively. The Deep Learning approach of CNN model technique has improved the effective analysis of currency recognition with improved accuracy, high speed and efficiency along with complete automatic readily procedure with no human intervention and minimal complexity. This paper represents a strategy which is parted into two divisions, Keras trained a DL Model as well as hosted a Flask based web app on *** proposed algorithm design and experimental based results are useful for majorly visually impaired people for differentiating all sorts of available denominations. © 2022 IEEE.

关键词： Forecasting

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Optimal Lot Size and Backordered Quantity Under Carbon Tax

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International Journal of Applied and Computational Mathematics 2024年第4期10卷 1-16页

作者： Raiya, Geetanjali Mittal, Mandeep Department of Mathematics Amity Institute of Applied Sciences Amity University Uttar Pradesh Noida 201313 India Department of Mathematics School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India

Researchers and industry are working together to develop low carbon inventory models that comply with carbon pricing regulations while maintaining company profits. However, this task becomes more challenging when companies also face issues related to product quality imperfections. To address this issue, a new study has developed an economic order quantity (EOQ) model that takes into account carbon emission tax, defective rates, selling prices, imperfect items, scrap items and backordering. The objective of this mathematical model is reduce overall expenses, encompassing emissions-related costs. A numerical example is come up to adorn the model, based on previous data. Sensitivity analysis has also been performed to validate the model and understand system characteristics, identifying the factors that affect total emissions. © The Author(s), under exclusive licence to Springer Nature India Private Limited 2024.

关键词： Carbon tax Defective items EOQ Optimization Shortages

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Predicting Future Automobile Ownership Trends in Uzbekistan Using Linear Regression 2nd

Predicting Future Automobile Ownership Trends in Uzbekistan ...

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Ather, Danish Singh, Malikhan Arora, Kavita Pathak, Sonal Qurashi, Suhail Javed Singh, Anupam Amity University Tashkent Uzbekistan Computer Science and Engineering College of Engineering & Technology Mathura India School of Computer Applications Manav Rachna International Institute of Research and Studies Haryana Faridabad India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India

ISBN: (纸本)9789819735938

The rapid urbanization and economic growth of Uzbekistan over the past decades have engendered significant changes in automobile ownership trends. Understanding these trends is crucial for infrastructure planning, sustainable transportation strategies, and environmental considerations. This Paper seeks to employ Machine Learning (ML) techniques to forecast the number of cars per 100 households in Uzbekistan by 2025, based on historical data from 2010, 2015, and 2020. The dataset, derived from official statistics provided by the State Committee of the Republic of Uzbekistan on Statistics, shows a remarkable increase from 21 cars per 100 households in 2010 to 48 by 2020. Leveraging regression models, we aim to predict the expected growth for 2025. Preliminary analyses hint at a continuing upward trend, impacted potentially by factors such as Population growth, GDP growth, urban migration, and increasing global connectivity. The findings from this research will not only serve as a prediction tool but will also shed light on the socio-economic factors influencing car ownership, providing valuable insights to policymakers and urban planners. While the limited data points present a challenge for complex model utilization, the study underscores the potential of ML in forecasting and the need for more comprehensive data collection in future studies. The predictive insights drawn from this research can significantly aid in shaping transportation policies, mitigating potential environmental impacts, and planning urban infrastructures in Uzbekistan. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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Addressing the Unique Security and Privacy Challenges in Cellular Network Environments

Addressing the Unique Security and Privacy Challenges in Cel...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Rajashekar, N. Aravinda, K. Sirisha, N. Rana, Ajay Al-Rubaye, Taqi Mohammed Khattab Singla, Atul Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India New Horizon College Of Engineering Electronics And Communication Engineering Bangalore India Mlr Institute Of Technology Department Of Computer Science And Engineering Telangana Hyderabad India Amity School Of Engineering And Technology Amity University Greater Noida India The Islamic University Department Of Medical Laboratory Technology College Of Medical Technologies Najaf Iraq Lovely Professional University Phagwara India

ISBN: (纸本)9798350366846

This study offers a full solution to cellular network privacy and security challenges. The recommended remedy tackles numerous major issues with enhanced login protocols, sophisticated encryption, machine learning algorithms to discover threats, and privacy-protecting technology. The study's 98% success rate emphasizes the importance of solid identification for preventing unauthorized entry. Communication is safe and data is secure with modern encryption technologies like 256-bit AES. Machine learning helps the system identify and address security threats. This yields 95% danger detection. 'Very high' privacy is offered by privacy-protecting technologies and secure multi-party computing components that prevent data intrusions. Controlling network latency allows cellular networks to connect seamlessly and in real time, improving the user experience. The recommended solution balances computing speed and security without delaying the system. This research provides a thorough and practical method for making cellular network settings safer and more private. The diverse strategy of the offered solution makes it suitable for many purposes and provides more security and robustness in the ever-changing world of cellular networks. © 2024 IEEE.

关键词： Cryptography

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Dynamic Load Balancing for Enhanced Resource Utilization in IaaS Cloud Computing 7

Dynamic Load Balancing for Enhanced Resource Utilization in ...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Kumar, Anshul Mathur, Sandeep Malviya, Adarsh Noida International University Department of Computer Science in School of Science Uttar Pradesh Noida India Amity University Amity School of Engineering and Technology Uttar Pradesh Greater Noida India Dronacharya Group of Institutions Department of Computer Science Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350350067

Efficient load balancing is vital in cloud computing to optimize resource use, enhance performance, and maintain application availability. Traditional methods often underutilize the scalability of IaaS models. Hence, a dynamic load balancing system is crucial for real-time traffic distribution among IaaS servers based on demand and resource availability. High traffic challenges in web hosting can degrade load times. Dynamic server load balancing is pivotal here. Our approach integrates ASA genetic algorithms with process scheduling. It queues and assigns requests randomly to virtual servers, verifying connections. A genetic algorithm optimizes schedules via selection, crossover, and mutation, iterating for the best schedule. This strategy markedly reduces load times and maximizes system performance. © 2024 IEEE.

关键词： Dynamic scaling IaaS Load Balancing Web Services Virtual server

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An Intelligent Learning Approach to Case Analysis for Predicting Legal Outcomes Using Random Forest and K-Nearest Neighbors 4

An Intelligent Learning Approach to Case Analysis for Predic...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Bafna, Puneet Thakur, Hardeo Kumar Singh, Jagendra Sadana, Jannam Hassan, M. Muhsen Vani, M. Sree Amity Law School Amity University Jaipur India School of Computer Science Engineering and Technology Bennett University Greater Noida India Cvr College of Engineering Department of Information Technology Hyderabad India The Islamic University Najaf Iraq Bvrit Hyderabad College of Engineering for Women Department of Cse Hyderabad India

ISBN: (纸本)9798350387490

Machine learning is an increasingly popular area of research that holds great potential for enhancing the effectiveness and precision of legal analysis and decision-making. More specifically, machine learning can be used to predict the most probable outcomes of a legal case, and the purpose of this project is to utilize machine learning with case law data from the Chennai District Court to predict the outcomes of cases that involve criminal offences and the charge of murder. In other words, the goal of this project is to predict whether a case will result in the conviction of a victim from a list of case factors. For this research, several approaches to machine learning, including K-Nearest Neighbors, Support Vector Machines, Naive Bayes, Random Forests, and Decision Trees, are implemented to identify the best solutions for predicting the case outcomes. The case factors used in the research provided in the court judgments and applied various preprocessing tools to eliminate random factors that could lead to unreliable predictions. Finally, the various performance scores of the model are assessed, which includes the accuracy of each trained model in terms of precision, recall, Fl-score, and the Area Under the ROC Curve. As a result, about 95% of models displayed high accuracy rates, while KNN performed the best - 96.76% precision and 0.985 AUC-ROC, with SVM at 94.5% and 0.975 extremely close to perfection. In addition, NB, RF, and DT also showed good levels of accuracy at 92.1%, 88.9%, and 85.6%, respectively. This research confirms that machine learning can be highly effective in predicting legal outcomes and increasing legal practice accuracy. © 2024 IEEE.

关键词： Adversarial machine learning

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SSVBT: Secure Certificateless Aggregate Signature Scheme in VANETS with Block Chain technology 11

SSVBT: Secure Certificateless Aggregate Signature Scheme in ...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Mittal, Ruchi Malik, Varun Aeri, Manisha Kaswan, Kuldeep Singh Singh, S Vikram Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India Galgotias University Department of Computer Science and Engineering Uttar Pradesh Greater Noida India Amity School of Engineering and Technology Amity University Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350350357

The proliferation of Vehicular Ad-hoc Networks (VANETs) highlights the crucial need for secure, privacy-protecting inter-vehicle communication. By integrating blockchain technology with a safe certificate-less aggregate signature process, this research presents a novel approach to enhancing VANET security. Traditional VANET signature systems that rely on PKI and certificates have their limitations solved by the suggested approach. Using a certificate-less paradigm eliminates the requirement for a certificate authority when creating keys, making key exposure and compromise less probable. By combining several signatures into one smaller one, the aggregate signature method significantly decreases network communication cost. The paper introduces blockchain technology as an extra component to the certificate-less aggregate signature, further ensuring the honesty and openness of the VANET. The blockchain records and validates transactions, aggregate signatures, key alterations, and more using its distributed and immutable ledger. This not only improves the signature scheme but also creates a VANET environment that is trustworthy and robust. © 2024 IEEE.

关键词： Blockchain

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A Comprehensive Review on Autonomous Consolidation of Virtual Machine for Energy and Resource Management 23

A Comprehensive Review on Autonomous Consolidation of Virtua...

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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Gaurav Bajpai Pawan Singh Abhay Kumar Agarwal Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh India Computer Science & Engineering Kamla Nehru Institute of Technology India

ISBN: (纸本)9798400709418

Energy efficiency and awareness of power utilization are significant challenges while building up a cloud computing infrastructure and next-generation data centers. Consolidating Virtual Machines (VMs) is frequently used to lessen data centers’ negative impact on the environment and their utility bills. When idle or underused hosts enter sleep mode, it is essential to consider workload variations and energy-efficient and optimized resource utilization. Excessive load on an individual server decreases the Quality of Service (QoS). VM consolidation is supported by the procedures of placing and migrating virtual machines. Problems with variability and scalability of hardware facilities, load volatility, and migration complexity all contribute to the difficulty of the VM consolidation process. This study provides an in-depth look at the strategies and problems of virtual machine consolidation in terms of energy efficiency, resource allocation, migration, and placement schemes. This study proposes the reduction of carbon emissions, which directly-indirectly saves the environment.

关键词： Artificial Intelligence

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Advancing Residential Energy Management: Integrating Renewable Resources and Vehicle-to-Grid Systems for Optimized Electrical Grid Performance 7

Advancing Residential Energy Management: Integrating Renewab...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Chandrashekar, Rakesh Rani, D. Shobha Nijhawan, Ginni Balaram, A. Chaturvedi, Prateek Babu, B. Sankara New Horizon College of Engineering Department of Mechanical Engineering Karnataka Bangalore India Institute of Aeronautical Engineering Department of Electrical and Electronics Engineering Telangana Hyderabad India Lovely Professional University Punjab Phagwara India Mlr Institute of Technology Department of Computer Science & Engineering Telangana Hyderabad India Amity University Amity School of Engineering and Technology Uttar Pradesh Greater Noida India Griet Department of Computer Science and Engineering Bachupally Hyderabad India

ISBN: (纸本)9798350350067

The term 'smart grid' describes the future generation of electrical power networks, which are very complicated systems. It must take into account all aspects of the electrical system, increasing its intelligence and adaptability. In response to shifts in the electrical market, this idea is put out with the intention of controlling the rising demand while guaranteeing higher service quality and enhanced safety. This research presents an urban scenario that incorporates dispersed energy sources into a smart grid. The research incorporates renewable energy sources like solar panels and tiny windmills into the system, as well as battery storage and grid-connected vehicles that can power the electrical grid by drawing electricity from the storage device. So, to maximize the energy production and absorption systems as well as the recharging and discharging time for a home user's electric car, a mixed integer linear programming is proposed. Additionally, a number of case studies are provided by changing important variables using the Taguchi technique of design of experiments in order to identify the best solution and demonstrate how each influences the system's complexity. © 2024 IEEE.

关键词： Vehicle-to-grid

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