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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Tripathi, Ashish Kumar, Kuldeep Misra, Anuradha Chaurasia, Brijesh Kumar Amity University Lucknow Amity School of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Lucknow India Bond University Center for Data Analytics Bond Business School Gold CoastQLD Australia Pranveer Singh Institute of Technology Department of Computer Science and Engineering Uttar Pradesh Kanpur India

ISBN: (纸本)9798350346961

In this paper, the classification of colon cancer tissues by means of machine learning approaches is evaluated. In today's world, a revolutionary advancement has come in the classification and diagnosis of diseases in the medical and healthcare sectors. Deep learning classifiers and machine learning methods are now broadly applied to accurately diagnose a number of diseases. Cancer is one of the world's most significant roots of death, appealing to the lives of one person out of every six. As per the national library of medicine, the third leading cause of death worldwide is colorectal cancer. Identifying an illness at a premature stage increases the chances of survival. Automated diagnosis and the classification of tissues from images can be completed much more quickly with the use of artificial intelligence. A publicly available IoT dataset CRC-VAL-HE-7K consisting of 7180 images, distributed among nine types of colorectal tissues: background, lymphocytes, adipose, mucus, colorectal adenocarcinoma epithelium, normal colon mucosa, debris, cancer-associated stroma, and, smooth muscle is used after preprocessing. Feature extraction is done by applying Differential-Box-Count on all blocks of images. The dataset is evaluated by these Machine Learning (ML) procedures: K-Nearest Neighbor, Support Vector Machine, Decision Tree, Random Forest, Extreme Gradient Boosting, and Gaussian Naive Bayes. Results show that the performance of Extreme Gradient Boosting is the best and most viable approach.. © 2023 IEEE.

关键词： Image classification

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Scalability Performance Analysis of Blockchain Using Hierarchical Model in Healthcare

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Blockchain in Healthcare Today 2024年第1期7卷 7-7页

作者： Sadath, Lipsa Mehrotra, Deepti Kumar, Anand Computer Science Engineering Department School of Engineering Amity University Dubai United Arab Emirates Computer Science Engineering Department Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India Electronics Engineering Department School of Engineering Amity University Dubai United Arab Emirates

Blockchain technology has become crucial in improving the privacy and security of enterprise applications in the cyber world. However, scalability has become a significant concern for researchers in large organizations, especially those with complex hierarchies and access privileges. As a result, the existing models and consensus algorithms suffer from various issues. Medical centers and healthcare providers are particularly affected by this problem due to the vast amount of data, making it a critical weakness of traditional database management systems. To address this issue, the authors propose a hierarchical model within the Hyperledger Fabric enterprise application, focusing on the healthcare sector as a use case. This model includes multiple organizations at different levels of the hierarchy, such as hospitals, hospital governance, and insurance companies. The initial implementation of this model includes two levels of hierarchy, demonstrating networks of hospitals joining an insurance company. The primary objective of the experiment is to test and improve the network’s performance using this model. The model’s performance is evaluated by manipulating and scaling environmental factors such as the number of organizations, transaction numbers, channels, block intervals, and block sizes. The benchmarking tool used for this assessment is Hyperledger Caliper, which measures indicators such as success and failure rates, throughput, and latency. Currently, the research focuses only on testing the model’s scalability using patient data. © 2024, Partners in Digital Health. All rights reserved.

关键词： Blockchain

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An Analysis of Object Detection in Bad Weather Conditions using Deep Learning Models

An Analysis of Object Detection in Bad Weather Conditions us...

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International Conference on Confluence The Next Generation Information technology Summit (Confluence)

作者： Janvi Verma Harsh Verma Supriya Raheja Computer Science Engineering Amity School of Engineering and Technology Amity University Noida India

Object detection, a task, in the field of computer vision faces obstacles when dealing with weather conditions such as fog, rain, snow, and low light situations. This paper provides an overview of advancements in the realm of object detection under challenging weather conditions. It delves into groundbreaking research that utilizes learning techniques adjustments to frameworks and the significance of datasets. This paper proposed the YOLOv5 model to detect the object in bad weather conditions. The paper presents the comparison between the YOLOv5 model presented in literature and the proposed YOLOv5 model in terms of accuracy, precision, recall and F1-score. The evaluation results prove the better performance of our proposed model.

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A New Approach for EOQ Calculation Using Modified Opportunity Cost

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Recent Advances in computer science and Communications 2022年第1期15卷 106-110页

作者： Singh, Juhi Mittal, Mandeep Pareek, Sarla Amity School of Science Engineering and Technology Haryana India Amity School of Computer Sciences Haryana Noida India Department of Mathematics and Statistics Banasthali University Rajasthan Jaipur India

Introduction: Optimal inventory levels are necessary for a firm to avoid short-age/excess of an item. The shortage of an item leads to stock out conditions resulting in loss of profit. When items are correlated with each other, the stock out condition of one item may result in the nonpurchase of its associated items also which, in turn, further brings down the profit. In this paper, this loss in profit is used to modify the opportunity cost of an item resulting in its modified EOQ. Methods: One illustrative example has been discussed which incorporates purchase dependencies in retail multi-item inventory management. The model discussed in this research paper will be motivational for researchers and inventory managers and provides a method for incorporating correlation among items while managing inventory. Results: The EOQs of items are estimated both by using the traditional method and then by using modified opportunity cost (modeled as loss profit). Results show that in frequent itemset {A, B, D}, EOQs of all three items increased when correlation among them is considered, resulting in an increase in the profit. Discussion: In inventory management system, for increasing the profit of a firm, EOQs of items need to be calculated in order to avoid shortage or excess of inventory. For explaining the approach, a very small database is taken consisting of only 5 items and 10 transactions, therefore, the increase in profit is minimal however when this approach is applied on a real database consisting of thousands of items and transactions, the increase in profit will be significant. Conclusion: One of the major focus areas of inventory management is to determine when and how much quantity of items needs to be ordered so that total inventory cost can be minimized and the profit of a firm can be maximized. However, while calculating the true value of an item and the profit it brings to the firm, it is very essential to analyze its effect on the sale of other items. Associat

关键词： Profitability

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Predictive Modelling of Economic Indicators using Machine learning and Data Analytics 4

Predictive Modelling of Economic Indicators using Machine le...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Singh, Akshita Singh, Ramendra Chauhan, Davendra Kumar Shrivastava, Ashok Kumar Alabdeli, Haider Amity International School Noida India Ghaziabad India Engineering Raj Kumar Goel Institute of Technology Department of Electrical & Electronics Ghaziabad India Engineering Amity University Department of Computer Science Gwalior India The Islamic University Najaf Iraq

ISBN: (纸本)9798350364729

This study defines the use of machine learning models to predict car production volume for better planning and correspond them with market desirability. This work assesses three different models: Artificial Neural Network (ANN), Support Vector Machine (SVM) and Long Short-Term Memory (LSTM), using an extensive dataset composed of 10,332 historical production volume records over the past ten years accompanied by raw material prices, energy costs along with labor cost attributes as well as experiments will market demand, seasonal trends economic indicators. The dataset was preprocessed with some techniques like handling of missing data, normalization and also encoding for categorical variables to make it strong. The models were trained and tested using a 70-30 training - test split, evaluated on different performance metrics such as accuracy, precision and other criteria. The bestcase accuracy of 97.75 % was achieved by the ANN, hence showing its capability to capture complex non-linear relationships within data. The SVM reached 93.40% accuracy, much stronger with high precision and recall tradeoff post update to the split point. The LSTM model providing an accuracy rate of 91.22%, considering it was best suited for handling sequential data. The above results have shown the ANN model better accuracy in predictions and because of this, we can use an ANN for future demand prediction efficiently. This has valuable implications for car manufacturing and is a good direction to elaborate on predictive models in the future. © 2024 IEEE.

关键词： Support vector machines

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Deep Learning (CNN) and Transfer Learning: A Review

Deep Learning (CNN) and Transfer Learning: A Review

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2022 International Conference on Applications of Intelligent Computing in engineering and science, AICES 2022

作者： Gupta, Jaya Pathak, Sunil Kumar, Gireesh Amity School of Engineering and Technology Department of Computer Science & Engineering Amity University Rajasthan Jaipur India Institute of Engineering & Technology Department of Computer Science & Engineering JK Lakshmipat University Jaipur Rajasthan India

Deep Learning is a machine learning area that has recently been used in a variety of industries. Unsupervised, semi-supervised, and supervised-learning are only a few of the strategies that have been developed to accommodate different types of learning. A number of experiments showed that deep learning systems fared better than traditional ones when it came to image processing, computer vision, and pattern recognition. Several real-world applications and hierarchical systems have utilised transfer learning and deep learning algorithms for pattern recognition and classification tasks. Real-world machine learning settings, on the other hand, often do not support this assumption since training data can be difficult or expensive to get, and there is a constant need to generate high-performance beginners who can work with data from a variety of sources. The objective of this paper is using deep learning to uncover higher-level representational features, to clearly explain transfer learning, to provide current solutions and evaluate applications in diverse areas of transfer learning as well as deep learning. © Published under licence by IOP Publishing Ltd.

关键词： Hierarchical systems

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Automated Diagnosis of Marigold Leaf Diseases using a Hybrid CNN-SVM Model 8

Automated Diagnosis of Marigold Leaf Diseases using a Hybrid...

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Banerjee, Deepak Kukreja, Vinay Sharma, Vandana Jain, Vishal Hariharan, Shanmugasundaram Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Amity University Amity Institute of Information Technology Noida Campus India Sharda University Sharda School of Engineering and Technology Department of Computer Science and Engineering UP Greater Noida India

ISBN: (纸本)9798350396638

For healthy yields of crops and to maintain food security, it is essential to identify and classify plant diseases. In this research, the aimed to create a convolutional neural network-based deep learning algorithm for accurately classifying various marigold leaf diseases. 2000 images of marigold leaves were included in the dataset, with 200 images for each of the ten groups. (nine diseases and one healthy leaf). Six layers of convolution, six maximum-pooling layers, and two fully linked layers with regularisation and ReLU function activation made up the suggested CNN model. The efficiency of the model was optimized through hyperparameter tuning. Using the Keras framework, the model was trained and evaluated on the preprocessed dataset, and its performance was assessed using metrics like precision, recall, F1-score, as well as accuracy. According to the findings, the developed model classified the various classes of marigold leaves with an overall accuracy of 92%, with precision, recall, as well as F1-score metrics varying from 50% to 71.79% depending on the class. The confusion matrix gave a thorough summary of how well the model did at identifying each of the various marigold leaf classes. The created model can be applied to the early discovery of diseases affecting marigold leaves, aiding in both disease containment and yield protection. This research also emphasizes how well deep learning models work at accurately identifying and categorizing various plant diseases. © 2023 IEEE.

关键词： Convolutional neural networks

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Evaluating and Optimizing the Performance of Dual-Hop LoRa Network using Genetic Algorithm 2

Evaluating and Optimizing the Performance of Dual-Hop LoRa N...

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2nd International Conference on Innovative Practices in technology and Management, ICIPTM 2022

作者： Kaur, Gagandeep Gupta, Sindhu Hak Kaur, Harleen Amity School Of Engineering And Technology Amity University Noida India School Of Engineering Sciences And Technology Jamia Hamdard Department Of Computer Science And Engineering New Delhi India

ISBN: (纸本)9781665466431

Aspiring to support vast IoT applications, Long Range (LoRa) developed by Semtech has been most adopted LPWAN technology as it provides low power consumption, long transmission range, massive scalability and low deployment cost. The current work focuses on enhancing the LoRa network performance by employing cooperative communication. Cooperative communication has emerged as the promising technology enabling efficient utilization of the communication resources by allowing the end devices to cooperate with each other in data transmission. The performance of dual hop LoRa network is investigated in terms of coverage probability and latency. Further to optimize the performance of the network, Genetic Algorithm (GA) has been utilized for both non-cooperative and cooperative scenarios. A mathematical model has been formulated and based on that an optimization problem has been defined in terms of estimated received power which gives the optimal transmission configuration of the LoRa node at which the received power is maximum. The optimized value of the received power is further utilized to optimize the performance of the network in terms coverage probability and latency. MATLAB simulations are carried out to highlight the goodness of the proposed model. Simulation results shows that by employing cooperative communication the performance of the LoRa network enhances by 22% and 8% improvement in coverage probability and latency respectively. Also, a significant improvement in coverage probability and latency can be observed in both non-cooperative and cooperative scenarios by employing GA. © 2022 IEEE.

关键词： Genetic algorithms

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Cloud Resource Management: Monitoring 23

Cloud Resource Management: Monitoring

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

作者： Anshika Rawat Shubham Singh Pawan Singh Department of Computer Science and Engineering Amity School of EngineeringandTechnology Department of Computer Science and Engineering Amity School of Engineering and Technology Lucknow Amity University Uttar Pradesh India India Computer Engineering and Application Gla University India Department of Computer Science and Engineering Amity School of of Engineering and Technology Amity University Uttar Pradesh India

ISBN: (纸本)9798400709418

The management and use of computer resources within enterprises has changed as a result of the emergence of cloud computing as a paradigm shift. With its diverse and comprehensive portfolio of cloud services, including AWS CloudWatch for monitoring, Amazon Web Services (AWS) is at the vanguard of this revolution. In order to fully utilize the capabilities of these platforms and achieve cost- effectiveness, scalability, and optimal performance, proper cloud resource management is essential. This, study offers a thorough investigation of AWS's cloud resource management, with a focus on Particle Swarm Optimization (PSO) integration as a cutting-edge optimization technique. The dynamic and ever-changing workloads seen in cloud computing systems present a special set of issues for resource allocation and optimization. However, finding the ideal compromise between performance and cost-efficiency is still a challenging task. This study examines the changing environment of cloud resource management with an eye toward the future. In order to enable even more intelligent resource allocation decisions, we take into account the possible integration of machine learning and predictive analytics into our PSO framework. We also look at the broader implications of PSO-driven resource management.

关键词： Amazon Web Services (AWS)

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Privacy-Aware Artificial Intelligence with Homomorphic Encryption using Machine Learning

Privacy-Aware Artificial Intelligence with Homomorphic Encry...

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2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Srinivasa Rao, B. Chattopadhyay, Saumitra Singh, Prashant Hazela, Bramah Sabarinathan, G. Yamini, Kalva Department of Computer Science and Engineering Bachupally Telangana Hyderabad India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Amity University Amity School of Engineering and Technology Lucknow Department of Computer Science and Engineering Uttar Pradesh India Psna College of Engineering and Technology Department of Mathematics Tamilnadu Dindigul India Sri Ramachandra Institute of Higher Education and Research Cyber Security in Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350333602

Along with the expansion of machine learning (ML) applications, the amount of data required to create predictions increases. Big-data ML has always been limited by off-chip memory capacity and computational speed. Considerably, privacy is one of the limitations of big data, which can be solved by homomorphic encryption (HE). Due to the combination of HE and ML, the multi-party privacy-protected ML suggested in this research may assist numerous users in doing artificial intelligence (AI) without disclosing private data. The technique may train common models in situations of data abuse, particularly in private data protection. The model trained using the ML technique named Artificial Neural Network (ANN) has a similar impact to the model developed using all data on a single computer, according to experiments using the algorithm. The gradient data is simply transmitted by all parties, and homomorphic procedures in the main computing system combine the gradient data. Besides, the optimal key is selected using the significance of the Lion Algorithm (LA). After homomorphic procedures, the learning model is modified depending on the new gradient data. © 2023 IEEE.

关键词： Neural networks

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