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A Smart Blockchain Model for Supply Chain Management System 4th

A Smart Blockchain Model for Supply Chain Management System

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4th International Conference on Data, engineering, and Applications, IDEA 2022

作者： Ayo, Femi Emmanuel Awotunde, Joseph Bamidele Misra, Sanjay Agrawal, Akshat Department of Mathematical Sciences Olabisi Onabanjo University Ogun State Ago Iwoye Nigeria Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Kwara State Ilorin240003 Nigeria Department of Applied Data Science Institute for Energy Technology Halden Norway Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University Haryana Gurugram122412 India

ISBN: (纸本)9789819724505

Industry 4.0 is the creation of smart industrial processes. Therefore, supply chain management describes a chain of activities from the purchase of raw materials to the distribution of finished goods. The problems with the existing manual supply chain system include consistency, timeliness, traceability, security, and trust among all supply chain actors. This study presented the integration between a rule-based expert system and blockchain technology called SupplyOnBlock for supply chain management. The rule-based expert system was proposed to create smart industrial processes and blockchain technology to create fast, traceable, and secure supply chain processes. The rule-based expert system is to create a smart individual process in the supply chain while the blockchain is to coordinate the individual smart processes in a network of blockchains. The proposed SupplyOnBlock model for supply chain management is designed to use the public key cryptographic hash function for encryption of key processes to avoid non-repudiation at any point in the SupplyOnBlock. The information to be transferred on the block is first hashed into a value to compress its volume then the public key cryptography is used to encode and decode the hashed information. The proposed encryption scheme should be able to validate the legitimacy of the sender using the private key of the public key cryptography algorithm. The receiver should be able to validate the legitimacy of the sender using the public key of the public key cryptography algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain

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Interpretation of Sign Language Using Machine Learning

Interpretation of Sign Language Using Machine Learning

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International Conference on Computational and Characterization Techniques in engineering & sciences (CCTES)

作者： Palak Srivastava Bramah Hazela Shikha Singh Pallavi Asthana Deependra Pandey Kamlesh Kumar Singh Vineet Singh Department of Computer Science Engineering Amity School of Engineering & Technology Amity University Uttar Pradesh India Department of Electronics & Communication Engineering Amity School of Engineering & Technology Amity University Uttar Pradesh India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

Sign language detection using machine learning has emerged as a crucial area of research aimed at bridging communication barriers between individuals with hearing impairments and the broader community. This paper explores the application of machine learning techniques for the automatic recognition and interpretation of sign language gestures. A systematic review of existing methodologies, including traditional machine learning approaches and recent advancements in deep learning architectures, reveals key challenges such as variability in signing styles, regional dialects, and real-time processing requirements. To address these challenges, we propose a novel approach that leverages convolutional neural networks (CNNs) and long short-term memory (LSTM) models, which have demonstrated significant improvements in accuracy and generalization across diverse datasets. Our experimental results show the efficacy of these models on benchmark sign language datasets, achieving high levels of accuracy and robustness in real-world conditions. Furthermore, this study discusses the potential societal impacts of sign language detection systems in education, healthcare, and everyday communication, emphasizing the role of technological innovation in fostering inclusivity. By advancing machine learning methodologies, we contribute to the development of accessible communication solutions for individuals with hearing impairments.

关键词： Sign language Technological innovation Accuracy Education Machine learning Auditory system Medical services Real-time systems Convolutional neural networks Long short term memory

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Advanced Predictive Modelling for Bitcoin and Ethereum Price Dynamics

Advanced Predictive Modelling for Bitcoin and Ethereum Price...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Mohd Waseem Shailendra Narayan Singh Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Noida Uttar Pradesh India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Over the last decade, Bitcoin and Ethereum have become cryptocurrencies that have attracted the attention of the financial world with their potential for business transactions and the use of new blockchain technology. This study is dedicated to predicting Bitcoin and Ethereum price trends from 2014 to 2024, covering the main period of growth and change in the cryptocurrency market. This research aims to develop a good forecasting model through a comprehensive analysis of historical price data, market trends, economic progress and macroeconomy. It uses techniques such as machine learning, time series analysis, and sentiment analysis to try to predict future price movements of Bitcoin more accurately and Ethereum. By revealing the fundamental principles influencing cryptocurrency prices, this research leads to a deeper understanding of the dynamics driving digital assets and their impact on the financial sector.

关键词： Economics Sentiment analysis Time series analysis Bitcoin Machine learning Predictive models Market research Blockchains Informatics Forecasting

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Automated Malaria Detection Using Deep Learning: A Convolutional Neural Network Approach for Microscopic Cell Image Analysis

Automated Malaria Detection Using Deep Learning: A Convoluti...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Mahajan, Manish Kalra, Amandeep Singh Bansal, Ankit Bhattacherjee, Abhishek Vishal Jain, Eshika Amity School of Engineering and Technology Amity University Punjab Mohali India Gulzar Group of Institutions Punjab Ludhiana India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Lovely Professional University Department of Computer Science Engineering Punjab Phagwara India Chitkara University Institute of Engineering and Technology Centre for Research Impact & Outcome Chitkara University Punjab Rajpura140401 India

ISBN: (纸本)9798350369106

This study explores using a Convolutional Neural Network (CNN) for automated malaria detection from microscopic cell images. A dataset of 27,558 images was split into training, validation and testing subsets, all with balanced class representation. The testing dataset was then used to evaluate the model, which achieved an overall accuracy of 94.81% and precision, recall and F1 scores of 0.94 and 0.94 for classes 'Uninfected' and 'Parasitized', respectively. The confusion matrix showed that it was reliable, especially for minimizing the false negatives for infected cases. These results highlight the potential of the model to decrease dependence on manual microscopy, increase diagnostic consistency and enable large-scale malaria screening. Through this research, we show the feasibility of integrating deep learning into malaria detection workflows while expanding access to healthcare in underserved regions. Future works include deployment optimization, integration with mobile applications, and validation on multiple site datasets to increase generalizability. The results of this work pave the way for artificial intelligence-powered diagnostics, a burgeoning field, and present a cost-effective, efficient, and scalable solution to combat malaria in high-burden areas. © 2024 IEEE.

关键词： Automated Diagnosis Convolutional Neural Network (CNN) Deep Learning in Healthcare Malaria detection Microscopic Cell Image Analysis

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Analysis and Impact of Electromagnetic Field Leakage in WPT on Central Nervous System: A Neural Network Approach

Analysis and Impact of Electromagnetic Field Leakage in WPT ...

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Sustainable Energy and Future Electric Transportation (SEFET), International Conference on

作者： Garima Shukla Vanshaj Awasthi Prafull Goswami Sofia Singh Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Mumbai Maharashtra India Department of Computer Science and Engineering Indian Institute of Information and Technology Sonipat Department of Artificial Intelligence Amity School of Engineering and Technology Amity University Noida Uttar Pradesh

ISBN: (数字)9798350383997

ISBN: (纸本)9798350384000

The potential for wireless power transfer (WPT) technologies to transform the charging capabilities of a wide range of applications, from consumer electronics to medical devices has attracted a lot of attention. But uncertainties remain about how these technology's electromagnetic field (EMF) leakage affects human health, especially the central nervous system (CNS). To find a way to assess and forecast all of these consequences, this study explores at the possible effects of EMF leakage from WPT systems on the central nervous system (CNS) and suggests a distinctive approach that employs the applications for neural networks. Using real- world data and computational models, this study will produce an accurate model of EMF absorption and propagation in biological tissues, with a specific focus on the central nervous system. By focusing insights into potential risks to health and informing the emergence of requirements which assure the safe implementation of wireless power technologies, the research will enhance our understanding of the connections between EMF exposure and the central nervous system. The ultimate objective of this study is to encourage safer and more sustainable wireless charging technologies by enhancing the accuracy and efficiency of risk assessment in circumstances of EMF exposure from WPT systems through the implementation of Machine Learning (ML) strategies

关键词： Wireless communication Surveys Electric potential Accuracy Inductive charging Central nervous system Wireless power transfer Machine learning Electromagnetic fields Biological neural networks

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A Machine Learning Approaches for Natural Language Processing and Sentiment Analysis in Social Media 7

A Machine Learning Approaches for Natural Language Processin...

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

作者： Manoharan, Geetha Sarkar, Prithu Pathak, Punit Ahmad, Khadri Syed Faizz Singh, Arun Vaishnavi, M. Sr University School of Business Telangana Warangal India Amity University Amity School of Communication Kolkata India Auro University School of Liberal Arts and Human Sciences Surat India Srm University Department Computer Science and Engineering Andhra Pradesh India Vikrant University M.P Gwalior India Bannari Amman Institute of Technology Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798350350067

In the recent decade, there has been a rise in social media and various companies are using it as a primary tool for marketing. In order to better understand the emotion and views of the customers, social media sentiment analysis has been used. Machine learning is a rising technology which helps in sentiment analysis in the social media which helps to easily categorize the views and opinions of the customers in certain categories. The major objectives of the study include exploring Machine Learning Approaches for Natural Language Processing and Sentiment Analysis in social media. In this research, the methods of sentiment analysis in online social networks using ML and techniques for grouping have been studied in detail. A secondary research approach has been followed for the research. The major benefits of sentiment analysis on social media have also been presented as a part of the report. In the research it has been found that there are two major types of clarifying sentiments which are supervised and unsupervised learning. The research also reveals that the best learning model for evaluating the Twitter dataset is determined to be CNN using Word to Vector and under instruction. © 2024 IEEE.

关键词： Adversarial machine learning

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An Approach to Generate Music using Genetic Algorithm 10

An Approach to Generate Music using Genetic Algorithm

引用

10th International Conference on Reliability, Infocom Technologies and Optimization ,Trends and Future Directions, ICRITO 2022

作者： Gupta, Sumita Majumdar, Rana Gambhir, Sapna Amity School of Engineering & Technology Amity University Department of Computer Science & Engineering Noida India Sister Nivedita University Department of Computer Science and Engineering New town Kolkata India J.C. Bose University of Science and Technology Department of Computer Engineering Faridabad India

ISBN: (数字)9781665474337

ISBN: (纸本)9781665474337

Music is a very vast domain with various notes, chords, and octaves. Music theories are very complex in nature and are difficult to understand by a layman, in this research paper the aim was to implement Genetic algorithm for generating fresh and unique music generation which is independent of human intervention. The work will automate the music generation capabilities. The algorithm works on different music parameters like pitch, frequency, beats, and notes in order to generate more relatable and interesting melodies by understanding the relationship between different given parameters. The algorithm can generate music melodies with constant rating analysis of the music generated by the algorithm. Each analysis phase helps in better and synchronized music generation. The algorithm takes inspiration from the theory of Natural selection. © 2022 IEEE.

关键词： Genetic algorithms

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Optimization for IoT Jobs in Multi-Cloud Computing Environments

Optimization for IoT Jobs in Multi-Cloud Computing Environme...

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Signal Processing and Advance Research in Computing (SPARC), International Conference on

作者： Anshul Atre Kamlesh Kumar Singh Brijesh Kumar Chaurasia Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University Lucknow UP INDIA Department of Electronics Communications and Engineering Amity School of Engineering & Technology Amity University Lucknow UP INDIA Department of Computer Science & Engineering Pranveer Singh Institute of Technology Kanpur India

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

Effective network communication is essential in the current digital era, and cloud computing (CC) and the Internet of Things (IoT) are significant aspects of daily life. Accelerating and lowering the latency of data transfers to the cloud for processing, analysis, and storage is the aim of cloud computing. IoT integration and job scheduling are the two most challenging issues in the multi-cloud computing environment because of the diverse and numerous IoT devices. The significant objectives of cloud computing-based IoT optimization are to reduce the total time needed to complete all input operations, determine which resources are required for the Message Queuing Telemetry Transport (MQTT), and other crucial protocols used in this work to display delay and evaluate the performance of different jobs at different IoT nodes and bit counts. Results and analysis show the viability of cloudSim is successfully achieved with the use of delayed quality of communication of jobs for different nodes.

关键词： Cloud computing Protocols Processor scheduling Quality of service Signal processing Internet of Things Telemetry Security Resource management Optimization

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Recognition of Valid QR Codes with Machine Learning

Recognition of Valid QR Codes with Machine Learning

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Arshia Minocha Aman Goyal Rashmi Gandhi Computer Science and Engineering Amity School of Engineering and Technology Amity University AUUP Noida Uttar Pradesh India

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305470

The widespread use of Quick Response (QR) codes has made QR codes an attractive target for cyberattacks, posing a security and privacy concern. Quick Response (QR) codes have revolutionized marketing strategies by providing a fast and efficient way to connect offline and online platforms. However, the security issues arise as the usage of QR codes. This paper introduces an automatic detector that identifies QR code images and lets the user know whether it is malicious or benign. To achieve this, a dataset of 10,000 original and malicious QR code images (both) is taken. Convolutional Neural Network (CNN) is employed to classify original and malicious codes. Additionally, histogram density analysis is incorporated to enhance feature identification, leading to improved classification accuracy. This paper implemented ResNet50, MobilenetV3 and InceptionV3 CNN models for evaluating the abilities to predict the malicious codes. The InceptionV3 model emerges as the most promising, attaining an accuracy of 98.13%, offering an efficient and accurate solution for document forensics and counterfeit detection.

关键词： Training Histograms Technological innovation QR codes Malware Real-time systems Security

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Early Detection of Cognitive Skill Impairments using Machine Learning

Early Detection of Cognitive Skill Impairments using Machine...

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International Conference on Computing Communication Control and Automation (ICCUBEA)

作者： Sunita Patil Swetta Kukreja Department of Computer Science & Engineering Amity School of Engineering and Technology Amity University Maharashtra Mumbai Maharashtra India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

Cognitive skills are essential for everyday tasks, encompassing mental abilities such as memory, problem-solving, and logical thinking. Conditions such as depression, anxiety, and schizophrenia can impair these skills, affecting memory, attention, and problem-solving abilities. The COVID pandemic and other stress-inducing events have a great impact on individuals, often leading to stress-related diseases. Depression often stems from heightened stress levels, yet individuals may struggle to recognize or seek help due to social stigma. Delayed treatment impairs outcomes. Surveys indicate low success rates in treating cognitive skill impairment. Early identification through routine cognitive testing is crucial, especially for vulnerable individuals due to age, family history, or health conditions. Regular screenings facilitate proactive healthcare management, leading to improved outcomes in patients with cognitive skill impairments. This study explores the application of Support Vector Machine (SVM) algorithms for the early detection of cognitive skill impairments, aiming to develop accurate prediction models for identifying individuals at risk of cognitive decline. By employing feature selection methods, the SVM achieved an 83% accuracy rate. This study aimed to advance the development of efficient cognitive assessment tools, enabling early intervention and personalized care for individuals vulnerable to cognitive impairment.

关键词： Support vector machines COVID-19 Accuracy Pandemics Predictive models Prediction algorithms Feature extraction Depression Problem-solving Testing

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