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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Purohit, Shardha Rajput, Aarfa Vats, Satvik Mudgal, Rusha Shah, Owais Ahmad Verma, Aditya Noida International University School of Journalism & Mass Communication Gautam Budh Nagar India Graphic Era Hill University Department of Computer Science & Engineering Dehradun India Amity University Amity School of Communication Manesar Gurugram India K. R. Mangalam University School of Engineering & Technology Gurugram India

ISBN: (纸本)9798350375190

This study conducts sentiment analysis on 50,000 movie reviews, comparing the traditional Random Forest classifier with a Long Short-Term Memory (LSTM) network. The Random Forest achieved robust metrics across accuracy, precision, recall, and F1-score (0.85), while the LSTM excelled in capturing the complex language patterns inherent in the reviews through its sequence-based processing. This study has refined the data by removing irrelevant characters and employing tokenization and lemmatization to enhance model training. Despite its higher computational requirements, the LSTM showed potential in detailed sentiment analysis. Future directions include exploring other neural architectures, enlarging the dataset, and practical applications to improve model efficacy. The study highlights key insights into sentiment analysis methodologies and their implications for both academic and practical realms. © 2024 IEEE.

关键词： Long short-term memory

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Analyzing the Impact of AI Adoption on Job Market Dynamics and Employment Trends Using a Synthetic Dataset

Analyzing the Impact of AI Adoption on Job Market Dynamics a...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Vikrant Chole Santosh Sahu Jhankar Moolchandani Ashok Kumar Rahul Kumar Anshita Shukla Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Madhya Pradesh Gwalior India

ISBN: (数字)9798350380583

ISBN: (纸本)9798350380590

This paper aims at establishing the effects of adopting artificial intelligence in the job market based on an artificially generated set of 500 jobs. In the following analysis, the impacts of AI Adoption levels on salary distributions, job growth, as well as the risks of job automation are examined with the help of such statistical methods as multiple linear regression, logistic regression, classification algorithms. The results show a positive relationship between AI utilization and job creation in industries where AI augments human work but reveal higher automation threats for industries with repetitive activities. Also, AI adoption corresponds to higher wages particularly in the advanced skill jobs but the jobs at high automation risk enjoy wage premium due to job insecurity. This paper acknowledges the fact that AI adoption is both a benefit and a threat that calls for careful executive actions on best strategies for managing changes in the workforce. Such future work should replicate these effects using actual data as well as religious and ethical effects on a wider scale.

关键词： Industries Logistic regression Ethics Automation Statistical analysis Linear regression Market research Remuneration Artificial intelligence Synthetic data

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Deep Learning Model for Weather Prediction

Deep Learning Model for Weather Prediction

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Computing, sciences and Communications (ICCSC), International Conference on

作者： Shubhanshi Singh Neha Tyagi dept. Computer Science and Engineering Amity School of Engineering and Technology Noida India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353655

Weather forecasting predicts atmospheric conditions at a particular time and place. It is an important task with a wide range of applications, including agriculture, transportation, and disaster prevention. Traditional weather forecasting methods rely on physical models of the atmosphere, which can be complex and computationally expensive. In recent years, deep learning techniques are effective for weather forecasting. Deep learning models can learn complex patterns from data, which can be used to predict future weather conditions accurately. This project aims to create a deep-learning model for forecasting weather. A huge dataset of historical meteorological data, including radar, satellite, and surface measurements, will be used to train the model. Numerous meteorological factors, such as temperature, precipitation, and wind speed, will be predicted by the model. We will compare the deep learning model's performance with conventional weather forecasting techniques. Creating a deep learning model that is more accurate and effective than current approaches is the project's main objective. The initiative may result in improved weather forecasts that are prompt and accurate, better agricultural, transportation, and disaster prevention decision-making, and less money lost because of weather-related incidents. Possible obstacles for the project are gathering and purifying a sizable dataset of meteorological data, using this data to train a deep learning model, and then comparing the model's output with more conventional techniques.

关键词： Deep learning Accuracy Computational modeling Atmospheric modeling Weather forecasting Transportation Predictive models Data models Agriculture Meteorology

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GPNST: An Improved Graphical Password Authentication Scheme Leveraging Neural Style Transfer Technique on Smartphones

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International Journal of Information Security 2025年第3期24卷 1-34页

作者： Ray, Palash Giri, Debasis Obaidat, Mohammad S. Jana, Manasi Sasmal, Mahuya Alenazi, Mohammed J. F. Haldia Institute of Technology West Bengal Haldia India Maulana Abul Kalam Azad University of Technology West Bengal Nadia India King Abdullah II School of Information Technology The University of Jordan Amman Jordan School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Department of Computational Intelligence School of Computing SRM University SRM Nagar TN Kattankulathur603203 India School of Engineering The Amity University UP Noida201301 India King Saud University Riyadh Saudi Arabia

The growing prevalence of smartphones and the increasing dependence on digital services have made it crucial to have authentication techniques that are both secure and user-friendly. This study examines the effectiveness and usability of graphical passwords named GPNST as an alternative authentication method on mobile devices. Graphical passwords use users’ capacity to remember visuals or patterns, offering a possible equilibrium between security and user pleasure. GPNST is a two-factor authentication technique that requires users to remember content and style images, or stylized images, as portfolio images. Additionally, a unique key token is assigned to each user device and securely sent to the server using the reversible data hiding approach, therefore mitigating several potential attacks. The research uses a combination of quantitative analysis to evaluate the strength of passwords and qualitative evaluations to measure user satisfaction and memorability. We investigate the influence of several design elements in graphical passwords, such as the intricacy of images and the layout of grids, on both the level of security and ease of use. In addition, the study evaluates the robustness of graphical passwords against popular forms of attacks, such as shoulder surfing and brute-force efforts. The findings provide valuable insights regarding the practicality of incorporating graphical passwords into smartphone authentication systems. This has implications for designers, developers, and users who are looking for secure and user-friendly solutions in the constantly changing field of mobile technology. The GPNST scheme demonstrates outstanding performance in our assessment test, with an accuracy rate of up to 94.80% and a login time of 9.61 seconds. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Static random access storage

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Ethical Hacking: Importance, Controversies and Scope in the Future 12

Ethical Hacking: Importance, Controversies and Scope in the ...

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12th International Conference on computer Communication and Informatics, ICCCI 2022

作者： Vishnuram, G. Tripathi, Khushboo Tyagi, Amit Kumar School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Chennai600127 India Amity University Department of Computer Science and Engineering Haryana India

ISBN: (纸本)9781665480352

With the ongoing digitalisation of the modern world and our quest to digitalise and automate everything, issues related to cybersecurity such as data breaches, security breaches etc., will be in the spotlight. Therefore, ethical hacking and its importance in the future can't be undermined. Ethical hacking technology has spread to almost all fields of the life and especially to all paths of computer industry;the need to protect the important data of the same should be addressed with right technology. In ethical hacking (i.e., white hat hacking) the objective in hand is to find weaknesses in the security systems and find potential data breaches and is in stark contrast to the almost universal definition of hacking i.e., to breach the security systems of individuals or companies with malicious intent and to steal data and plant viruses (black hat hacking). ethical hacking is a way of combatting and neutralising black hat hackers. With this research paper, my objective is to analyse and establish the importance of ethical hacking and also to analyse the controversies surrounding it, its pros and cons, its ethical and moral boundaries and its scope in the future. © 2022 IEEE.

关键词： Security systems

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Use of Neural Machine Translation in Multimodal Translation 6

Use of Neural Machine Translation in Multimodal Translation

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Nair, Manavi Tanwar, Sarvesh Badotra, Sumit Kukreja, Vinay Amity Institute of Information Technology Amity University Uttar Pradesh Noida India School of Computer Science and Engineering Bennett University U.P Greater Noida India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350304480

Multimodal Neural Machine Translation (MNMT) is a type of Machine Translation that allows the translation of source language that contains various forms of information, such as images, video, audio, and signs, into the target language while maintaining the intended meaning of the source language. In MNMT, the use of deep learning techniques such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are required to process different modalities and to generate an appropriate translation. The use of attention mechanisms, early and late fusion techniques, and hierarchical fusion have significantly improved the accuracy and fluency of translation. The performance of different MNMT systems can be evaluated in terms of various metrics such as BLEU score, accuracy, and fluency. MNMT has the potential to revolutionise the way we communicate by bridging the gap between different languages and cultures. The integration of MNMT in various industries, such as e-commerce, tourism, and healthcare, can bring significant benefits in terms of customer experience and accessibility. It states how Neural Machine Translation plays a significant role in processing different modalities in Multimodal Neural Machine Translation. More research work in this field can produce effective and innovative ideas to improve Multimodal Neural Machine Translation. © 2023 IEEE.

关键词： Machine Translation Multimodal Neural Machine Translation Natural Language Processing Introduction Neural Machine Translation

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Majority Voting-based Consensus Mechanism for UAVs Decision Making in Battlefield

Majority Voting-based Consensus Mechanism for UAVs Decision ...

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2023 IEEE International Conference on computer, Information, and Telecommunication Systems, CITS 2023

作者： Kakkar, Riya Gupta, Rajesh Obaidiat, Mohammad S. Jadav, Nilesh Kumar Tanwar, Sudeep Institute of Technology Nirma University Department of Computer Science and Engineering Ahmedabad India King Abdullah Ii School of Information Technology The University of Jordan Jordan School of Computer and Communication Engineering University of Science and Technology Beijing100083 China The Amity University Uttar Pradesh Noida201301 India

ISBN: (纸本)9798350336092

The revolutionary and rapid development of Information and Communication technology (ICT) has significantly contributed to modernizing military operations by improving the long-range communication and information sharing of unmanned aerial vehicles (UAVs) on the battlefield. However, efficient decision-making between clusters of UAVs controlled by the controller station (CS) is still one of the critical aspects that can cause potential threats to the armed forces on the battlefield. Thus, we have proposed a majority voting-based mechanism to attain consensus decision-making for UAVs, i.e., followers, monitored by the CS (leader) on the battlefield. Furthermore, we have focused on various voting scenarios to choose the efficient decision (attack or retreat) for the armed forces without any conflict between leader and follower utilizing the majority voting mechanism. Finally, the performance evaluation of the proposed mechanism is analyzed and simulated using the performance metrics of consensus delay and throughput. © 2023 IEEE.

关键词： Decision making

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A Comprehensive Analysis of Various Federated Learning Techniques

A Comprehensive Analysis of Various Federated Learning Techn...

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Advances in Computation, Communication and Information technology (ICAICCIT), International Conference on

作者： Neha Saini Dolly Sharma Deepti Mehrotra Aikaterini Bourazeri Amity School of Engineering and Technology Amity University Noida India School of Computer Science & Electronic Engineering University of Essex UK

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

A method for data privacy maintenance while training machine learning models across decentralized devices is Federated Learning (FL). It comes under the category of decentralized field of machine learning. The decentralized approach faces various challenges, one of them is aggregating model updates which come from diverse and potentially unreliable sources. This study provides a comparison review of various algorithms for privacy preservation, and maintaining security and also talks about the various federated averaging techniques. These techniques are essential for an efficient model aggregation. This research paper also provides an analysis of strengths, weaknesses, and various application areas of federated learning. This paper has included the maximum techniques available for federated learning by comparing their datasets and various other parameters. This paper also provides insight into the related future work that is proposed in various research articles.

关键词： Training Privacy Data privacy Machine learning algorithms Federated learning Reviews Intrusion detection Maintenance Information and communication technology Security

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Analysis of Big Data in Cloud Computing Technologies

Analysis of Big Data in Cloud Computing Technologies

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International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Sanjeev Thakur Lalit Verma Computer Science & Engineeering Amity School of Engineering & Technology Noida India

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350364

The abstract provides a brief summary of the research paper, highlighting the key topics covered. It explains that the paper will give a brief description of the various technologies and tools used for Big Data in cloud computing environments, as well as the advantages and difficulties posed by this technology. This paper reviews the research challenges and issues associated with big data analysis in situations using cloud computing. It discusses both the traits and benefits of big data analytics in cloud computing, and highlights the major research challenges such as scalability, security, privacy and resource management. The paper also presents some failure research directions and potential solutions to address these challenges.

关键词： Cloud computing Data privacy Reviews Scalability Big Data Market research Reliability

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The Impact of Industrial Automation on the Manufacturing Industry in the Era of Industry 4.0 15

The Impact of Industrial Automation on the Manufacturing Ind...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Gurav, Vidya Sunil Gugnani, Anchit Meena, Y.R. Marathe, Vijay Vijay, S. Arul Antran Nanda, Simranjeet D.K.T.E.S's Textile and Engineering Institute Department of Management Studies Ichalkaranji416115 India Amity Institute Of Physiotherapy Amity University Rajasthan Jaipur302020 India School of Engineering and Technology Department of Civil Engineering Karnataka Bangalore India Vishwakarma Institute of Technology Pune India Karpagam College of Engineering Department of Computer Science Engineering Coimbatore641032 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350370249

With the dawn of Industry 4.0, industrial automation has become one of the most disruptive technologies in the manufacturing industry. It encompasses the use of innovative technology and methods to automate different processes related to production, which decreases dependence on human laborers and ensures increased production results with greater efficiency. It was a game changer for the industry - industrial Automation that transformed product design, production, and delivery. A significant positive aspect of using industrial Automation is that it improves the speed and accuracy at which production occurs. Robotization, artifidal intelligence, and modern equipment combined to allow manufacturing to continue mumuchquicker while being more accurate. It helps reduce production time, bringing down wastage and errors due to cost-saving, leading to better quality control. The integration resulted in Industrial automation leading towards Smart Factories where machines & systems are interconnected and communicate banks on each other. This has enabled the manufacturers to monitor and optimize their production processes, ensuring their organization planned maintenance over preventive repairs, avoiding machinery failures, or putting suppliers in check with plummeting demand. Industrial Automation Increasing Potential Job Displacement and Reskilling/Upskilling of Workers. © 2024 IEEE.

关键词： Industry Machinery Manufacturing Robotization technology

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