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Origins to Innovations: A Comprehensive Journey Into Artificial Intelligence's Future Application

Origins to Innovations: A Comprehensive Journey Into Artific...

2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Saraswat, Shivam Mehra, Ritika Gurpreet Yuvaraj, K. Karthigaikumar, P. Wankhede, D.S. Department of Civil Engineering School of Engineering & Technology K. R. Mangalam University Haryana Gurugram India School of Engineering and Computer Dev Bhoomi Uttarakhand University Uttarakhand 248007 India Department of Computer Applications Invertis University Uttar Pradesh Bareilly India Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Department of Electronics and Communication Engineering Karpagam College of Engineering Coimbatore India Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350355338

In the contemporary era characterized by the proliferation of extensive data sets and the advent of Industry 4.0, artificial intelligence (AI) emerges as a prominent catalyst for economic growth. It assumes a pivotal role in facilitating the integration of advanced technologies like bitcoin, virtualization, the Internet of Things (IoT), and graphics processors, hence fostering innovation and development. In this scholarly essay, a comprehensive examination of artificial intelligence (AI) and learning methodologies spanning the years 1961 to 2018 is conducted. Through an extensive examination of AI, encompassing various aspects such as causal factors, practical systems, fundamental machine learning, commercial achievements, and current advancements, this investigation serves as a highly valuable resource for both academic scholars and industry experts. There is a substantial number of challenges associated with artificial intelligence (AI). However, it is undeniable that AI has seen significant advancements, emerging as an innovative and transformative tool across all domains and applications. © 2024 IEEE.

关键词： Adversarial machine learning

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CheXVGG: A deep neural network for multi-disease classification of chest-xray images using transfer learning 2

CheXVGG: A deep neural network for multi-disease classificat...

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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Kaur, Navdeep Mittal, Ajay Mehr Chand Mahajan Dav College for Women Department of Computer Science and Applications Chandigarh India Uiet Panjab University Department of Computer Science and Engineering Chandigarh India

ISBN: (纸本)9781665437899

Multi disease classification is a thoroughly investigated research topic in the healthcare field. Various deep neural networks (DNNs) have been proposed for multi-disease classifications and have achieved significant results. Most of the existing DNN solutions use low-resolution images for training, limiting performance. This work introduces a deep neural network that takes high-resolution chest radiograph images as input and classifies these into 14 common chest diseases. The proposed model, CheXVGG, comprises the convolutional, maxpool blocks, which mirror VGG19 architecture and fully connected layers with embedded dropout layers and a sigmoid layer. The network is end-to-end trained using a large public dataset, ChestXray14. We have empirically shown that although it is computationally expensive to use the high-resolution image for training, it improves the network's performance. The speculative study of the experiments performed shows that CheXVGG has achieved significant performance in multi-disease classification. © 2022 IEEE.

关键词： Deep neural networks

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Sustainable Health Care System for Cognitive Breast Cancer Detection using Machine Learning 2

Sustainable Health Care System for Cognitive Breast Cancer D...

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Ranjitha, P. Bhanu, K.N. Jain, Sachin Malapriya, S. Presidency University Computer Science and Engineering Bangalore India Global Institute of Management Science Department of Computer Applications Bangalore India Jss Science and Technology University Information Science and Engineering Mysuru India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

Cancer of the breast is a common cause of death in women. It is the second most origin of cancer deaths in women. For classifying breast tumors using image processing techniques, the researchers had developed image classification systems. Breast cancer disease becomes complicated as the causes remain unspecified. The regular indication of this disease masses and micro calcifications which is appeared on mammograms. They can be used to improve initial diagnosis approaches over time, which is important for women's lives. Mammography is an x-ray of the breasts to execute in the absence of symptoms. X-ray mammography is the major test used for screening and processing is the key to developing breast cancer. The first method comprises determining the cells present with various processes. An abnormal cell growth that serves no purpose is a tumor. In this work machine learning technique-SVM is used to detect the breast cancer and this work also predict the various stages of breast cancer. Proposed model results in 97% accuracy which is better than previous work. © 2022 IEEE.

关键词： Support vector machines

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Blockchain-Internet of things-Machine Learning: Development of Traceable System for Multi Purposes 3

Blockchain-Internet of things-Machine Learning: Development ...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Kowsalya, K. Rani, R. Paritala Jhansi Ritu Bhiyana, Malika Saini, Manisha Patil, Pravin P Srm Institute of Science and Technology Computer Science and Applications India Koneru Lakshmaiah Education Foundation Computer Science and Engineering Department Andhra Pradesh Vaddeswaram India Kurukshetra University Computer Science India Kurukshetra University Computer Science Govt Pg College Ambala Cantt Kurukshetra India Kurukshetra University Computer Science Govt Pg College Ambala Cantt India Graphic Era Deemed to Be University Department of Mechanical Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

The Iot devices (IoT) and cryptocurrencies may be combined to create a platform that promotes improved and sustainable and credibility. The resulting innovations have been used in a number of industries, most notably in the monitoring of farm products. Particularly Ai devices (such as RFID, GIS, Geolocation, and others) have the capacity to streamline the collection of data pertaining to crucial traceable features. To handle, store, and search for information, data is acquired and put into the public ledger. The data security that enters the system may be ensured via a dispersed, randomized, and noninflatable cryptocurrency. Nonetheless, IoT devices could produce aberrant data as they gather *** study evaluates the entire beverage distribution network from sowing to sales, develops the network topology and each operate effectively, and patterns and enforces a computer vision (Fluid ounces) proof - of - stake tea reputable monitoring system, which considers literally the entire record keeping chain of food goods (MBITTS). This article presents a unique approach based on Internet of Things (IoT) technologies, such as RFID sensors, for enhancing the precision of cryptocurrency source data. © 2023 IEEE.

关键词： Blockchain

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Improving Support Vector Machine Performance with Advanced Kernel Methods 7

Improving Support Vector Machine Performance with Advanced K...

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

作者： Mittal, Mudit Al-Jawahry, Hassan M. Varshney, Neeraj Kumar, Sunil Prashanth Michaelson, Jacob Reddy, R. Akhilesh Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Gla University Department of Computer Engineering and Applications Mathura India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Chennai India Mlr Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798350350067

In many different fields, Support Vector Machines (SVMs) have shown to be an effective tool for regression and classification problems. When using support vector machines (SVMs), the kernel function - which specifies the feature space where the data is linearly separable - has a significant impact on the performance of the model. In this research, we look at how to improve SVM performance by creating and using new kernel approaches. We contrast the effectiveness of conventional kernels like the linear, polynomial, and radial basis function (RBF) kernels with new kernel functions designed to capture complicated data structures. Extensive studies on benchmark datasets show that classification accuracy, convergence speed, and generalisability are significantly improved. applications in image recognition, bioinformatics, and financial forecasting are well-suited to the suggested kernels because to their exceptional performance in dealing with high-dimensional and non-linearly separable data. Research and practitioners in the area may benefit greatly from this study's thorough examination of the computational complexity and actual implementation of these sophisticated kernel approaches. It offers useful insights for optimising SVM performance. © 2024 IEEE.

关键词： Support vector regression

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Correction to: Sustainable edge computing with mobile crowd computing: a proof-of-concept with a smart HVAC use case

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The Journal of Supercomputing 2024年第18期80卷 26685-26686页

作者： Pramanik, Pijush Kanti Dutta Pal, Saurabh Mukhopadhyay, Moutan Choudhury, Prasenjit School of Computer Applications and Technology Galgotias University Greater Noida India Department of Computer Science and Engineering Brainware University Kolkata India Department of Computer Science and Engineering Bengal Institute of Technology Kolkata India Department of Computer Science and Engineering National Institute of Technology Durgapur India

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Augmenting Content Retrieval Using NLP in AIML 4

Augmenting Content Retrieval Using NLP in AIML

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

作者： Shrivastava, Kapil Quezada, Angeles Sharma, Karuna Marquez Lobato, Bogart Yail Vashishth, Nitish Gla University Department of Computer Engineering & Applications UP Mathura India Tijuana Baja California México Computer Science & Engineering Sdgi Global University Tecnológico Nacional de México Campus UP Ghaziabad India Computer Science & Engineering Sdgi Global University UP Ghaziabad India Electronics & Communication Engineering Raj Kumar Goel Institute of Technology UP Ghaziabad India

ISBN: (纸本)9798350364729

The paper Augmenting Content Retrieval Using NLP in AIML describes using Natural Language Processing (NLP) with Artificial Intelligence Markup Language (AIML) to enhance content retrieval. Among Markup languages, AIML is most commonly used in chatbots or conversational UI. On the other hand, NLP is a subfield of artificial intelligence that handles reading and understanding human language. Their abstract underscores the importance of improved content extraction for AIML systems that rely on a keyword-based approach, which wouldn't work very well as it fails to capture all elements in human language. Here, wide-scale use of the NLP techniques provides a breakthrough in the analysis. It allows understanding the meaning inherent in natural language and interpreting semanticity through context-sensitive, syntactic rule-based arrangement. The paper proposes a mix of NLP and AIML for better retrieval. Approach: In this technique, we first preprocess the user input using NLP techniques, i.e., POS tagging & Named Entity Recognition, to extract context-specific Keywords/Concepts. Then, these keywords and concepts are mapped with answers in the AIML-based system. The abstract also presents the experimental evaluation of the proposed strategy, which significantly outperforms conventional keyword-based retrieval for content. This proves that applying NLP in AIML systems can even increase the capability to understand and recover user inputs. © 2024 IEEE.

关键词： Knowledge representation

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Deep Reinforcement Learning for Optimizing Route Planning in Urban Traffic

Deep Reinforcement Learning for Optimizing Route Planning in...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Mittal, Mudit Sehgal, Archana Varshney, Neeraj Kumar, Sunil Prashanth Boob, Nandini Shirish Reddy, R. Akhilesh Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India GLA University Department of Computer Engineering and Applications Mathura India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India MLR Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798331512965

Urban traffic networks are efficient route design problems due to the intricacy and dynamic character of contemporary cities. Conventional methods hamper real time adjustment to continually changing traffic conditions, thereby providing suboptimal routes, a greater burden on the roadways, and greater air pollution. This thesis presents a novel deep reinforcement learning (DRL) method to optimize the route planning for urban traffic networks. With this rendering of real time traffic data, we use DRL to make autonomous decisions to reduce travel time and congestion of routes. One advantage of using sophisticated neural networks for feature extraction and policy learning is that, since the model is so adaptable to changing traffic patterns, the model guarantees that. And, in comparison to common routing algorithms, the results of simulations using real world traffic data show that this method is able to greatly enhance route efficiency and overall traffic flow management. The suggested DRL based system is feasible as a smart city program based on intelligent transportation system which can scale to grand metropolitan regions. The research presents that DRL can enhance the commuter run experience, reduce the environmental effect, and alter urban transportation. Future study will also investigate how this system can be better optimized when integrated with traffic infrastructure enabled by the Internet of Things. © 2025 IEEE.

关键词： Deep reinforcement learning

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Sentiment Analysis of Covid19 Vaccines Tweets Using NLP and Machine Learning Classifiers

Sentiment Analysis of Covid19 Vaccines Tweets Using NLP and ...

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2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing, COM-IT-CON 2022

作者： Rawat, Amarjeet Maheshwari, Himani Khanduja, Manisha Kumar, Rajiv Memoria, Minakshi Kumar, Sanjeev Uttaranchal University Department of Computer Applications Dehradun India Uttaranchal University Department of Computer Science and Engineering Dehradun India Himgiri Zee University Department of Computer Science and Information Technology Dehradun India

ISBN: (数字)9781665496025

ISBN: (纸本)9781665496025

Sentiment Analysis (SA) is an approach for detecting subjective information such as thoughts, outlooks, reactions, and emotional state. The majority of previous SA work treats it as a text-classification problem that requires labelled input to train the model. However, obtaining a tagged dataset is difficult. We will have to do it by hand the majority of the time. Another concern is that the absence of sufficient cross-domain portability creates challenging situation to reuse same-labelled data across applications. As a result, we will have to manually classify data for each domain. This research work applies sentiment analysis to evaluate the entire vaccine twitter dataset. The work involves the lexicon analysis using NLP libraries like neattext, textblob and multi class classification using BERT. This word evaluates and compares the results of the machine learning algorithms. © 2022 IEEE.

关键词： Sentiment analysis

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Harnessing Tech Innovations for Diversified Multigenerational Work Force and Work Life Balance

Harnessing Tech Innovations for Diversified Multigenerationa...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Mittal, Mudit Sharma, Vishal Varshney, Neeraj Kumar, Sunil Prashanth Boob, Nandini Shirish Reddy, R. Akhilesh Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India GLA University Department of Computer Engineering and Applications Mathura India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India MLR Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798331512965

This research aims to examine how technologically innovative breakthroughs may help a multigenerational heterogeneous workforce to maintain a good work and life balance. We investigate how technology can be used to strategically raise up the rate of participation and align with the particular needs of different age groups. The research findings show how AI powered personal learning, collaborative digital communication tools and flexible remote working solutions promote a healthy and mature culture in the workplace. The findings explain how key it is to utilize technology in a refined manner such that all workers can thrive and maintain a sustainable balance between their work and home life. © 2025 IEEE.

关键词： Collaborative learning

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