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A Comprehensive Study of Case-Based Reasoning in Information Retrieval: An Artificial Intelligence Approach

A Comprehensive Study of Case-Based Reasoning in Information...

引用

Pervasive Computational Technologies (ICPCT), International Conference on

作者： Surabhi Solanki Seema Verma Prolay Biswas Pulakesh Roy Aparna Singh Surbhi Sharma Computer Science & Engineering Banasthali Vidyapith Tonk Rajasthan SCSET Bennett University Greater Noida India Department of Electrical & Electronics Engineering Education National Institute of Technical Teachers' Training and Research Bhopal India Computer Science & Engineering Dayananda Sagar University Ramanagara India Computer Science& Engg Brainware University Kolkata India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Information Retrieval (IR) systems which provide accurate and contextually relevant information to healthcare and law etc. where accuracy is very crucial. Traditional information retrieval methods fundamentally based on keyword matching and statistical models usually difficult to achieve the semantic meaning and contextual nuances of queries. Case-Based Reasoning (CBR), as an artificial intelligence technique, provide advance solutions to enhance IR systems based on a set of past cases through adaptive context-supporting retrieval. By taking advantage of past cases data, CBR allows IR systems to interpret complex context-sensitive queries and offer personalized results. The paper provides an overview of traditional IR models, examines the CBR integration into these systems, and evaluates the impact of CBR on retrieval effectiveness and personalization. And finally, we elaborate on the challenges involved with scaling and adapting cases towards real-time retrieval and provide some future directions for research, including integrating machine learning techniques and hybrid models to enhance the relevance, interpretability, and scalability of CBR-based IR systems.

关键词： Adaptation models Accuracy Scalability Semantics Medical services Machine learning Information retrieval Cognition Real-time systems Context modeling

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Comparative Analysis of Machine Learning Algorithms on Credit Card Fraud Detection

Comparative Analysis of Machine Learning Algorithms on Credi...

引用

Cognitive Computing in engineering, Communications, sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Dev Sharma Esha Kaur Nikunj Sharma Daksh Rawat Satya Prakash Yadav Manika Manwal Computer Science and Engineering Graphic Era Hill University Dehradun India School of Computer Science Engineering and Technology (SCSET) Bennett University Greater Noida U.P. India Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India R/S Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

Fraud detection is a crucial area not only for credit cards but also for industries handling financial transactions where fraud detection is crucial. Machine Learning (ML) had come up powerfully with a variety of tools that can process large amounts of data and can find complex hidden patterns from the data. This abstract provides the usage of ML Algorithms in Fraud Detection with different types of techniques used. In this paper a comparison has been made between different algorithms based on their Accuracy, Precision, Recall, and F1-score. Algorithms Used are SVM, KNN, Decision Tree, Random Forest, Logistic Regression. Dataset was collected from Kaggle and it contains 284806 rows and a total of 31 columns.

关键词： Support vector machines Logistic regression Machine learning algorithms Accuracy Computational modeling Biological system modeling Nearest neighbor methods Credit cards Fraud Decision trees

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Currency Detection for Visually Impaired People using Edge Devices

Currency Detection for Visually Impaired People using Edge D...

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Modeling, Simulation & Intelligent Computing (MoSICom), International Conference on

作者： Ujjwal Kadam Arvind Meena Chaudhary Abuzar Ujjwal Gaurav Singhal Divya Srivastava Computer Science and Engineering Netaji Subash University of Technology New Delhi India SCSET Bennett University Greater Noida India

Visual impairment is a global issue that affects a significant number of individuals. In India, there are nearly 4.95 million blind people and 70 million vision-impaired people and many of them have difficulty reading currency notes and struggle with performing their everyday transactions. There is a need to develop a device that can help them read currency notes and perform their everyday transactions smoothly. In this paper, we have a solution for the same. An edge device, embedded in a cane stick, that can detect the currency notes using a CNN model and provide audio output. We have improved upon an existing INR (Indian National Rupees) currency dataset by adding additional images to it. We trained a CNN model that can classify INR currency images and deployed it on a Raspberry Pi. The TensorFlow model that we have deployed boasts better accuracy compared to other existing models and has been trained over the largest existing INR dataset.

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Software Cost Estimation: A Comparative Analysis

Software Cost Estimation: A Comparative Analysis

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International Conference on computer, Electrical & Communication engineering (ICCECE)

作者： Ram Gopal Gupta Ankur Dumka Bireshwar Dass Mazumdar VMSB Uttarakhand Technical University Dehradun India Department of Computer Science and Engineering. Women Institute of Technology Dehradun Uttarakhand India School of Computer Science Engineering and Technology (SCSET) Bennett University Greater Noida Uttar Pradesh India

One of the most crucial aspects of software development is software cost estimation. It entails calculating the amount of time and money needed to finish the software development. Software cost estimation is crucial when a project's lines of code surpass a certain threshold and when the programme is released with too many errors and unmet criteria, which results in the project being abandoned. The calculation of software costs for a project is a critical factor in determining whether or not its development is approved. Numerous approaches for estimating software costs have been implemented. The timely and cost-effective completion of software is ensured by appropriate cost estimation. The conclusion is that there isn't a single strategy that works well in every situation or for every job. Therefore, in order to compare all approaches and determine which one is the best and most likely to produce realistic and accurate estimations, a complete understanding and rigorous analysis are needed. This article discusses the process of determining the most accurate approach for software cost estimation in order to provide higher levels of accuracy.

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Sentiment Analysis: A comparative Analysis of Machine learning models

Sentiment Analysis: A comparative Analysis of Machine learni...

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Innovations in High Speed Communication and Signal Processing (IHCSP), International Conference on

作者： Aryan Panwar Ishitva Singh Abhishek Kumar Jaspreet Kaur Satya Prakash Yadav Aditya Verma Computer Science and Engineering Graphic Era Hill University Dehradun India Computer Science and Engineering Dronacharya Group of Institutions Greater Noida Uttar Pradesh Computer Science and Engineering Chandigarh Engineering College Chandigarh Group of Colleges Mohali India School of Computer Science Engineering and Technology (SCSET) Bennett University Greater Noida U.P. India Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India. R/S Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

Sentiment analysis (SA) is the process of Finding or adding a meaning to a text using natural language processing, a deep examination of data that is maintained online at various platforms such as movie reviews, product reviews at different online shopping apps, twitter (now X), etc., to recognize and classify the opinions and ideas that are expressed in a certain passage of text. The process's aim is to search for the author's perspective on a certain topic, film, item, etc. The outcome can be neutral, bad, or good. There have been studies that demonstrated various methods in the SA methodology for gathering and analysing feelings related to the selected subjects’ polarity—positive, negative, or neutral. Social media SA is a reliable source of data and information. SA becomes significant in a variety of business, social, and academic fields. The evaluation and sentiment analysis process, however, is looked upon with difficulties. Finding the proper side of polarity and accurately interpreting sentiments that are hindered by these difficulties. Sentiment analysis is a part of natural language processing and used text mining to find and evaluate the bias from the text.

关键词： Sentiment analysis Analytical models Logistic regression Accuracy Reviews Social networking (online) Text recognition Machine learning Nearest neighbor methods Vectors

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Paradigm Shift in E-Commerce by Applying Cognitive Multi-Agent System with Machine Learning and Deep Learning Techniques

Paradigm Shift in E-Commerce by Applying Cognitive Multi-Age...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Deepak Ankur Dumka Bireshwar Dass Mazumdar Veer Madho Singh Bhandari Uttarakhand Technical University Dehradun Uttarakhand India Department of Computer Science & Engineering Women Institute of Technology Dehradun Uttarakhand India School of Computer Science Engineering & Technology (SCSET) Bennett University Greater Noida Uttar Pradesh India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The incorporation of artificial intelligence (AI) into the e-commerce realm has transformed business operations and customer interactions. In paper we elaborate the transformative impact of artificial intelligence in online marketing and focus on the key application and benefits. This overview explores the AI landscape within e-commerce, emphasizing crucial elements and progressions within this domain. The AI in e-commerce provide real-time customer support, enhancing the efficiency and responsiveness of service. The AI in e-commerce increase the fraud detection capabilities, secure transaction and also protecting the customer trust, from these advancement, data privacy concern, algorithm bias and integration complexities are remain challenges in e-market *** objective of AI paradigm in e-commerce is to provide transformative shift towards intelligent automation, personalized experiences, data-driven insights, and enhanced customer engagement, driving growth and innovation in the e-commerce industry.

关键词： Deep learning Intelligent automation Technological innovation Real-time systems Electronic commerce Security Artificial intelligence Informatics Business Multi-agent systems

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Deceptive Opinion Detection Using Stacking-Based Deep Ensemble Learning

Deceptive Opinion Detection Using Stacking-Based Deep Ensemb...

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Disruptive Technologies (ICDT), International Conference on

作者： Arvind Mewada Sushil Kumar Maurya Mohd. Aquib Ansari Om Prakash Sharma Suman Avdhesh Yadav Shahnawaz Ahmad SCSET Bennett University Greater Noida India CSED Siksha ‘O’ Anusandhan Bhubaneswar India Computer Science & Engineering SRM University Assam India SCSE IILM University Greater Noida India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

The widespread reliance on online reviews in e-commerce and social platforms has led to an increase in deceptive opinions, commonly called fake reviews or opinion spam. These reviews manipulate consumer perceptions, impacting purchasing decisions, brand credibility, and platform trustworthiness. Detecting fake reviews is challenging due to their human-like writing style, evolving deceptive tactics, and the scarcity of well-annotated datasets. Traditional machine learning models, such as Support Vector Machines (SVM) and Naive Bayes, struggle to achieve high accuracy. In contrast, deep learning models like CNN and LSTM perform better but still face limitations in capturing diverse deceptive patterns. This study proposes a stacking-based deep ensemble learning model that integrates Simple Flatten, CNN, LSTM, and BiLSTM as base classifiers with a logistic regression meta-learner for improved classification performance. Evaluated on the Amazon fake review dataset, the proposed model achieves an accuracy of 90.04%, outperforming individual deep learning models such as BiLSTM (87.13.00%), LSTM (86.10%), CNN (83.11%) and MLP (80.00 % ). This will provide a more reliable and scalable solution for online platforms to detect deceptive opinions.

关键词： Support vector machines Deep learning Logistic regression Accuracy Reviews Bidirectional long short term memory Transformers Ensemble learning Reliability Fake news

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A Robust Security Method for Medical Image Based on 2D Schaffer Map

A Robust Security Method for Medical Image Based on 2D Schaf...

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Electrical, computer and Energy Technologies (ICECET), International Conference on

作者： Suvita Rani Sharma Manpreet Kaur Birmohan Singh Sachin Minocha Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Longowal Sangrur Punjab India SCSET Bennett University Greater Noida India Department of Electrical and Instrumentation Engineering Sant Longowal Institute of Engineering and Technology Longowal Sangrur Punjab India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

In this work, a methodology has been propounded for the security of the medical images containing Confusion and Diffusion phases in which a 2D Schaffer map is utilized. The confusion phase rearranges the row and column of medical images using the sorted indexes of the chaotic sequences produced by the Schaffer map. During the diffusion phase, the chaotic sequences are utilized to alter the pixel values of the original image through the XOR operation. This phase applies to both the row and column pixel values of the images, enhancing security. The efficacy of the proposed security method has been assessed through histogram analysis, chi-square, and variance test, revealing uniformity in encrypted image pixel values. The correlation analysis shows results closer to zero and an entropy value nearly equal to the ideal 8. Moreover, the encrypted image exhibits lower Peak Signal-to-Noise Ratio (PSNR) and higher Mean Square Error (MSE) values, indicating structural dissimilarity with the original image. The Unified Averaged Changed Intensity (UACI) value exceeds 33.40% and the Number of Changing Pixel Rate (NPCR) value exceeds 99.60%, indicating resistance against differential attacks, while the Structural Similarity Measure (SSIM) values closer to zero for the encrypted image indicates structural dissimilarity. The results affirm that the propounded methodology is secure from entropy, differential, statistical, and brute force attacks.

关键词： Histograms PSNR Correlation Mean square error methods Entropy Brute force attacks Indexes Biomedical imaging Immune system Videos

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Enhanced Pixel Privacy: Leveraging Deep Learning for Advanced Image Steganography

Enhanced Pixel Privacy: Leveraging Deep Learning for Advance...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Mohammmad Kashif Iman Shakeel Shahnawaz Ahmad Shabana Mehfuz Department of Computer Engineering Jamia Millia Islamia New Delhi India Department of Electrical Engineering Jamia Millia Islamia New Delhi India School of Computer Science and Engineering and Technology (SCSET) Bennett University Greater Noida India

Digital image steganography is the practice of safely, undetectably, and retrievably obfuscating data in images. In our increasingly digital world, data breaches are becoming more common, posing a serious threat to data security, including that of digital images shared or stored online. Various steganographic methods have been proposed to address these concerns, largely involving the concealment of a secret image within a host image. Traditional methods, however, have limitations, including restricted data embedding capacity and lower quality of the reconstructed image. This research study proposes and implements a novel model, the Enhanced Pixel Privacy GAN (EPPGAN), which combines a multi-attention-based Swin Transformer with a Cycle GAN training approach. A detailed comparison of the performance of various CNN architectures in hiding a secret image within a host image is performed. The proposed technique outperforms the other existing techniques in terms of image quality and embedding capability.

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Gaining Perspective into Cloud Computing: A Business-Oriented Approach

Gaining Perspective into Cloud Computing: A Business-Oriente...

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International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)

作者： Javed Ahmad Shahnawaj Ahmad Mohd. Arif Mohd Nazim Department of Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Greater Noida India Department of Computer Science and Engineering School of Computer Science Engineering and Technology (SCSET) Bennett University Greater Noida UP India Department of Computer Applications School of Computing Science and Engineering Greater Noida Uttar Pradesh India Department of CSE Noida Institute of Engineering and Technology Greater Noida Uttar Pradesh India

Computing may be a profoundly examined subject within the technological and financial world, and numerous huge players in the software industry are working for the improvement of computing services. With the help of a literature review, this work has identified that the classification of cloud computing (CC) does not specify how to classify CC based on industry-driven professional articles, as most of this study has been based on academic distributions. This may limit the ability of the research study to provide a consolidated view of the most current advances in this topic. CC has recently captured the attention of scholars, financial professionals, and specialists. Many of these experts have proposed a variety of applications based on various cloud-based techniques, frameworks, and essentials, which have led to different interpretations, specifications, deployment models, and service models. Therefore, a complete characterization and classification of CC are needed to address the above issue. The ultimate purpose of this research is to classify CC in the literature, including the set of security concerns, history, concepts, technologies, and challenges.

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