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7th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2025

作者： Raynukaazhakarsamy Pradhan, Rahul Jaff, Nadhema Ahmed Gopal, G. Venu Ali, Mariam Fadel Nagaraj, P. Department of Computer Applications Nehru Arts and Science College (Autonomous) Coimbatore India Department of Computer Engineering & Applications GLA University Mathura India Department of Business Administration College of Administrative and Financial Sciences Knowledge University Erbil Iraq Computer Science and Engineering (AI & ML) Geethanjali College of Engineering and Technology Telangana India Department of Radiology Techniques Uruk University Baghdad Iraq Department of AI & ML K.Ramakrishnan College of Engineering Tamil nadu Trichy India

ISBN: (纸本)9798331515683

In the financial sector, customer churn is a significant problem that influences the organization's revenue growth. Churn means that customers have left their current providers which is a negative factor for the providers or financial institutions;therefore, accurate churn prediction helps institutions to increase customer loyalty and satisfaction by improving the quality of service. In this paper, a new method of using Autoencoder and LSTM network for predicting financial churn patterns is introduced. The Autoencoder is used to reduce dimensions and for unsupervised learning acts as a pass through that allows the model to learn the latent patterns in customer behaviour and transactions. Since the LSTM has demonstrated its efficacy in analyzing temporal dependencies of a sequence in time series, it is used to predict churn by analyzing sequential customer activity. The ensemble approach combines the merits of the two models improving the predictive power and especially the stability in a situation of data scarcity and high noise levels. Experimental comparison with conventional predictive models such as decision trees and logistic regression reveal that the Autoencoder-LSTM is more accurate and sensitive than traditional models by comparison. In other words, the proposed model enhances the early identification of potential churners and such institutions can then take appropriate measures of containing it, and in the process, offer relevant retention solutions. The paper describes the benefits of utilizing deep learning models for churn prediction for the financial industry and provides information on how such methods might be used to improve customer retention. Possible future research avenues consist of improving the model interpretability and generalizing the approach to other industries with comparable churn prediction requirements. The accuracy obtained from the proposed method is higher at 99.7% compared to other methods. © 2025 IEEE.

关键词： Autoencoder Ensemble models Financial churn LSTM Predictive analytics

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Evaluation of Subjective Answers Using Machine Learning 3

Evaluation of Subjective Answers Using Machine Learning

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3rd International Conference on Issues and Challenges in Intelligent Computing Techniques, ICICT 2022

作者： Sneha, G. Shreya Shree, G. Babu, Tina Nair, Rekha R. School of Engineering Dayananda Sagar University Department of Computer Science and Engineering Bengaluru India School of Engineering Dayananda Sagar University Department of Computer Applications Bengaluru India

ISBN: (纸本)9781665482684

Negative methods are currently used to evaluate subjective writing. The evaluation of the subjective responses is an essential responsibility. When a human analyses anything, the evaluation's quality can change depending on the person's emotions. All outcomes in machine learning are solely dependent upon the user's input data. To address this issue, our suggested method combines machine learning (ML) and natural language processing (NLP). To analyse the subjective response, our algorithm performs tasks including tokenizing words and phrases, classifying parts of speech, chunking and chinking, lemmatizing words, and word netting. Our suggested approach also offers the context's semantic meaning. There are two modules in our system. Extracting data from scanned photos is the initial step. Then arranging it properly, and the second is using ML and NLP to analyse the text obtained in the previous phase and assigning grades to it. © 2022 IEEE.

关键词： Machine learning

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Client Segmentation and Customization in E-Commerce: applications of Machine Learning from a Management Perspective

Client Segmentation and Customization in E-Commerce: Applica...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Sriprasadh, K. Palit, Shamik Pravallika, B. Manjunatha Lenka, Reena Singla, Atul Srm Institute Of Science And Technology Faculty Of Science And Humanities Department Of Computer Science And Applications Chennai Vadapalani India University Of Stirling Rak Campus Department Of Computing Science And Software Engineering United Arab Emirates Institute Of Aeronautical Engineering Dundigal Hyderabad India New Horizon College Of Engineering Department Of Mechanical Engineering Bangalore India University Pune India Lovely Professional University Phagwara India

ISBN: (纸本)9798350366846

A machine learning-based e-commerce personalised recommendation system helps address the issue of information overload that inevitably arises when consumers have more and more options in e-commerce, leading to an increasingly complex structure. Customer segmentation is a crucial component of contemporary marketing strategy since it enables companies to effectively adjust their advertising campaigns and customise their communications with customers. Through the analysis of enormous quantities of consumer behaviour data and the discovery of trends that can be utilised for dividing customers into segments, ML algorithms provide a potent tool for automating the procedure of customer segmentation. We report our research and results for identifying a customer's segment. In order to perform machine learning, we integrate the Python Tensorflow library with Pandas for manipulating data frames. To address any discrepancies or gaps in the collected client data, it is refined. Key features are found, and to find hidden patterns and relationships, an exploratory analysis of the data is done. After that, we decide on a suitable machine learning technique to divide the clientele into various groups. We also experiment with several clustering models, including conventional machine learning techniques. © 2024 IEEE.

关键词： Python

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Joint aspect-opinion extraction and sentiment orientation detection in university reviews

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International Journal of Information Technology (Singapore) 2022年第6期14卷 3213-3225页

作者： M, Devi Sri Nandhini Gurunathan, Pradeep Department of Computer Science and Engineering A.V.C. College of Engineering Mannampandal Tamilnadu Mayiladuthurai India Department of Computer Applications A.V.C. College of Engineering Mannampandal Tamilnadu Mayiladuthurai India

With the exponential growth of the Internet and the various forms of social media, the number of people using such platforms to share their views and experiences is also greatly increased. The reviews posted by people regarding a service or entity serve as valuable sources for decision making. However, acquiring valuable insights from the abundantly available unstructured information is not so straightforward. There is a need for more effective methodologies to carry out aspect-based sentiment analysis in a fine-grained manner. The proposed work performs joint aspect-opinion extraction and sentiment orientation detection (JAESOD) using an integrated approach of complex dependency rule-based extraction and proper treatment of various forms of adjectives that contribute to the effective extraction of subjective sentiment-bearing terms. The objective of the proposed work is three-fold: to extract aspect-sentiment word pairs, to detect implicit aspects, and to perform sentiment orientation detection at the aspect level along with sentiment scoring. In this paper, we propose a novel methodology to classify the sentiment orientation on a fine-tuned nine-point scale in contrast to the existing three to five-point scale found in the literature works. Concerning the task of sentiment detection, the goal of the proposed work is to achieve optimality in sentiment classification by giving due weightage to the modifies or intensifiers based on their degree of intensification and fair treatment of crucial factors such as double intensification, extended forms of adjectives, negations, sentiments expressed in numerical forms that play a vital role in fine-tuning the sentiment orientation detection. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer applications and Management.

关键词： Appositive adjectives Aspect-based sentiment analysis Aspect-sentiment word extraction Degree of intensification Implicit aspect extraction Nonary scale of sentiment strength Sentiment orientation detection

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A New Hybrid Model for Brain Tumor Recognition in MRI Images Based on Hand-Crafted Features and Deep Learning 27

A New Hybrid Model for Brain Tumor Recognition in MRI Images...

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27th International Conference on Digital Signal Processing and its applications, DSPA 2025

作者： Nizamli, Yasser Fadel, Weaam Filatov, Anton Shichkina, Yulia Department of Software Engineering and Computer Applications Saint Petersburg Electrotechnical University Saint Petersburg Russian Federation Department of Computer Science Saint Petersburg Electrotechnical University Saint Petersburg Russian Federation

ISBN: (纸本)9798331518752

Brain tumors are among the most life-threatening cancers that can affect people of any gender or age. MRI scans manually evaluated by an expert are the initial diagnostic procedure that precedes an appropriate treatment plan. However, the human factor involves the possibility of error, and the process itself can be time-consuming and resource-consuming, ultimately putting patients’ lives at risk. Therefore, efforts are being made to automate the diagnostic process through computer-aided systems, of which artificial intelligence has become the basis. This paper presents a novel system for detecting brain abnormalities in MRI images that ensures high precision without compromising the simplicity of its operational structure. In the proposed model, numerical features are extracted from grayscale MRI images using the rectangular histogram of oriented gradients method. The resulting features are fed after dimensionality reduction to a specialized sequential deep network composed of 11 learnable layers. The classification accuracy of the model reached 99.51%, outperforming many analogues, making it a promising option for supporting the diagnostic decision-making process. © 2025 IEEE.

关键词： brain tumor deep learning MRI R-HOG

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Enhanced Image Concealment: Leveraging LSB and Brotli Compression 3

Enhanced Image Concealment: Leveraging LSB and Brotli Compre...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Yacoob, Shaik Sabnis, Vikrant Jain, Jay Kumar Mansarovar Global University MP Bhopal India Mansarovar Global University Faculty of Engineering and Technology Department of Computer Science MP Bhopal India Maulana Azad National Institute of Technology Department of Mathematics Bioinformatics and Computer Applications MP Bhopal India

ISBN: (纸本)9798350354171

Steganography a cryptographic technique enables the concealment of confidential data within multimedia files like images and videos. This method facilitates the exchange of encrypted information through unconventional means, ensuring that only intended recipients can decipher the hidden message. The LSB technique has attracted significant attention in the literature due to its capability to conceal secret message bits within the least significant parts of image pixels. Although LSB has performed well in image steganography, there is some way to go in terms of its development, especially in the ways in which messages are dealt with. This paper presents a new technique of image steganography basing on the LSB method and the BROTLI algorithm. It uses the compression algorithm to compress message text. After that, to scramble the text, one LSB embedding technique is then used to hide the compressed text in the cover image. Applying the methods of data analysis on the benchmark images, the new approach developed outperforms the standard methods that exist at the present stage. This supports the idea of performing BROTLI on data with a view of compressing the data to be embedded in LSBs. © 2024 IEEE.

关键词： Steganography

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Ad Service Detection - A Comparative Study Using Machine Learning Techniques 13

Ad Service Detection - A Comparative Study Using Machine Lea...

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13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022

作者： Yadhu Krishna, M. Sanjana, S. Thushara, M.G. Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Computer Science and Engineering Amritapuri India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Computer Science and Applications Amritapuri India

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

The use of web-based services has been soaring since the internet has come up. Hence, traditional forms of advertisements have moved on to the web, leading to a rising number of ad services and trackers affecting user privacy. Ad blockers were developed in order to prevent these. However, the currently available ad blockers are purely based on a predefined set of blacklist domains. This study proposes a machine learning-based approach to detect web-based ad services and trackers. In order to help us explore the area using machine learning techniques, a dataset was created. The developed dataset contained 74,000 entries comprising both domains that served advertisements and normal domains. In addition, we have also compared the scores that were obtained by various supervised machine learning models, and the best model was identified. The best model offered up to 88% accuracy in classifying ad services and normal websites. The model contributed by this paper could be further developed into a machine learning-based advertisement blocker system. © 2022 IEEE.

关键词： Websites

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Privacy-Preserving Human Activity Recognition in Smart Homes using Deep Learning and Edge Computing for Real-Time Processing 5

Privacy-Preserving Human Activity Recognition in Smart Homes...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Mariappan, K. Gopal, Baskar Parthasarathy, J. Sreekanth, G.R. Nanmaran, R. Jegajothi, B. SRM Institute of Science and Technology Department of Computer Science and Applications Chennai India College of Engineering University of Technology & Applied Science Nizwa Oman Computer Science & Engineering Jayalakshmi Institute Of Technology Dharmapuri India Information Technology Nandha Engineering College Erode52 India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India SRS Tech Solutions Chennai India

ISBN: (纸本)9798331530013

The rapid advancement of smart home technologies necessitates efficient human activity recognition (HAR) systems while ensuring user privacy. This research presents a novel architecture that integrates deep learning and edge computing for real-time HAR, emphasizing privacy protection. By employing differential privacy methods and federated learning, the approach minimizes personal data exposure risks. Using the UCI HAR dataset, the model achieves an accuracy of 96.50%, with precision, recall, and Fl-score metrics of 95.75%, 96.20%, and 95.97%, respectively. The results indicate high accuracy and adaptability to varying smart home environments, while edge computing ensures minimal latency. This work highlights the importance of balancing performance and privacy in HAR systems, paving the way for secure smart home applications. Additionally, it points to the potential of deep learning in medical diagnostics, suggesting future research could enhance diagnostic capabilities across various medical imaging modalities, including CT scans and MRIs, to improve healthcare outcomes. © 2024 IEEE.

关键词： Smart homes

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Insect Classification Framework based on a Novel Fusion of High-level and Shallow Features

Insect Classification Framework based on a Novel Fusion of H...

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2022 International Conference on Machine Learning and Data engineering, ICMLDE 2022

作者： Haarika, Raye Babu, Tina Nair, Rekha R. Department of Computer Science and Engineering Dayananda Sagar University School of Engineering Bengaluru India Department of Computer Applications Dayananda Sagar University School of Engineering Bengaluru India

The largest and most diversified group of organisms are insects. Since insects play a crucial role in many ecosystems, it must be precisely identified for efficient management. However, it is difficult and labor-intensive to identify insect species. Due to advancements in deep learning, computer vision, and sensor technologies, there is rising interest in image-based systems for quick, accurate identification. This study investigates a novel hybrid feature set of shallow features from the tiger beetle dataset, such as texture and wavelet features and high-level features from SqueezeNet. The tiger beetle insect is classified into the cicindelini and collyridini classes with a random forest classifier with 97.65 percent accuracy using this hybrid feature set, which considers the texture and structural characteristics of the insect. Thus the technique provides insight into various features and indicates promising future directions for image-based insect identification and species classification relevant to computer science, Agriculture, and Ecology research. © 2022 Elsevier B.V.. All rights reserved.

关键词： Ecology

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NodeMCU-based Smart Car Parking System for Real-Time Monitoring and Efficient Space Utilization 2

NodeMCU-based Smart Car Parking System for Real-Time Monitor...

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2nd International Conference on IT Innovations and Knowledge Discovery, ITIKD 2024

作者： Nair, Rekha R Kishore, S. Babu, Tina Alliance School of Advanced Computing Department of Computer Science and Engineering Alliance University Bengaluru India Nitte Institute of Professional Education Department of Computer Applications Nitte (Deemed to Be University Mangalore India

ISBN: (纸本)9798350355468

The rapid increase in vehicle ownership has created a pressing demand for larger parking areas and efficient parking management solutions. This study proposes an innovative NodeMCU-based smart car parking system to address the challenges of efficient parking space management i n urban areas. The system utilizes NodeMCU technology, built on the ESP8266 micro-controller, to provide real-time monitoring and optimize parking space utilization. The architecture incorporates NodeMCU devices equipped with sensors to detect vehicle presence in individual parking spots, wirelessly transmitting data to a central server for processing and analysis. The server implements robust data processing and storage mechanisms, along with a user-friendly interface for easy retrieval of parking information. This approach aids in efficient parking space management and helps reduce illegal parking incidents. By providing real-time data and easy-to-use interfaces, the system helps drivers quickly locate available spots, potentially decreasing traffic congestion and parking-related frustration. The proposed smart parking solution aims to streamline parking management, improve urban mobility, and Improve the entire parking experience for drivers in increasingly crowded metropolitan locations. This research contributes to the development of smart city infrastructure, offering a practical solution to modern parking challenges. © 2025 IEEE.

关键词： accuracy CNN Deep learning efficiency ResNet50 Transfer learning

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