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Predictive Analysis of Telecom Customer Churn Using Machine Learning Techniques 1

International Conference on Internet of Everything and Quantum Information Processing, IEQIP 2023

作者： Baby Lavanya, K. Ismail Ansari, M.D. Gundu, Venkateswarlu Krishna Mohan, G. Mouleswararao, B. Department of Computer Science and Applications Koneru Lakshmaiah Education Foundation AP Guntur India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation AP Guntur India

ISBN: (数字)9783031619298

ISBN: (纸本)9783031619281

Customer churn poses a significant challenge to the telecom industry as it directly impacts revenue and customer retention efforts. To tackle this concern, the present research suggests an all-encompassing machine learning oriented method for forecasting customer attrition within the telecommunications industry. The dataset is employed for training and testing the Machine Learning models, and their effectiveness is assessed through conventional metrics like accuracy, precision, recall, and the area under the curve of the receiver operating characteristic (AUC-ROC). Conducting an analysis of feature importance enables us to pinpoint the foremost factors that impact customer churn. The findings illustrate that the logistic regression model attains accuracy in its predictive capabilities for customer attrition. The approach offers a practical and accurate solution, aiding telecom companies in reducing churn, increasing customer loyalty, and ultimately improving business performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Telecommunication industry

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A Detailed Analysis of Colorectal Polyp Segmentation with U-Network 1

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International Conference on Internet of Everything and Quantum Information Processing, IEQIP 2023

作者： Ramakrishna, Talluri Shiva Asritha, Gamini Rama Sai Lakshman, Nagendla Guru, D.S. Prakash, Kolla Bhanu Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur India Department of Computer Science and Engineering Mysore University Mysore India

ISBN: (数字)9783031619298

ISBN: (纸本)9783031619281

Colonoscopy is the most important technique for detecting colorectal cancer and its precursors. High miss rates continue to be a problem, leaving many abnormalities undiscovered. Systems for computer-aided diagnosis (CAD) have been developed using machine learning techniques to increase the accuracy of colonoscopies. CAD systems can aid in the discovery and characterization of lesions by pointing out areas in the colon that may have gone unnoticed during endoscopic examinations. To analyze colonoscopy pictures, these systems employ several approaches, including feature extraction, image processing, and classification. CAD systems can aid in the discovery and characterization of lesions by pointing out areas in the colon that may have gone unnoticed during endoscopic examinations. To analyze colonoscopy pictures, these systems employ several approaches, including feature extraction, image processing, and classification. Additionally, real-time assistance from CAD systems during colonoscopy examinations can give doctors a second opinion and lower the possibility of missing lesions. Additionally, by tracking and keeping an eye on lesions over time, these systems can aid in the early identification of potential malignant growth. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Endoscopy

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Predicting Diseases of Mango Leaves Using ResNet50 and CNN 5

Predicting Diseases of Mango Leaves Using ResNet50 and CNN

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

作者： Ramya, Chimakurthi Himasree, Talluri Fathimunnisa Bhavyasri, Kummari Shareefunnisa, Syed Vignan's Foundation for Science Technology and Research Department of Computer Science and Engineering India Vignan's Foundation for Science Technology and Research Department of CSE India

ISBN: (纸本)9798331530013

Mango leaf disease presents a significant challenge to mango trees worldwide, with untreated infections leading to severe leaf damage and potential tree mortality. Early detection is crucial to mitigating the effects of these diseases and preventing their spread. Our dataset includes 4,000 images categorized into eight classifications of mango leaf diseases, with 3,200 images for training and 800 for validation. This study employs a Convolutional Neural Network (CNN) Transfer Learning Model, specifically ResNet50, trained with carefully tuned hyperparameters to achieve an accuracy of 97%. This innovative approach significantly reduces the time and effort required for traditional inspections, offering a precise and automated solution for disease detection. By improving diagnosis and supporting sustainable farming practices, this research contributes to the development of scalable solutions for managing mango crops effectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Visual News Ticker Surveillance Approach from Arabic Broadcast Streams

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computers, Materials & Continua 2023年第3期74卷 6177-6193页

作者： Moeen Tayyab Ayyaz Hussain Usama Mir M.Aqeel Iqbal Muhammad Haneef Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan Department of Computer Science Quaid-i-Azam UniversityIslamabad44000Pakistan Department of Computer Science Senior Member IEEEUniversity of WindsorN9B 3P4Canada Department of Software Engineering Foundation University IslamabadIslamabad44000Pakistan Department of Electrical Engineering Foundation University IslamabadIslamabad44000Pakistan

The news ticker is a common feature of many different news networks that display headlines and other *** ticker recognition applications are highly valuable in e-business and news surveillance for media regulatory *** this paper,we focus on the automatic Arabic Ticker Recognition system for the Al-Ekhbariya news *** primary emphasis of this research is on ticker recognition methods and storage *** that end,the research is aimed at character-wise explicit segmentation using a semantic segmentation technique and words identification *** proposed learning architecture considers the grouping of homogeneousshaped *** incorporates linguistic taxonomy in a unified manner to address the imbalance in data distribution which leads to individual ***,experiments with a novel ArabicNews Ticker(Al-ENT)dataset that provides accurate character-level and character components-level labeling to evaluate the effectiveness of the suggested *** proposed method attains 96.5%,outperforming the current state-of-the-art technique by 8.5%.The study reveals that our strategy improves the performance of lowrepresentation correlated character classes.

关键词： Arabic text recognition optical character recognition deep convolutional network SegNet LeNet

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A Comprehensive Multi-modal Approach for Enhanced Product Recommendations Based on Customer Habits

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Journal of The Institution of Engineers (India): Series B 2024年第6期105卷 1537-1545页

作者： Bodapati, Jyostna Devi Veeranjaneyulu, N. Yenduri, Lakshmana Kumar Advanced Computer Science and Engineering Vignan’s Foundation for Science Technology and Research Andhra Pradesh Guntur India Department of IT Vignan’s Foundation for Science Technology and Research Vadlamudi522213 India San Francisco Bay Area United States

In response to the evolving demands and challenges in recommendation systems, a comprehensive and innovative approach is proposed for delivering personalized and accurate product recommendations while mitigating existing limitations. This approach combines cutting-edge methodologies from collaborative filtering, content-based recommendation systems, and state-of-the-art deep learning architectures to enhance the standard of recommendations and improve user contentment. The hybrid approach encompasses various modules, including data preprocessing, feature engineering, deep learning model development, recommendation generation processing, and post-processing. Utilizing the H &M Fashion dataset, a rich collection of product images and metadata, for experimentation, the dataset undergoes preprocessing to ensure data quality, and features are extracted from both text and images using transfer learning approaches. These features are then integrated into a unified representation using fusion techniques. Subsequently, a deep learning model is developed to generate personalized recommendations based on the integrated features. Empirical findings on benchmark H &M dataset demonstrate the superiority of the proposed hybrid approach over traditional content-based and collaborative-based models, with improvements in accuracy, recall, and F1 score. The hybrid model effectively combines different recommendation techniques, offering robustness, adaptability, and enhanced recommendation quality. This research contributes to the advancement of recommendation systems, offering insights and solutions for delivering tailored recommendations in the fashion retail industry. © The Institution of Engineers (India) 2024.

关键词： Recommender systems

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Behavioral insights into compression: a study of Move-to-Front-or-Middle deterministic online algorithm through sequence classification and characterization

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International Journal of Information Technology (Singapore) 2025年第1期17卷 189-203页

作者： Baisakh Mohapatra, Hitesh Guru, Abhishek Department of Computer Science and Engineering Sambalpur University Institute of Information Technology Sambalpur India Computer Science and Engineering GMR Institute of Technology Andhra Pradesh Rajam India School of Computer Engineering KIIT Deemed to Be University Bhubanseswar India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

Since the list update problem was applied to data compression as an effective encoding technique, numerous deterministic algorithms have been studied and analyzed. A powerful strategy, Move-to-Front (MTF), involves moving an accessed item to the front of the list immediately. Theoretical analysis has demonstrated MTF’s potential, showing it to be 2-competitive through competitive analysis. This success has inspired researchers to develop various intriguing MTF variants and assess their performance both theoretically and experimentally. One such variant, Move-to-Front-or-Middle (MFM), has been experimentally shown that MFM performed better than the classic MTF on Calgary and Cantebury Datasets. However, the experimental performance alone does not provide sufficient insight into the internal behavior of the algorithm or explain why its performance surpasses MTF on certain datasets. Therefore, in this paper, we conduct a detailed behavioral study of MFM through sequence classification and characterization to gain deeper understanding of the algorithm and aims to reconcile the disparity between theoretical and experimental outcomes. This paper also by classifying and characterizing MFM’s performance across various request sequences, considering both the presence and absence of locality of reference. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Competitive analysis Deterministic list update algorithm List update problem Sequence classification

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A novel workload forecasting model for cloud computing using ALAA-DBN algorithm

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Multimedia Tools and Applications 2025年第13期84卷 11383-11407页

作者： Karimunnisa, Syed Gopu, Arunkumar Rao, T. Prabhakara Ayyadurai, M. Kumar, ESaravana Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation AP VaddeswaramGuntur-522501 India School of Computer Science and Engineering VIT - AP University Amaravati522237 India Department of CSE Godavari Institute Of Engineering And Technology AP Rajahmundry533296 India Department of ECE SRM Institute of Science and Technology Ramapuram Campus Chennai India Department of CSE The Oxford College of Engineering Bengaluru India

Cloud Computing (CC) generally exhibits varying workload patterns. This autoscaling feature of CC has been extensively managed through predictive cloud resource management approaches. For this reason, a solitary forecasting model is not sufficient to forecast various workload patterns of CC web applications. With the intention of developing a proficient CC model, this paper implements a novel cloud workload forecasting model with the efficiency of DL algorithms like Deep Belief Network (DBN) and Lion Algorithm (LA). Here, the previous workload models are assessed and trained via DBN, and the prediction efficacy is enhanced by optimizing the DBN's hidden neurons through LA. The proposed workload forecast model for the cloud is accomplished by tuning the significant parameters via the Adaptive Lion Algorithm Assisted-DBN (ALAA-DBN)model. Besides, a well-organized resource provisioning pattern is followed to manage the under/over provisioning issues. The proposed CC model also considers providing better Quality of Service (QoS) to the users without violating the Service Level Agreement (SLA). Eventually, the significance of the proposed ALAA-DBN is verified through a comparative analysis with state-of-the-art models. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Quality of service

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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

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Neural Computing and Applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

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Deep Neural Network Model for Improved DDoS Attack Detection in Cloud Environments 5

Deep Neural Network Model for Improved DDoS Attack Detection...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Verma, Rashmi Jailia, Manisha Kumar, Munish Kaliraman, Bhawna Department of Computer Science and Engineering Banasthali Vidyapith Rajasthan Tonk India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Computer Science and Engineering DPG Institute of Technology and Management Gurugram India

ISBN: (纸本)9798350361155

As the prevalence of Distributed Denial of Service (DDoS) attacks continues to escalate, safeguarding cloud environments against these threats becomes paramount. This paper introduces a Deep Neural Network (DNN) model designed to enhance the accuracy and efficiency of DDoS attack detection in cloud environments. Leveraging the inherent capabilities of deep learning, the proposed model exhibits improved performance on the widely recognized NSL-KDD dataset. The research findings demonstrate a substantial increase in accuracy, underscoring the efficacy of the DNN model in fortifying the security posture of cloud infrastructures. The escalating frequency and sophistication of Distributed Denial of Service (DDoS) attacks pose a substantial threat to the security of cloud environments. In response to this pressing concern, this paper introduces a Deep Neural Network (DNN) model engineered to significantly enhance the accuracy and efficiency of DDoS attack detection in cloud infrastructures. By harnessing the inherent capabilities of deep learning, the proposed model represents a breakthrough in fortifying the security posture of cloud systems. This research employs the widely recognized NSL-KDD dataset, a comprehensive resource for Intrusion Detection System (IDS) evaluation, to evaluate the model's performance. The proposed DNN model transcends conventional methods by autonomously learning intricate patterns within network traffic data, adapting to the evolving landscape of DDoS attacks. The literature survey delves into the vulnerabilities associated with DDoS attacks in cloud environments, emphasizing the need for innovative detection mechanisms. Previous research has underscored the effectiveness of deep learning, particularly DNNs, in addressing complex cybersecurity challenges, positioning them as ideal candidates for enhancing threat detection capabilities. © 2024 IEEE.

关键词： Denial-of-service attack

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CNN-Based Detection of DDoS Attacks in Multi-Cloud Environments

CNN-Based Detection of DDoS Attacks in Multi-Cloud Environme...

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

作者： Verma, Rashmi Jailia, Manisha Kumar, Munish Kaliraman, Bhawna Banasthali Vidyapith Department Of Computer Science And Engineering Rajasthan Tonk India Koneru Lakshmaiah Education Foundation Department Of Computer Science And Engineering Vaddeswaram India Dpg Institute Of Technology And Management Department Of Computer Science And Engineering Gurugram India

ISBN: (纸本)9798350366846

With the increasing reliance on cloud computing services, ensuring the security of cloud-based infrastructures has become paramount. This paper proposes a novel approach utilizing Convolutional Neural Networks (CNNs) to detect Distributed Denial of Service (DDoS) attacks in a multi-cloud environment. The utilization of CNNs, known for their proficiency in feature extraction from structured and unstructured data, offers an innovative solution to the dynamic and evolving nature of DDoS attacks. The datasets utilized in this study include NSL-KDD. As the global frequency of cyberattacks rises, the digital landscape faces significant threats impacting both individual online presence and corporate entities. This paper employs deep learning techniques to enhance security against DDoS attacks, utilizing the inherent ability of deep learning to extract intricate patterns from vast datasets. This makes it a potent tool for constructing effective detection and mitigation systems for the DDoS threat. The research presents a comprehensive approach for detecting DDoS attacks by leveraging CNNs (Convolutional Neural Networks) and advanced data preprocessing methods, focusing on the widely recognized NSL-KDD dataset. The research findings reveal that the proposed CNN-based approach consistently outperforms, achieving an impressive accuracy score of 97.46%. These results underscore the promising potential of the proposed methodology in significantly improving the accuracy and effectiveness of intrusion detection systems. In a parallel exploration, another research paper introduces an alternative approach by offering a CNN model on the same dataset, achieving an accuracy outcome of 96.61%. While this alternative approach demonstrates commendable performance, the findings highlight the superior accuracy achieved by the proposed CNN methodology in the context of DDoS attack detection. © 2024 IEEE.

关键词： Denial-of-service attack

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