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Consumer Behaviour Analysis for Customer Segmentation and Purchase Prediction

Consumer Behaviour Analysis for Customer Segmentation and Pu...

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International Conference on Advanced computing and Communication Systems (ICACCS)

作者： Amritha Gopakumar Aathira Shine Amina Ajim Anjali T Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

E-commerce, short for electronic commerce, is the practice of placing orders online or negotiating a sale's terms and price. Although e-commerce is often associated with selling products and services to consumers, business-to-business (B2B) transactions also make use of it. To market their goods, many smaller consumer enterprises rely only on online sales. Nowadays, people are choosing to make purchases online as online transaction mechanisms and shopping platforms develop. Nevertheless, given that both clients and Merchants are unable to interact with clients face-to-face, there is a limited understanding of consumer demands. Due to this, businesses may struggle to gauge nuanced cues such as body language and personal preferences. Limited direct engagement can hinder the ability to address specific customer needs and preferences, potentially resulting in misinterpretation or overlooking crucial details. In turn, businesses may face difficulties in tailoring their products or services to meet the nuanced expectations of their customers, emphasizing the importance of alternative strategies such as detailed customer feedback mechanisms and data analytics to bridge this gap. The online system logs user activity and gathers data on consumer behaviour, allowing for the prediction of consumer purchasing patterns. Therefore, to improve the shopping experience of people, our study aims to use historical sales data to analyze consumer behaviour and do customer segmentation using RFM (Recency, Frequency, and Monetary) based K-means clustering and purchase prediction using various ML models to improve sales, out of which the combination of Logistic Regression and XGBoost performed the best.

关键词： Logistic regression Consumer behavior Data analysis Communication systems Computational modeling Predictive models Data models Electronic commerce Faces Business

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Performance Evaluation of Data Migration Tools: A Comparative Study Based on Data Type, Size and Network Bandwidth 23

Performance Evaluation of Data Migration Tools: A Comparativ...

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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Suchit Anandrao Sapate Ganesh Rajaram Pathak Omprakash Tembhurne Computer Science and Engineering MITADT University India MIT School of Computing MITADT University India

ISBN: (纸本)9798400709418

Modern organizations require the ability to seamlessly transfer data between their various environments and systems. Due to the complexity of data migration, it is important that the tools used to perform this process are evaluated. This paper aims to provide a comprehensive analysis of the performance of the different tools by taking into account network bandwidth, data type, and size. The study evaluates the performance of various data migration tools, such as Amazon Web Services' Data Migration Service (AWS DMS), Azure Data Factory, Talend's Data Integration, and Google Cloud's Transfer Service. The evaluation was performed using the Apache JMeter open-source platform. JMeter is a flexible environment that can be used for testing and evaluation. The goal of the evaluation is to replicate the real-world processes involved in data migration. The various tools' performance is evaluated by testing different scenarios that involve the extraction, transformation and loading of data. Besides data types, other factors such as network bandwidth efficiency are also taken into consideration to see how they perform in different environments. The evaluated tools' key performance metrics, such as the speed of data transfer, scalability, and error rate, are compared and analyzed. The findings of the evaluation help organizations identify the strengths and weaknesses in their data migration tools and which one is ideal for their requirements. The study's findings provide valuable information on the different tools for data migration. The evaluation process, which was carried out through the open-source Apache JMeter platform, can be used to analyze and extend the capabilities of other migration tools. The results of this evaluation can help organizations identify the ideal tool for their specific requirements and improve their performance.

关键词： Apache

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Optimising Energy Efficient Routing protocols in Wireless Sensor Networks - A comparative study

Optimising Energy Efficient Routing protocols in Wireless Se...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： Thotapalli Sri Surya Manideep Gamidi Rohan Sneha Saragadam Apurvanand Sahay Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amrita School of Computing Bengaluru India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

This study presents a comparative overview of optimization techniques that include, among others, Ant Colony Optimization, Firefly Algorithm, Bat Algorithm, Bee Colony Optimization, Q-learning, Dynamic Programming, and Branch and Bound Algorithm. Addressing each domain in detail, explanations will be offered for the methods described: for example, ACO’s foraging behavior, Firefly’s light attraction, Bat’s echolocation, and BCO’s collaborative approach. The Travelling Salesman Problem (TSP) is treated with the DP algorithm and Branch and Bound. Different tests in each algorithm are made, and their efficiency and weaknesses are approximated to guide the selection of a proper approach to be used in future optimizations. This research highlights the strengths and drawbacks of each method, thus enhancing the understanding of how these optimization techniques operate in various fields and contributing to the informed selection of algorithms for specific problems. The results provide practical insights and suggest directions for further improvement and application in real-world scenarios.

关键词： Ant colony optimization Wireless sensor networks Q-learning Heuristic algorithms Approximation algorithms Search problems Routing protocols Dynamic programming Optimization Tuning

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Detection of Autism Spectrum Disorder using Deep Learning Models

Detection of Autism Spectrum Disorder using Deep Learning Mo...

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International Conference on Inventive computing and Informatics (ICICI)

作者： Kotha Chakradhar Kotte Thulasi Tharun Periyavaram Sandesh Kumar Reddy Thangam S Department of Computer Science & Engineering Amrita School Of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350373295

ISBN: (纸本)9798350373301

Autism Spectrum Disorder (ASD) is a serious developmental disorder that involves problems with social interaction and communication, as well as repetitive behavior. Early diagnosis and treatment are key to improving the lives of people with autism. One recent breakthrough in the field involves the use of cutting-edge machine learning algorithms to automatically identify ASD using different kinds of data like behavioral observations. The proposed approach combines various types of deep learning models such as YOLOv8n, Detectron2, VGG16, ResNet50, Xception, Inceptionv3, and MobileNetV2. Using these models’ capabilities, the goal of the research study is to increase the distinction between ASD-related behavior signals and normal development patterns. The effectiveness of our method was demonstrated by test results which showed it was highly accurate.

关键词： Deep learning Autism Machine learning algorithms Accuracy Computational modeling Informatics Residual neural networks

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Yolo-based Deep Learning Techniques for Identifying Floating Bottles in Inland Water: A Comprehensive Analysis

Yolo-based Deep Learning Techniques for Identifying Floating...

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International Conference on computing and Networking Technology (ICCNT)

作者： B Saranya Devi Deepa Gupta Rimjhim Padam Singh Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Floating trash like bottles are one of the main causes of the serious environmental threat posed by the growing pollution of inland water bodies. Conservation efforts are hampered by the lack of effective and affordable techniques for locating and retrieving these bottles. Real-time object detection algorithms in conjunction with unmanned boats can offer ways to remove floating trash from rivers. A popular real-time object detection model is the You Only Look Once (Yolo) Series. Based on efficacy and accuracy rate, each YOLO model gets better than the other. The study presents an extensive analysis of YOLO designs, such as YOLOv5, YOLOv7, YOLOv8, and YOLO-NAS, for bottle identification in inland water. The dataset used is a floating trash in the river, and the performance metrics such as precision, recall and mean average precision are employed, to compare and experimentally validate each algorithm’s detection efficiency and accuracy. The methodology trains each architecture for 200 epochs. Regarding the precision of detection, the experimental results shows that YOLOv8-x model has high value of $85.5 \%$ and $\mathrm{mAP} \text{@}[I o U=0.5: 0.95]$ of 43.5%. YOLO-NAS variations have high recall, they are applicable for minimizing missed detection. YOLO-NAS m has a higher recall value of $90 \%$.

关键词： YOLO Measurement Deep learning Accuracy Boats computer architecture Water conservation Water pollution Real-time systems Rivers

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Revised IEEE 802.11 MAC protocol for an Energy Efficient and Optimized Wireless Communication in Wireless Sensor Networks

Revised IEEE 802.11 MAC protocol for an Energy Efficient and...

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Emerging Technologies in computer science for Interdisciplinary Applications (ICETCS), International Conference on

作者： Sreyas Ramesh B.N. Sukanth Sathyavarapu Sri Jaswanth M Rajesh B Sreevidya Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350372502

ISBN: (纸本)9798350372519

In the world of wireless communication, the Medium Access Control (MAC) protocol sports a pivotal task in managing access to the shared communication medium. This project proposes a change to the existing IEEE 802.11 MAC protocol by introducing a modification in the sensing process. The conventional algorithm incorporates Short Inter-Frame Space (SIFS), DIFS (Distributed Inter-Frame Space) and backoff periods to regulate channel access. The proposed modification seeks to experiment the protocol by eliminating SIFS, thereby focusing solely on backoff and DIFS. This will reduce the delay each node waits before transmitting the packet. Eventually the reduction in wait time, reduces the energy consumed thus optimized the energy usage of nodes. To evaluate the efficacy of the modified protocol, a wireless network simulation is conducted in NS2. The simulation provides insights into the behavior of the network under the altered MAC protocol, shedding light on its performance and potential advantages.

关键词： Wireless sensor networks Protocols Wireless networks Simulation Aerospace electronics Media Access Protocol Delays

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Weapon Classification with Xception: An Efficient Deep Learning Approach

Weapon Classification with Xception: An Efficient Deep Learn...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Yeshwanth A R Bhuvaneswari Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

Intelligent security systems require robust capabilities for weapon detection and classification. This study employs the Xception architecture, leveraging its unique features such as depth-wise separable convolutions and hierarchical feature extraction. A diverse dataset encompassing weapon and non-weapon entities is meticulously annotated and preprocessed. The Xception model is fine-tuned to effectively identify weapons, achieving notable results with an accuracy of 98.3%, precision of 98.50%, recall of 99.49%, and f1-score of 98.99%. Comparative evaluation with YOLOv8, ResNet50, and ResNet18 confirms Xception’s superiority in recognizing subtle weapon patterns. Rigorous evaluation on relevant datasets provides empirical evidence of the model’s effectiveness in real-world scenarios. These findings underscore Xception’s practical viability in enhancing security applications, contributing substantively to the discourse on advanced neural network architectures' role in addressing contemporary security challenges. This research emphasizes the importance of innovative approaches in augmenting security systems' capabilities to ensure public safety.

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Optimizing Head and Neck Tumour Segmentation Using Evolutionary Algorithms and Deep Learning

Optimizing Head and Neck Tumour Segmentation Using Evolution...

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Advancement in Renewable Energy and Intelligent Systems (AREIS), International Conference on

作者： Abin Joseph Dhanya M Dhanalakshmy Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350387230

ISBN: (纸本)9798350387247

The paper explores the use of deep learning models for accurate head and neck tumor segmentation, aiming to improve efficiency and accuracy in medical imaging. The main focus is to employ evolutionary algorithms to optimize these models, minimizing hyper parameters for better results. Traditional manual segmentation is labor-intensive and prone to variability; thus, this study automates segmentation using models like U-Net, V-Net, and SegNet, enhancing patient care through accurate early diagnosis. Key metrics such as Dice Coefficient, Structural Similarity Index, and Intersection over Union are used for performance evaluation, while MRI data from a dataset are preprocessed for model training. The proposed optimized U-Net architecture demonstrates promising results, suggesting that evolutionary algorithms can enhance deep learning models for medical image analysis.

关键词： Deep learning Image segmentation Analytical models Accuracy Head Evolutionary computation Data models Neck Tumors Genetic algorithms

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Multimodal Lung Cancer Image Analysis

Multimodal Lung Cancer Image Analysis

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International Conference on computing and Networking Technology (ICCNT)

作者： Akshita Prasanth Pallithodiyil Atyam Preetham Srikrishna Gutha Venkat Sai D Paripoorna R R Sathiya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This paper addresses the significant global impact of lung cancer, the primary cause of cancer-related deaths worldwide and the second leading cause of mortality following cardiovascular disease. The challenge lies in detecting lung cancer, often diagnosed at an advanced stage, complicating treatment efforts. The proposed model integrates CT (Computed Tomography) and PET (Positron-Emission Tomography) scans to enhance lung cancer prediction. CT scans offer detailed anatomical insights, while PET scans excel in identifying abnormal metabolic activity and precise tumour localization. This combined approach provides a comprehensive assessment, empowering healthcare professionals with improved diagnostic accuracy and better-informed treatment planning for lung cancer patients.

关键词： Support vector machines Accuracy Computed tomography Lung cancer Predictive models Feature extraction Planning Convolutional neural networks Positron emission tomography Tumors

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An Extensive Analysis of ML Techniques for Predicting and Analysing Medical Data

An Extensive Analysis of ML Techniques for Predicting and An...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： Akshay Rajan Milan Manoj Gouri Santhosh Sarath S Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

The study aims to examine the effectiveness of three machine learning algorithms: Random Forest, Support Vector Machine (SVM), and Logistic Regression, navigating through the complex terrain of medical-related datasets. Through an in-depth analysis of three major datasets including demographics, health indicators, and assessments of Parkinson's disease, this study reveals the subtle advantages and disadvantages of each algorithm. Whereas Random Forest has unmatched flexibility in figuring out complex correlations, Logistic Regression stands out as an example of simplicity and openness. SVM struggles with computing loads and parameter complexities at the same time. This thorough investigation avoids claiming a model is supe-rior, allowing practitioners to identify the complex interactions amongst machine learning giants when it comes to healthcare datasets.

关键词： Support vector machines Deep learning Logistic regression Machine learning algorithms Computational modeling Medical services Predictive models Data models Real-time systems Random forests

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