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Customer Segmentation using Machine Learning

Customer Segmentation using Machine Learning

引用

Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Razia Sulthana A Anukriti Jaiswal Supraja P Sairamesh L Department of Computing and Mathematical Sciences University of Greenwich Old Royal Naval College London United Kingdom Electrical and Computer Engineering College of Engineering Carnegie Mellon University Silicon Valley California United States Department of Networking and Communication SRM Institute of Science and Technology Kattankulathur India Department of Information Science and Technology Anna University TamilNadu India

Customer segmentation has seen major growth in all sectors in the last decade. Several techniques have been devised to analyze customer behavior through loyalty, purchases, recency, frequency and monetary to develop efficient marketing strategies that cater to each client individually. As the availability of products and services increases, so does the competition. With the spiraling of automation accompanied by its cost-effectiveness and ease of availability, all businesses equip themselves with the required workforce and machinery to conduct experiments such as customer segmentation on an industrial scale. In the proposed work, the datasets are manipulated by extracting features from existing attributes. A widespread approach is RFM that calculates the Recency, Frequency and Monetary values for each customer tuple. This paper aims at laying out a new approach at every step of customer segmentation from pre-processing, clustering, validation and suggesting marketing strategies for customer retention. Two datasets- Online Retail II Set and Mall Customer Segmentation are modelled and the results from analysis of both the datasets are presented and compared to reach a generalized opinion.

关键词： Measurement Clustering algorithms Machine learning Forestry Feature extraction Indexes Machinery

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Jamming Mitigation in JRC Systems via Deep Reinforcement Learning and Backscatter-supported Intelligent Deception Strategy 6

Jamming Mitigation in JRC Systems via Deep Reinforcement Lea...

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6th IEEE International Conference on computer and Communication Systems, ICCCS 2021

作者： Ismail, Lotfi Niyato, Dusit Sun, Sumei Hoang, Dinh Thai Kim, Dong In Liang, Ying-Chang School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Institute for Infocomm Research Astar Singapore Singapore School of Electrical and Data Engineering University of Technology Sydney SydneyNSW Australia Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of Center for Intelligent Networking and Communications University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9780738126043

In this paper, we develop a framework to optimize the trade-off between radar sensing and data transmission in Joint Radar-Communication (JRC) systems under smart and reactive jamming attacks. First, we propose a novel JRC design architecture that uses backscatter technology and deception strategy to leverage jamming attacks for a JRC system. The deception strategy is used to predict the jammer's action and adopt appropriate counterattack instantaneously, while backscatter technology is used to transmit data on the jamming signals. To deal with the jamming strategy uncertainty (e.g., jamming capability), we then develop a deep reinforcement learning algorithm to quickly find the optimal defense policy for the JRC system. Our in-depth investigation reveals that the proposed design not only significantly undermines the jamming attacks, but also utilises jamming signals to improve the system performances. Compared with conventional anti-jamming methods, our proposed design significantly improves data throughput while maintaining a satisfactory radar sensing performance in dynamic environments. Moreover, the proposed deep reinforcement learning based solution can converge four times faster than a conventional deep Q- Learning based solution. © 2021 IEEE.

关键词： Deep learning

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Multi-Functional RIS for Sum-Rate Maximization in Rate-Splitting Multiple Access Networks

Multi-Functional RIS for Sum-Rate Maximization in Rate-Split...

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IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Wen Wang Wanli Ni Hui Tian Zhaohui Yang Changsheng You Dusit Niyato State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China School of Computer Science and Engineering Nanyang Technological University Singapore

In this paper, we propose a multi-functional reconfigurable intelligent surface (MF-RIS) assisted rate-splitting multiple access (RSMA) scheme to improve the spectral efficiency in the downlink communications. With the ability of simultaneous signal reflection, transmission/refraction, and amplification, the proposed MF-RIS enables full-dimensional communications with enhanced signal power, thereby improving the signal reception of RSMA users in whole space. Under the constraints on the power budget, MF-RIS coefficients, and minimum rate requirement, a non-convex optimization problem is formulated to maximize the sum-rate by jointly designing the rate allocation, transmit beamforming, and MF-RIS configuration. Then, a low-complexity alternating optimization algorithm is proposed to solve this challenging problem. Simulation results show that the proposed MR-RIS-aided RSMA scheme can achieve up to 33.6% and 51.9% higher spectral efficiency than the simultaneous transmitting and reflecting RIS-aided RSMA scheme and the MF-RIS-aided non-orthogonal multiple access scheme, respectively.

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IoT Based Environmental Monitoring System using LSTM-WAO-EEMD

IoT Based Environmental Monitoring System using LSTM-WAO-EEM...

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： Uppara Raghu Babu Neelam Sharma K Senthil Kumar Deshmukh Deepak Madhukar Pavankumar Ravindra Sonawane Neerav Nishant Civil Engineering Department Srinivasa Ramanujan Institute of Technology Ananthapuramu Andhra Pradesh India Department of Physical Education Lovely Professional University Punjab India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai Tamil Nadu India Mechanical Engineering Pravara Rural Engineering Collage Loni Maharashtra India Mechanical Engineering Department JSPM's Rajarshi Shahu College of Engineering Tathawade Pune India Department of Computer Science and Engineering School of Engineering Babu Banarasi Das University Lucknow Uttar Pradesh India

In this proposed approach, the system takes a look at the environmental monitoring systems that are based on the Internet of Things. The suggested system's primary function is to supply remote locations with environmental parameters via the internet. The proposed method is a practical and easy way to monitor the surrounding environment and atmosphere. The system is a representation of ambient and environmental parameter monitoring by use of low-power wireless sensors linked to the Internet that report their readings to a centralized server. Finally, data collected from all over the world may be seen on the base station remotely from any Internet-connected device. From the physical level, with its sensors and communication protocol, to the cyber level, with its data management and storage, this work comprises the full solution, a cyber-physical system. It solves the issue of incompatible systems by providing a clear structure for exchanging information between sensors of varying measuring capabilities. This improves the effectiveness of monitoring systems. Using real-time sensors, the suggested system monitors conditions like moisture, temperature, humidity, rainfall, gas concentration, and seismic notification. An open-source platform known as Thing speak checks in on these variables every 2 minutes. The sensor data is then preprocessed, the features are selected, and the model is trained. EEMD-WOA-LSTM is used in the proposed method. When compared to two other models, including LSTM and GRU, the suggested method fares exceptionally well.

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Securing Private 5G Campus Networks: Abstract Survey on Current Status, Security Threats, and Research Landscape

Securing Private 5G Campus Networks: Abstract Survey on Curr...

引用

6G networking (6GNet), International Conference on

作者： Hubert Djuitcheu Sachinkumar Bavikatti Mallikarjun Mohammad Asif Habibi Nandish P. Kuruvatti Hans D. Schotten Department of Electrical and Computer Engineering Division of Wireless Communications and Radio Navigation Rheinland-Pfälzische Technische Universität (RPTU) Kaiserslautern Germany Intelligent Networking Research Group German Research Center for Artificial Intelligence (DFKI) Kaiserslautern Germany

Private 5G campus networks promise to revolutionise the industrial sector by enabling communication services with high reliability, extremely low latency, high capacity, lower complexity, longer battery-life devices, and high user density according to the well-known use cases URLLC, mMTC, and eMBB. Their installation in complex and mixed environment presents a range of security issues, which have a direct impact on their smooth operation. This work aims to provide a concise overview of the current state of securing 5G campus networks. It discusses the unique security requirements of these networks compared to conventional 5G networks, emphasizing the differences. Additionally, this work outlines specific attacks targeting campus networks and examines the associated security challenges.

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Metaheuristic Optimization for Path Planning in UAV Networks for Long-Distance Inspection Tasks

Metaheuristic Optimization for Path Planning in UAV Networks...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： J. Akshya M. Manochitra R. Indu Poornima T. Jeevitha M. Saravanakumar M. Sundarrajan Mani Deepak Choudhry Department of Computational Intelligence SRM Institute of Science and Technology Chennai Computer Science and Engineering (Cyber Security) Dr. N.G.P. Institute of Technology Coimbatore Department of Information Technology KGiSL Institute of Technology Coimbatore Department of Artificial Intelligence and Data Science Nehru Institute of Engineering and Technology Coimbatore Department of Networking and Communications SRM Institute of Science and Technology Chennai Deportment of Computing Technologies SRM Institute of Science and Technology Chennai

ISBN: (数字)9798350380583

ISBN: (纸本)9798350380590

With broad applicability in surveillance, delivery, and environmental monitoring, multi-drone path planning has received a great deal of attention recently. So far, wide applicability has been explored by various applications of Genetic Algorithms, Particle Swarm Optimization, and Ant Colony Optimization, which have already established reliable solutions to navigation and coverage through drone path optimization. However, in dynamic, obstacle-dense environments, these algorithms fail to adapt in real-time with energy efficiency. Traditional GA and PSO approaches can be effective for generating an initial path but fail to respond to collision avoidance and efficient resource management. In complex and unpredictable conditions, ACO fails to converge rapidly, which leads to suboptimal paths and increased energy consumption. To overcome such barriers, we present here an HGARL for enhanced multi-drone path optimization. The proposed algorithm thus combines the population-based search capability of GA with RL's adaptive learning ability, therefore providing an algorithm that in addition to finding optimal paths adjusts the paths according to changing environmental situations. HGARL presents better performance compared to existing methods. In path convergence, it reaches up to 15%, and in energy consumption, it reduces up to 20% with safe collision-free navigation. The strong potential of HGARL for robust, efficient, and adaptive solutions of real-time multi-drone operation in complex and dynamic environments has set a new benchmark for UAV path planning.

关键词： Energy consumption Navigation Heuristic algorithms Reinforcement learning Path planning Real-time systems Reliability Genetic algorithms Drones Convergence

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A Super-Resolution Technique With An Ensemble Deep Learning Based For Cervical Cancer Detection System

A Super-Resolution Technique With An Ensemble Deep Learning ...

引用

North Karnataka Subsection Flagship International Conference (NKCon), IEEE

作者： K. Murugan B. Gomathy N. Prasath A. Umaamaheshvari D. Malathi Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering Dr.NGP Institute of Technology Coimbatore India Department of Networking and Communications SRM Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Akshaya College of Engineering and Technology Department of Electronics and Communication Engineering Kongu Engineering College Erode India

A novel way for classifying cervical cells using Convolutional Neural Networks (CNN) is presented in this work. Features are learned automatically in this technique. Classification accuracy and model diversity can be enhanced using ensemble of CNN. This is a multi-step process and it requires the training of various CNN models and they must selected accordingly for ensemble. So, for resolving these issues and for creating expert medical decision making systems, Convolutional Neural Network and Random forest with Boosting called CNNRFB is used in current research work. For cervical cancer Prediction, an optimal prediction model can be constructed by medical practitioners using this solution. One dataset with all relevant information about various cervical cancer is created initially. In an image, without information loss, image pre-processing is done using super resolution technique. Self-similarity based Non-Local-Mean (NLM) metric is used as a base for this Super-Resolution (SR) algorithm. The NLM weight is used as a self-similarity metric under noisy environment for computing best self-example accurately. Deep ensemble learning methods are used for performing image classification and cancer prediction. System's accuracy are decided by dataset values and deep learning training technique. Python code is used for executing and simulating all the techniques. The experimental results shows the accuracy, recall, f-measure and precision produced by proposed technique shows better results when compared with the traditional approches.

关键词： Training Deep learning Superresolution Predictive models Prediction algorithms Convolutional neural networks Noise measurement

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Decentralized Data Privacy Protection and Cloud Auditing Security Management

Decentralized Data Privacy Protection and Cloud Auditing Sec...

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

作者： M. Mageshwari R. Naresh Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chengalpattu Chennai Tamilnadu India Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur Chengalpattu Chennai Tamilnadu India

ISBN: (纸本)9781665462013

Decentralized data protection is becoming more important as more firms employ Cloud resources. Securing, protecting, and processing user data are primary issues that Cloud Computing faces. This study’s primary goal is to detect and address attack protection in Cloud. Our objectives are to comprehend the present state of cloud computing security concerns and methods, identify future cloud computing security concerns to offer countermeasures for potential. Preventative measures to employing cloud services include data security and privacy concerns. The challenge is to create cloud platforms for integrating decentralized information systems to ensure the user’s privacy. For management units of distributed systems, the requirements analysis yielded a model for organizational levels of cloud systems. By bypassing the single-point-of-failure of TPA, our suggested approach may prevent service disruption and privacy-preserving concerns. The extensive security analysis proves that our suggested solution meets all the needed security criteria. We also implement our suggested method with PSO-Homomorphic Algorithm (PSO-HA) and evaluate its efficiency in real-world situations, demonstrating its efficacy, high efficiency, and portability. Our results show that our methodology for simultaneously-integrated recovery can be implemented in polynomial time and is thriving.

关键词： Privacy Cloud computing security Security management Data security Computational modeling Data protection Object recognition

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Age and Energy Analysis in Code-Based Status Update System over Fading Channels

Age and Energy Analysis in Code-Based Status Update System o...

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IEEE International Conference on Communications (ICC)

作者： Yajing Deng Shaohua Wu Junhua You Ning Zhang Qinyu Zhang School of Electronics Engineering Harbin Institute of Technology (Shenzhen) Shenzhen China Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Harbin Institute of Technology (Shenzhen) Shenzhen China Peng Cheng Laboratory Shenzhen China Department of Electrical and Computer Engineering University of Windsor Windsor ON Canada

Energy efficiency and information freshness are two fundamentally critical performance metrics in real-time status update systems which can be measured by energy cost (EC) and age of information (AoI), respectively. This paper examines the AoI and EC performance of the hybrid automatic repeat request with incremental redundancy (HARQ-IR) scheme in code-based status update systems and presents unified results that can generally depict the average AoI and EC over block fading channels. First, we propose a practical code-based status update system that fully takes into account the impact of information processing and long-distance transmission in performance analysis. Then, we analyze and derive the average AoI/EC expressions for HARQ-IR scheme, which are unified results over block fading channels. The simulations of different transmission protocols validate our explicit results and show that there is a distance threshold on whether to retransmit the failed updates. Based on the simulation results, it appears that system AoI/EC demand will affect distance threshold values, which provide guidance for future designs of age-energy tradeoff transmission schemes.

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Hybrid Human-Artificial Intelligence Enabled Edge Caching Based on Interest Evolution 14th

Hybrid Human-Artificial Intelligence Enabled Edge Caching Ba...

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14th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2021

作者： Li, Zhidu Li, Fuxiang Wu, Dapeng Wang, Honggang Wang, Ruyan School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Optical Communication and Networks Chongqing China Key Laboratory of Ubiquitous Sensing and Networking Chongqing China Electrical and Computer Engineering Department University of Massachusetts Dartmouth Dartmouth United States

ISBN: (纸本)9783030898137

How to cache appropriable contents for users from huge amount of candidates is a challenge in edge caching network. To address this challenge, this paper studies an edge caching scheme based on user interest, where an interest extraction and evolution network is developed. Specifically, the input features are first classified and embedding. The user interest is then mined and modeled according to the user historical behaviors with the gated recurrent unit network. Thereafter, the user interest evolution process is studied by analyzing the impact of the previous interests on the current interest through an attention mechanism. The group interest model is further studied by merging user interest evolution and social relationships among contents, based on which edge caching scheme is obtained. The effectiveness of the proposed scheme is finally validated by extensive experiments with a real-world dataset. The analysis in this paper sheds new light on edge content caching from user interest evolution perspective. © 2021, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Behavioral research

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