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The Application of Computer algorithms in Marketing Data Analysis and Prediction 2

The Application of Computer Algorithms in Marketing Data Ana...

引用

2nd IEEE international conference on Integrated Circuits and Communication systems, ICICACS 2024

作者： Jiao, Pengfei Guo, Chunxiang Shaanxi Fashion Engineering University School of Economics and Management Shaanxi Xi'an China

ISBN: (纸本)9798350317558

Many enterprises face data quality issues such as missing data, data errors, data inconsistencies, etc. Using data mining algorithms to analyze and predict market data has become an important way for enterprises to improve their competitiveness and market efficiency. This article adopts data mining algorithms, uses large-scale market data for analysis and modeling, and constructs a data set containing multiple characteristic variables by collecting and sorting historical market data. This article chooses data mining algorithms to train and model the data set, and optimizes the performance and accuracy of the model by repeatedly adjusting model parameters and evaluation indicators. Research results show that the algorithm's accuracy test data is between 85% and 94%. By using algorithms for data analysis, companies can more accurately predict market trends, customer behavior and sales performance. This helps companies develop more precise marketing strategies and resource allocation plans, and improve marketing effectiveness and return rates. © 2024 IEEE.

关键词： Computer Algorithm Data Analysis and Prediction Data Mining Marketing Strategy

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Reinforcement Learning in Process Scheduling 13

Reinforcement Learning in Process Scheduling

引用

13th international conference on Cloud Computing, Data Science and engineering, Confluence 2023

作者： Sarkar, Darothi Singal, Swati Institute of Engineering and Management Department of Computer Science and Engineering Kolkata India Amity University Uttar Pradesh Amity School of Engineering and Technology Department of Computer Science and Engineering Noida India

ISBN: (纸本)9781665462631

An efficient scheduling algorithm for a CPU has always been on the lookout by researchers and engineers since the invention of computers. Today most of the tasks are running online facilitated by servers. So, the requirement for fast, efficient, and reliable scheduling algorithms is the need of the hour. However, the traditional heuristic algorithms are having issues like complexity and uncertainty, so it is important to apply innovative technology to improve the task scheduling to ensure the efficient performance of these heavyduty systems. This study aims at developing a new scheduling model that integrates both optimized task scheduling and resource utilization using deep reinforcement learning algorithms. © 2023 IEEE.

关键词： Scheduling algorithms

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High-Performance Computing for Accurate Weather Forecast: Leveraging Advanced algorithms and HPC systems for Improved Predictions 3

High-Performance Computing for Accurate Weather Forecast: Le...

引用

3rd international conference for Advancement in Technology, ICONAT 2024

作者： Mariappan, Umasree Balakrishnan, D. Karunya, T.Blessing Ponraj, Anitha Abhishekh, P. Devi, R. Karishma P.S.R. Engineering College Department of Information Technology Tamil Nadu Sivakasi626140 India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Krishnankoil626126 India Kalasalingam Academy of Research and Education Department of Information Technology Tamil Nadu Krishnankoil626126 India Academy of Research and Education Department of Electronics and Communication Engineering Kalasalingam Tamil Nadu Krishnankoil626126 India

ISBN: (纸本)9798350354171

High-performance computing (HPC) has revolutionized the field of weather forecasting by enabling the use of advanced algorithms and powerful computing systems to predict weather patterns with greater accuracy. Weather sensors and satellites collect vast amounts of data on temperature, pressure, humidity, wind speed, and precipitation, which are then processed and analyzed in near real-time to generate accurate forecasts. This article highlights the role of HPC in improving weather forecasting by leveraging Scalable Probabilistic Approximation (SPA) and HPC systems. Weather modelling is used to improve our understanding of how processes work in land, oceans, and the atmosphere. It also helps predict weather between days and seasons, as well as weather projection for the future. By leveraging these technologies and techniques, HPC is revolutionizing weather pattern prediction, enabling more accurate and timely forecasts that can help protect lives and livelihoods. High-performance computing and advanced algorithms are crucial for improving the accuracy of weather forecasting. By leveraging these technologies, researchers can better predict weather patterns, which has significant implications for various industries and applications. © 2024 IEEE.

关键词： Wind forecasting

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Evaluating Resource Allocation Strategies in Mobile Ad Hoc Networks with Cloud Computing algorithms

Evaluating Resource Allocation Strategies in Mobile Ad Hoc N...

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2024 international conference on E-Mobility, Power Control and Smart systems, ICEMPS 2024

作者： Pillai, Sanjaikanth E. Vadakkethil Somanathan Jindal, Mayank Mimani, Sushant Jiwani, Nasmin Kiruthiga, T. School of Electrical Engineering and Computer Science University of North Dakota Grand Forks United States San Jose CA United States GoDaddy SnohomishWA United States University of The Cumberlands Dept. of Information Technology WilliamsburgKY United States Vetri Vinayaha College of Engineering and Technology Dept. of Ece Trichy India

ISBN: (纸本)9798350394399

Cellular advert Hoc Networks (MANETs) is self-organizing wireless networks that lack the physical infrastructure and centralized manage of conventional networks. With a purpose to enable powerful communiqué, it's miles important to deal with the resource allocation problem in MANETs. Recently, Cloud Computing algorithms were used to evaluate aid allocation techniques in MANETs. Those algorithms contain sampling strategies, which include nearest neighbor, Monte Carlo, and hybrid strategies, to optimize the useful resource utilization levels. This allows for a more correct assessment of the to be had assets in MANETs. Moreover, these algorithms also can be carried out to autonomously control and time table communication flows with a view to improve standard network performance. This paper affords an outline of the existing aid allocation techniques and their evaluation in MANETs the usage of cloud computing algorithms. It also discusses the impacts of the useful resource control selections in MANETs and the challenges confronted in deploying cloud computing algorithms in these settings. Lastly, a fixed of future studies directions is proposed to in addition improve resource allocation techniques in MANETs. © 2024 IEEE.

关键词： Cloud computing

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A Comparative Study on Combined Economic Emission Dispatch Using Machine Learning Techniques 9

A Comparative Study on Combined Economic Emission Dispatch U...

引用

9th international conference on Communication and Electronics systems, ICCES 2024

作者： Nandhakumar, A. Muthukumar, R. Dhaanish Ahmed Institute of Technology Department of Biomedical Engineering Coimbatore India Erode Sengunthar Engineering College Department of Electrical and Electronics Engineering Erode India

ISBN: (纸本)9798350377972

Economic Emission Load Dispatch (EELD), the combination of economic efficiency and environmental sustainability in power system operation, has arisen as a critical challenge in the current era of power generation and distribution. This review paper provides an in-depth review of the application of machine learning methods to tackle the inherent complexity of EELD. It encompasses the latest advancements and notable trends in this sector. The review begins by explaining the essential concepts and goals of EELD, highlighting the importance of balancing operating costs and lowering greenhouse gas emissions. EELD solutions have been built on traditional optimization approaches such as Linear Programming and Genetic algorithms. However, machine learning techniques have recently gained popularity due to their capacity to deal with power systems' complex, non-linear interactions. This paper aims to analyze the strengths and limits of several algorithms in optimizing generation schedules while ensuring that they adhere to emission regulations. Moreover, this paper explores the role of data-driven methodologies in EELD, highlighting the importance of precise data collection and preprocessing. This statement elucidates the incorporation of exogenous variables, such as meteorological predictions and energy consumption trends, into EELD (Energy Efficient Load Dispatch) models, emphasizing their influence on augmenting the efficacy of decision-making procedures. © 2024 IEEE.

关键词： Low emission

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Human Activity Recognition with Smartphones using Machine Learning Algorithm

Human Activity Recognition with Smartphones using Machine Le...

引用

2024 international conference on Knowledge engineering and Communication systems, ICKECS 2024

作者： Ghamya, K. Prema, K. Reddy, D. Abhinay Varaprasad, K. Sai, K. Navyasri Tejesh, S. School of Computing Tirupati India Sree Vidyanikethan Engineering College Department of Cse Tirupati India

ISBN: (纸本)9798350359688

Human Activity Recognition with Smartphones (HAR) leverages the ubiquitous nature of smartphones equipped with sensor technology to identify and classify daily human activities from the generated data. This paper explores the field of HAR, focusing on the use of smartphone sensors like magnetometers, gyroscopes, and accelerometers to capture movement patterns. This provides a comprehensive exploration of HAR, delving into methodologies such as feature extraction data acquisition, and the application of various machine learning algorithms for activity classification. The discussion encompasses challenges and limitations intrinsic to smartphone-based HAR, addressing issues like data variability and device heterogeneity. Promising research directions are highlighted, with an emphasis on context awareness, multi sensor fusion, and deep learning approaches, aiming to enhance the robustness and accuracy of activity recognition systems. The abstract concludes by emphasizing the potential applications of HAR in healthcare, where it could revolutionize patient monitoring, fitness monitoring, optimizing routines, and personalized user experiences, showeasing its role in shaping the future of health-related technologies. This model contributes valuable insights into the dynamic field of HAR, considering the synergy between smartphones, sensor technologies, and machine learning algorithms to propel innovative applications and personalized user experiences. © 2024 IEEE.

关键词： Gyroscopes

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Explore the Impact of Quantum Computing to Enhance Cryptographic Protocols and Network Security Measures 5

Explore the Impact of Quantum Computing to Enhance Cryptogra...

引用

5th IEEE international conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Ambika, S. Balaji, V. Rajasekaran, R.Thalapathi Periyasamy, P.N. Kamal, N. E. G. S. Pillay Engineering College Department of Information Technology Tamil Nadu Nagapattinam India Chennai India Saveetha School of Engineering Simats Dept. of Computer Science and Engineering Chennai India Hindusthan Institute of Technology Department of Computer Science and Engineering India Grt Institute of Engineering and Technology Dept. of Computer Science and Engineering Tamil Nadu Tiruttani India

ISBN: (纸本)9798350383522

The revolutionary effects of quantum computing on security procedures for networks and cryptographic protocols are the focus of this study. Our research shows that conventional cryptographic algorithms are susceptible to quantum assaults;specifically, RSA and ECC encryption are very vulnerable. On the other hand, cryptographic algorithms that are immune to quantum attacks, such as multivariate polynomial cryptography and lattice-based algorithms, show promise in meeting these challenges. Key exchange rates between 200 kbps and 450 kbps and error rates as low as 0.5% are some of the significant achievements shown by Quantum Key Distribution (QKD) implementations. These applications show that QKD is a viable solution for protecting communication channels up to 200 km away. On top of that, there are remarkable outcomes when network security systems incorporate quantum advancements. The detection accuracy of Quantum-Enhanced Intrusion Detection systems (QIDS) is 98.5% and the false positive rate is minimally 0.2%. The detection accuracy of quantum-secured communication systems is 99.2% and the false positive rate is an extremely low 0.1%. While quantum firewalls keep a detection accuracy of 97.8% and a false positive rate of 0.3%, quantum-safe cryptographic modules show a low latency of 3 ms. © 2024 IEEE.

关键词： cryptographic protocol data security quantum key distribution quantum-enhanced intrusion detection

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reconfigurable Intelligent Surface-Aided Semantic Communications: Research Trends and Open Issues 15

Reconfigurable Intelligent Surface-Aided Semantic Communicat...

引用

15th international conference on Information and Communication Technology Convergence, ICTC 2024

作者： Park, Heejae Seol, Kihyun Song, Seungyeop Lee, Yerin Park, Laihyuk Seoul National University of Science and Technology Department of Computer Science and Engineering Seoul01811 Korea Republic of

ISBN: (纸本)9798350364637

Semantic communications represent an advanced approach to information exchange where the focus is on transmitting the meaning (semantics) rather than the raw data itself. reconfigurable Intelligent Surface (RIS) consists of a two-dimensional metasurface made up of energy and cost-efficient passive reflective elements and a control unit for real-time reconfiguration. Adjusting the phase shifts of the RIS can enhance the received signal strength through constructive reflection or reduce user interference through destructive reflection. Integrating RIS into semantic communications presents a promising solution for adapting these systems to dynamic channel environments. This paper aims to analyze the research trends and open issues in RIS-based semantic communication technology. © 2024 IEEE.

关键词： Latent semantic analysis

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Comparison of Cellular Network Coverage Prediction Using Machine Learning algorithms and Conventional Models 18

Comparison of Cellular Network Coverage Prediction Using Mac...

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18th international conference on Telecommunication systems, Services, and Applications, TSSA 2024

作者： Edbert Saragih, Geo Perdana Iskandar Yuliana, Hajiar School of Electrical Engineering and Informatics Bandung Institue of Technology Telecommunicaation Engineering Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institue of Technology Bandung Indonesia Universitas Jenderal Achmad Yani Faculty of Engineering Department of Electrical Engineering Cimahi Indonesia

ISBN: (纸本)9798331527686

Machine learning has emerged as a powerful tool for predicting signal range in wireless communication networks, offering significant improvements over conventional models. Traditional methods, such as empirical path loss models, rely on predetermined formulas and parameters that may not fully capture the complex variability of real-world environments. In contrast, machine learning algorithms leverage large datasets to learn intricate patterns and dependencies within the data, resulting in more accurate and robust predictions. Moreover, machine learning demonstrates better adaptability compared to traditional methods because it does not depend on fixed formulas, allowing it to adjust more effectively to diverse and changing conditions, especially in complex urban environments where signal propagation is affected by numerous variables. This advancement not only improves network planning and optimization but also ensures higher service quality for end-users. Therefore, incorporating machine learning into signal coverage prediction marks substantial progress in creating more efficient and dependable wireless communication networks. © 2024 IEEE.

关键词： Adversarial machine learning

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A Comprehensive Exploration of Data Security Techniques Using Image Steganography 4

A Comprehensive Exploration of Data Security Techniques Usin...

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4th international conference on Sustainable Expert systems, ICSES 2024

作者： Singh, Vishakha Sharma, Manasi Shirode, Anushka Mirchandani, Sanjay VE.S. Institute of Technology Department Of Computer Engineering Mumbai40074 India

ISBN: (纸本)9798331540364

Steganography, the practice of concealing information within digital media, has seen significant advancements through various tools like Steghide, OpenPuff, and OpenStego. However, the effectiveness and robustness of these tools in resisting steganalysis remain critical challenges. This work investigates the application of advanced steganalysis algorithms, including Shannon Entropy, Gaussian Mixture Model, Scale-Invariant Feature Transform, and Spatial Rich Model, using the Python ecosystem. A comparison of these algorithms with pre-existing tools is conducted to highlight the gaps in current methodologies. The proposed approach integrates deep learning techniques such as Simple CNN, Batch Normalization, and ResNetlOl to enhance accuracy and reduce loss in steganography. © 2024 IEEE.

关键词： Convolutional neural networks

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