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A Scalable Machine Learning Model for Sales Forecasting using PySpark

A Scalable Machine Learning Model for Sales Forecasting usin...

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

作者： Harshitha, Talluri Tanya, Simhadri Jaswanthi, Thadakaluru Venugopalan, Manju Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359688

Sales forecasting is a useful tool used for estimating future demand and sales. It provides insightful information for strategic decision-making, resource allocation, and inventory control. This research aims to utilize various machine learning algorithms for constructing a robust sales forecasting model, with the help of the Machine Learning library in PySpark. PySpark is chosen for its scalability, parallel processing framework, and seamless integration with machine learning libraries, making it an ideal choice for handling large datasets. MLlib is a powerful machine learning library within PySpark, offering a comprehensive set of scalable and distributed algorithms that enable efficient processing and analysis of large datasets. The research also incorporates time-series analysis. The performance of the research is evaluated using evaluation metrics R-Square(R2) value Root Mean Squared Error (RMSE) and Mean Squared Error (MSE), Mean Absolute Error (MAE). Out of all experimented models, Gradient Boosting Regressor stands out as the best-performing model with R-squared value of 0.93, showcasing its effectiveness in accurate sales predictions. © 2024 IEEE.

关键词： Adversarial machine learning

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MediGuide AI using Machine Learning 2

MediGuide AI using Machine Learning

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2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Pooja, R. Elugubanti, Nikitha Mary Shiba, C. Kalai priya, V. Sasirekha, K. Sabapathy, S Rathana R.M.K Engineering College Computer Science and Business Systems Tiruvallur District Kavaraipettai India R.M.K Engineering College Department Of Computer Science and Business Systems Kavaraipettai India R.M.K College of Engineering and Technology Department Of Computer Science and Engineering Tiruvallur District Puduvoyal India R.M.D Engineering College Department Of Computer Science and Business Systems Kavaraipettai India Dhaanish Ahmed College of Engineering Department Of Computer Science and Engineering Vanchuvanchery Chennai India

ISBN: (纸本)9798331505745

Providing diverse patient groups with their specific medication requirements, using a wide range of drugs, poses meaningful challenges for prescribing. Standardized approaches often lead to large waste, many side effects and suboptimal outcomes. Therefore, we need improved, more personalized healthcare. MediGuide AI's drug recommendation system is powered by machine learning and it offers a thorough solution. Optimized data pre-processing and analysis, leading to the desired outcome, is achieved using advanced machine learning algorithms like Naive Bayes, TF-IDF and the Natural Language Toolkit. Highly personalized prescriptions are generated using thorough patient data, including demographics and meaningful clinical factors to achieve substantially better accuracy. Important enhancements include dual recommendation outputs, which provide many drug recommendations alongside a large number of alternative Ayurvedic treatments presented side by side for effortless comparison. An advanced personalized dosage calculation module precisely determines drug dosages using advanced regression models based on age, weight and metabolism. This system helps patients track and record their medications and it also suggests several nearby Ayurvedic shops based on their location. MediGuide AI employs exceptionally powerful algorithms and its advanced tools deliver remarkably precise and exceptionally user-friendly healthcare solutions. Current systems make use of rule-based methods that cannot learn from new data, whereas MediGuide AI leverages a hybrid Naïve Bayes and PassiveAggressiveClassifier model for higher accuracy and real-time adaptability. It also includes Ayurvedic options to ensure personalized healthcare experience. © 2025 IEEE.

关键词： Drug dosage

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Integrating Federated Learning and Blockchain for Enhanced Financial Portfolio Management 3

Integrating Federated Learning and Blockchain for Enhanced F...

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3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Murugan, V.G. Nanda, Sudhansu Sekhar Sudindra, V.R. Jatty, Gajendra Naidu Kumar, N.Rajesh Chamundeeswari, Vijaya Department of Management Studies Andhra Pradesh Madanapalle India Sri Sri University Odisha Cuttack India Icfai Business School Department of Finance Bangalore India University of Botswana Gaborone Botswana St.Michael College of Engineering and Technology Sivagangai India Saveetha Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798331508456

Financial portfolio management receives secure and efficient processing through the combination of Federated Learning (FL) with blockchain technology. FL protects data privacy through its distributed training capabilities that prevent financial data exposure. Blockchain secures operations and maintains transparency through the creation of unalterable model change logs on its ledger systems. A proposed approach reaches 91.3% model accuracy level and a 1.27 Sharpe Ratio and delivers a 14.3% return on investment which surpasses conventional approaches. The training duration reaches 4.8 hours while security enhancements and increased efficiency become available in the system. The designed framework supports 200 transactions per second at 250 ms response time while maintaining complete security with 0.10 USD transaction fees. It demonstrates the ability of FL and blockchain technology to boost security measures and risk-adjusted performance metrics and computational capability. Future Work will concentrate on blockchain performance optimization while testing practical applications of algorithms and revising algorithms to optimize financial applications. © 2025 IEEE.

关键词： Investments

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An Intelligent Security systems in Cloud Computing using Cryptography Techniques 5

An Intelligent Security Systems in Cloud Computing using Cry...

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5th international conference on Electronics and Sustainable Communication systems, ICESC 2024

作者： Srilakshmi, V. Chetana, V. Lakshmi Dornala, Raghunadha Reddi Vallabaneni, S. Parvathi Ramanjaiah, Ganji School of Computer Science and Engineering Vit -AP University Andhra Pradesh Amaravati522237 India School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amaravati Campus India Walgreens United States Pvp Siddhartha Institute of Technology Department of It Vijayawada India R v Institute of Technology Dept.of Cse Guntur India

ISBN: (纸本)9798350379945

Delivering computing resources over the internet has seen a paradigm shift towards cloud computing. However, there are serious security risks because cloud environments are distributed and shared. Intelligent Security systems (ISS) that use cryptographic techniques are now necessary to overcome these issues, such as data confidentiality, integrity, and authenticity;this study developed an ISS for cloud computing, concentrating on cryptographic techniques. The RSA-OAEP (Optimal Asymmetric Encryption Padding) and X.509 algorithms are combined to create the ISS and further cover a variety of cryptographic methods, emphasizing their functions in protecting cloud-based systems, including encryption, digital signatures, and key management. It also looks at improving security systems' threat detection and response capabilities by integrating artificial intelligence (AI) algorithms like Grubbs' Test. In addition, several issues and potential paths for intelligent security systems in cloud computing are examined, highlighting the necessity of ongoing study and development to remain ahead of developing cyber threats. © 2024 IEEE.

关键词： Cloud security

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Fault Detection in Smart Grids by Hybrid Generative Adversarial Networks with Neuro Fuzzy Algorithm

Fault Detection in Smart Grids by Hybrid Generative Adversar...

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2024 international conference on Intelligent algorithms for Computational Intelligence systems, IACIS 2024

作者： Tejaswini, N.P. Riad Al-Fatlawy, Ramy Malathy, V. Kirthiga, N. Vidya Sagar Mudunuri, E.C. Nitte Meenakshi Institute of Technology Dept. of Ise Bengaluru India The Islamic University Engineering College of Technical Engineering Department of Computers Techniques Najaf Iraq Sr University Department of Ece Warangal India Kalasalingam Academy of Research and Education Department of Cse India Sagi Rama Krishnam Raju College Engineering College Bhimavaram India

ISBN: (纸本)9798350360660

Inrecent years, integration of advanced communication networks and estimation techniques smart grid enables real time analysis and has control to optimize distribution of energy, improve grid resilience. Detecting and classification of faults in electricity networks is important for uninterrupted provision and maintenance of costs at minimum. By implementing data-driven approaches these systems brought many improvements like consumption of energy and quick re-establishment. In this paper, hybrid Generative adversarial networks (GAN) - neuro fuzzy algorithm is implemented for detection of faults in smart grids and done by extraction of features which are extracted from smart meters, training is done by GAN then trained model is further sent for classification which is done by neuro fuzzy algorithm if there is a fault then model sent for identification or location of fault and evaluation of accuracy is done, if there is no fault then again sent to feature extraction process. Proposed hybrid GAN-neuro fuzzy algorithm achieved 95.33 % of precision and 97.23 % of recall. © 2024 IEEE.

关键词： Generative adversarial networks

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Intrusion Detection using Machine learning 15

Intrusion Detection using Machine learning

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15th international conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Manasa, U. Kuzhalivaimozhi, S. Department of Information Science and Engineering The National Institute of Engineering Karnataka Mysore India

ISBN: (纸本)9798331300579

The fast development in the use of computer networks raises concerns about network availability, integrity, and confidentiality. This requires network managers to use various types of intrusion detection systems (IDS) to monitor network traffic for unauthorized and malicious activity. An intrusion is a malicious breach of security policy. As a result, an intrusion detection system monitors network traffic flowing through computer systems to look for malicious actions and recognized dangers, providing alarms when it detects them. Mining approaches can be highly useful in the development of an intrusion detection system. In order to detect intrusions, network traffic might be categorized as normal or anomalous. After examining over twenty-five studies, we chose the top three classification algorithms in our paper: Logistic Regression, Naive Bayes, and SVM. This study compares the top three classification algorithms based on their performance criteria to determine the best suited method available. © Grenze Scientific Society, 2024.

关键词： Network intrusion

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Single particle imaging of biological specimens 6

Single particle imaging of biological specimens

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6th conference on Frontiers in Optical Imaging and Technology: Novel Imaging systems

作者： Cheng, Guanxiao Yu, Jinjie Key Laboratory of Optoelectronic Devices and Systems Ministry of Education and Guangdong Province College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen518060 China

ISBN: (数字)9781510679719

ISBN: (纸本)9781510679702

Single-particle imaging (SPI) at an X-ray free electron laser (XFEL) is demonstrating its potential to support the imaging and structure determination of biological specimens at atomic resolution without the need for crystallization. According to a principle of diffraction before damage, diffraction patterns of specimens with random orientations injected to the focus of ultra-short and extremely bright XFEL can be recorded before the specimens are damaged. For a successful image reconstruction, robust algorithms are needed. So far, fast and robust reconstruction algorithms from noisy and incomplete diffraction patterns have been challenges for XFEL SPI. Here we briefly outline the workflow of SPI and discuss key challenges and corresponding approaches to both phase retrieval and orientations determination. We also give an outlook of the promising algorithms for SPI by means of machine learning or deep learning and believe that the current computational challenges of XFEL SPI can be handled by utilizing techniques of modern data processing and artificial intelligence for imaging at atomic resolution and femtosecond temporal resolution in the future. © 2024 SPIE.

关键词： Diffraction patterns

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Hardware Implementation of Block Ciphers - A Case Study on Encrypted Image Transfer Over Universal Asynchronous Receiver Transmitter

Hardware Implementation of Block Ciphers - A Case Study on E...

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2023 international conference on Self Sustainable Artificial Intelligence systems, ICSSAS 2023

作者： Sharmila, S. Bhuvaneswaran, R.S. Natarajan, Kailash Prathiba, A. Vaithiyanathan, D. Ramanujan Computing Centre College of Engineering Guindy Anna University Chennai India School of Electronics Engineering Center for Nanoelectronics and Vlsi Design Vit Chennai India National Institute of Technology Delhi Department of Electronics and Communication Engineering Delhi India

ISBN: (纸本)9798350300857

Communication protocols such as Universal Asynchronous Receiver/Transmitter (UART), Serial Peripheral Interface (SPI), Inter-Integrated Circuits (I2C), and Computer Area Networks (CAN) exist to facilitate communication among devices. This work studies a comparison of the additional overhead of offering Advanced Encryption Standard (AES)-128 and PRESENT-64 algorithms with reconfigurable UART on the transmitter and receiver side to offer data encryption. Advanced Encryption Standard (AES) in the basic loop architecture and PRESENT in the fully unrolled architecture for their implementation in the proposed study of secured data transfer over the UART channel. AES-128 consumes almost three times more clock cycles to encrypt and decrypt than PRESENT-64. Considering that AES-128 takes only double the input size of PRESENT-64 and requires thrice the number of clock cycles, AES-128 is more time-consuming. Xilinx Field Programmable Gate Array (FPGA) implementation results suggest that PRESENT-64 consumes slightly more onboard resources than AES-128 since the proposed work employs an unrolled PRESENT architecture. The complete architecture is specified using Verilog HDL and implemented in Xilinx families, namely Artix7, Spartan6, Virtex6, and Virtex7 series of FPGAs. © 2023 IEEE.

关键词： Data transfer

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An Efficient Parking Management System for Smart Cities Using GA-RBF Model to Optimize Fuel, Time, and Cost

An Efficient Parking Management System for Smart Cities Usin...

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2024 international conference on Intelligent algorithms for Computational Intelligence systems, IACIS 2024

作者： Dimlo, U. M. Fernandes Swathi, T.A. Lymareva, Ol'Ga Pandey, Vivekanand Saikumar, Janardhan Devi, V. Sreyas Institute of Engineering and Technology Department of Cse Hyderabad India Srinivasa Ramanujan Institute of Technology Department of Civil Engineering Anantapur India Kuban State University Candidate of Economic Sciences Krasnodar Russia Amity University Patna India Audisankara College of Engineering and Technology Department of Ece Gudur India Hindusthan College of Engineering and Technology Department of Aiml Coimbatore India

ISBN: (纸本)9798350360660

Making better use of parking spots in big parking lots and showing drivers where there are openings makes parking easier for them, which in turn alleviates traffic congestion. With the help of cutting-edge sensing and monitoring technology, this is the desired outcome of smart parking management. Aiming to make parking more effective and less burdensome on depleting resources, the concept of smart parking construction evolved. To achieve the best possible results during model training, feature selection, and preprocessing, it is essential to adhere strictly to this strategy. The steps of data transformation, encoding, imputation, and normalization make up data preparation. After successfully visualizing high-dimensional data in the actual world, the proposed approach are utilizing dimension reduction in feature selection. The model was trained using a GA-RBF. Traditional methods, such as GA and RBF, are surpassed by the suggested approach. Applying the strategy resulted in a 92.27% improvement in accuracy. © 2024 IEEE.

关键词： Traffic congestion

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Multi-cell VLC System and reconfigurable Intelligent Surfaces: Connecting the dots 3

Multi-cell VLC System and Reconfigurable Intelligent Surface...

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3rd IEEE international conference on Control, Instrumentation, Energy and Communication, CIEC 2024

作者： Sabui, Deblina Chatterjee, Sourish Khan, Gufran S. SeNSE New Delhi India Jadavpur University Electrical Engineering Department Kolkata India

ISBN: (纸本)9798350313703

The several advantages of visible light communication (VLC) have made it a potentially competent candidate for future communication technology. The reliability of the VLC system depends on the line-of-sight (LOS) links. However, surrounding environments pose unique challenges, frequently causing LOS blockages. The reconfigurable intelligent surfaces (RIS) also known as intelligent reflecting surfaces (IRS) is a collection of tunable passive elements that have proven their ability in RF communication to manipulate and control the transmitted/reflected RF signals by changing the orientation or phases of RIS. RIS is anticipated to play a crucial role in enhancing VLC by shadowing compensation, extending coverage by controlling the beam directions and improving communication reliability by mitigating interferences. This work delves into the emerging field of reconfigurable intelligent surfaces (RIS) and their transformative role in multi-cell indoor visible light communication (VLC) systems. A RIS-based indoor multi-cell system model is developed for the purpose of mitigating inter-channel interference (ICI) issues during LOS blockages. The SINR performance of the proposed system is estimated for four different scenarios. Results show that the integration of RIS in between VLC channels can enhance the detector's received power up to 27 times compared to the power received from the LOS and NLOS channels. Moreover, incorporating RIS has raised the average SINR value by 25% during no blockage and more than 70.4% during the blockage. © 2024 IEEE.

关键词： Cells

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