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Enhancing Sales Forecasting in E-Commerce Through Advanced Predictive Analytics and Deep Neural Networks

Enhancing Sales Forecasting in E-Commerce Through Advanced P...

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Automation and Computation (AUTOCOM), International Conference on

作者： Mahesh Kotha Gnanajeyaraman Rajaram K. Sathishkumar Mohammed Hussein Fallah J. Karpagam Anil P S Department of CSE (AI&ML) CMR Technical Campus Hyderabad India Department of CSE SIMATS Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical sciences Chennai India Dept. of Computer Science Erode Arts and Science College Erode Tamilnadu India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore India Department of Management Gnanam School of Business Thanjavur Tamilnadu

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Within this context, this research is aimed at enhancing the sales forecasting in e-commerce using the newer approaches of predictive analytics and deep neural networks. There usually is significant variability of results as compared to traditional approaches that are based on historical data and simple econometric models. This research evaluates how deep learning approaches, LSTM and Transformer models, improve precisely the forecast by noticing the behaviour dynamics and more complex patterns in the business environment. The actual approach includes sales history data, understanding customers, trends, and related promotions to train as well as test neural networks. The basic hybrid approach integrates deep learning and statistical methods to forecast the maximum potential outcomes. The performance of models in this study is evaluated by Mean Absolute Error (MAE), Root Mean Squared Error(RMSE) and R-Squared to compare the forecasted sales values with the actual ones. It is established that incorporating the deep learning models that have been described enhances the forecast precision concerning the baseline and conventional methods of approach significantly, especially the hybrid model. The precise accuracy, and factors that cause it to fluctuate from one product category to another, and the impact of promotional activities on the forecast's precision, are addressed. Thus, the study contributes to the knowledge that adopting innovative techniques such as advanced predictive analytics and deep learning to improve sales forecasting can potentiate strategic business courses of action and the firm's performance in today's cut-throat e-commerce marketplace.

关键词： Deep learning Biological system modeling Predictive models Transformers Market research Data models Electronic commerce Forecasting Predictive analytics Long short term memory

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Machine Learning Approaches to Improve Visibility in Global Supply Chains

Machine Learning Approaches to Improve Visibility in Global ...

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IoT, Communication and Automation Technology (ICICAT), International Conference on

作者： Muntather Muhsin Hassan S. Rao Chintalapudi Kodali Anuradha Himanshu Sharma Theresa Cenate C F Gnanajeyaraman Rajaram Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of CSE(AI&ML) CMR Technical Campus Hyderabad Telangana India Department of CSE GRIET Bachupally Hyderabad Telangana India Department of Computer Engineering & Applications GLA University Mathura Department of EEE New Prince Shri Bhavani College of Engineering and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS Chennai

ISBN: (数字)9798350368109

ISBN: (纸本)9798350368116

Literature review is the main aspect of this research where special attention is given to learning the ways of applying machine learning to enhancing transparency of supply chains all over the world. This is achieved with the help of big data to blend a classical model with several features of the improved ml tool of demand forecasting, anomaly detection, or better route optimization. Data is the whole information collected for analyzing while pre processing means the step through which data is passed before analysis. Model development refers to the process of developing a model which will be followed in practice while implementation refers to the act of practicing what was envisaged in the development of the model. Last but not least, control is the last step in the model implementation process where the outcome of the model is critically determined. The outcome of the research has presented that reliability and effectiveness has been established to achieve an enhanced level of predictability in terms of hit ratio, delivery performance, inventory and overall organizational parameters. It reveals that, if helms properly, then the ml has the capability to increase the effectiveness and visibility of the global supply chain – which is a vital element of any business. . Learning, knowledge creation cannot be a one time process and hence needs continuous application where the stakeholders are the active participants. This study provides informed guidance on the factors that can be applied in integrating ml in supply management.

关键词： Supply chain management Supply chains Demand forecasting Machine learning Predictive models Stakeholders Reliability Optimization Anomaly detection Systematic literature review

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Phishing Websites Classification using Extreme Learning Machine

Phishing Websites Classification using Extreme Learning Mach...

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Communication, computer sciences and engineering (IC3SE), International Conference on

作者： Swathi Gowroju Shilpa Choudhary G. Jyothi B. Sabitha B. Bikram Kumar R. Srilakshmi Department of Computer Science &Engineering (AI& ML) Sreyas Institute of Engineering and Technology Hyderabad India Department of Computer Science &Engineering(AIML) Neil Gogte Institute of Technology Hyderabad India Department of CSE(DS) Sreyas Institute of Engineering and Technology Hyderabad India Department of Computer Science & Engineering CVR College of Engineering Hyderabad India Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of ET(CSE-DS) department CVR College of Engineering Hyderabad India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

Phishing presents a widespread and perilous menace within the domain of cybercrime, functioning as a rapid and uncomplicated means for cybercriminals to get confidential personal data. Phishing attacks specifically target unsuspecting persons with the intention of obtaining sensitive information, such as usernames, passwords, and financial credentials. Phishing websites frequently incorporate indicators inside their textual content and browser cookies. In order to address this issue, the suggested method makes use of the UCI Phishing Websites Data and implements categorization based on Extreme Learning Machine (ELM). In contrast to gradient-based algorithms, the ELM approach employs a solitary hidden layer of neurons that include input weights produced randomly. This design facilitates effective feature learning from data with a high number of dimensions, eliminating the need for iterative changes. The evaluation yielded a remarkable accuracy of 95.34% when compared to other machine learning techniques such as Random Forest Classification and Logistic Regression.

关键词： Representation learning Logistic regression Machine learning algorithms Extreme learning machines Phishing Neurons Passwords

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A Powerful Algorithm for e-Commerce Credit Risk Analysis

A Powerful Algorithm for e-Commerce Credit Risk Analysis

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2023 International Conference on Data science and Network Security, ICDSNS 2023

作者： Dayanand Lal, N. Guruswamy, Sandhya Lalitha, Vuyyuru Awasthy, Mohan Pareek, Piyush Kumar Gitam University Bengaluru Campus Dept. of Cse Gst Bengaluru India School of Computing and Information Technology Reva University India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India Bharati Vidyapeeth Peeth Deemed to Be University Department of Engineering and Technology Navi Mumbai India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350301595

It is critical to build a system and model for analysing the credit risk associated with e-commerce to decrease the chance of credit defaults occurring during online transactions. This approach examines a client's income, assets, negative transaction rate, reputation, and third-party credit evaluation findings to determine creditworthiness. The risk assessment index-based credit risk analysis model is built using the RB-XGBoost approach. The credit risk analysis algorithm's training process employs a data balancing technique to increase the system's capacity to forecast accurately. This method is designed to solve the problem of class imbalance in machine learning datasets. The training set is separated into groups at this point, and each group is compared to a specified threshold. The dataset is then made more representative by combining oversampling and undersampling. The system's capacity to analyse the credit risk presented by online shops is improved by balancing data subsets. Online retailers might utilise the proposed model and indexing approach to better assess their clients' creditworthiness and thereby reduce the number of consumers whose payments are late. By suggesting a unique way to address class imbalance in machine learning datasets, this study adds to the expanding body of work on credit risk assessment in online transactions. © 2023 IEEE.

关键词： Risk assessment

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Neuromorphic Computing Architectures for Enhanced Signal Processing in Autonomous Systems

Neuromorphic Computing Architectures for Enhanced Signal Pro...

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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： R. Arun Chendhuran T. Jayanthi Rampalli Shasidhar Nagendar Yamsani Sankepally Chandrasekhar A. Athiraja Department of EEE Bannari Amman Institute of Technology Erode Department of ECE Dr. N.G.P Institute of Technology Coimbatore Tamilnadu Department of Mechanical Engineering Military College of Electronics and Mechanical Engineering Secunderabad Telangana School of Computer Science and Artificial Intelligence SR University Warangal India Department of Mechanical Engineering Malla Reddy Engineering College (Autonomous) Telangana Department of CSE (AI&ML) Malla Reddy College of Engineering Hyderabad Telangana

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

Innovative computing techniques that strike a balance between speed, efficiency, and adaptability are required due to the growing complexity of signal processing in autonomous systems. Neuromorphic computing, which draws inspiration from the structure of the human brain, offers a workable method for enhancing signal processing in real-time analysis and decision-making devices, including robotic systems, drones, and driverless cars. The integration of neuromorphic architectures for sophisticated signal processing in robotic systems, drones, and autonomous cars is examined in this research. The application of event-driven processing and spiking neural networks (SNNs) for real-time analysis and decision-making is the main emphasis of the research. The approach involves developing a scalable neuromorphic framework that makes use of cutting-edge hardware, including neuromorphic chips, and evaluating its reaction time, energy efficiency, and processing speed in comparison to traditional von Neumann architectures. Neuromorphic systems demonstrate their potential for continuous real-time data handling by reducing latency by 30% and improving energy efficiency by up to 50%. The system's resilience in handling intricate environmental signals and adjusting to changing inputs is highlighted by important findings. According to the paper's conclusion, incorporating neuromorphic designs greatly improves autonomous systems' efficiency and real-time processing capabilities, opening the door to more responsive and energy-efficient operations. Improving hardware compatibility and broadening applications across various autonomous technologies are two areas of future research.

关键词： Autonomous systems Neuromorphic engineering computer architecture Spiking neural networks Signal processing Energy efficiency Real-time systems Hardware Robots Drones

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Implementation of EEG Signal Decomposition and Feature Extraction Through Efficient Wavelet Transforms

Implementation of EEG Signal Decomposition and Feature Extra...

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International Conference on Communication, Computing and Internet of Things (IC3IoT)

作者： P Pavithara C Kalaivanan P Ponmurugan V Latha Jothi K Karthik K Kavin Kumar Department of Electronics and Communication Engineering Kongu Engineering College Perundurai Erode Department of Electrical and Electronics Engineering Sona College of Technology Salem-5 India Meenakshi Academy of Higher Education & Research (Deemed to be Univeristy) Chennai India Department of Computer Science and Engineering (AI&ML) Velalar College of Engineering and Technology Erode India Department of Electrical and Electronics Engineering Kongunadu College of Engineering and Technology Tholurpatti Post Trichy Dt Tamilnadu India

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

In the diagnosis of brain and mental disorders and anomalies, Electroencephalogram (EEG) analysis plays a critical role. There are five bands of EEG signal namely Alpha, Gamma, Beta, Delta and Theta. This research proposes an enhanced method of EEG signal analysis based on FPGA employing Wavelet Packet Transform (WPT) and Discrete Wavelet Transform (DWT) for decomposition followed by feature extraction. Using the decomposed EEG signal, the mean, median, variance, and peak-to-peak values can be computed. The EEG signal consists of five distinct stages: alpha, beta, gamma, theta, and delta waves. Four levels of DWT and three levels of WPT are used to split the signal. The 8 tap transpose FIR filter with daubechies 4 wavelet is employed. In DWT, approximate coefficients from the filters are down sampled to four levels whereas in WPT both the approximate coefficients and detail coefficients are down sampled to three levels. The coefficients of the high pass filter are detailed, but those of the low pass filter are approximations. The proposed decompositions have been implemented using the Basys 3 Field Programmable Gate Array (FPGA). The DWT uses a total of eight filters: four high pass filters and four low pass filters. The WPT algorithm performs the decomposition using a single high pass filter and seven low pass filters, each with a unique initial approach. The various hardware resources utilized for DWT and WPT are estimated. The latency of WPT is less than DWT making it faster at the expense of resources utilized.

关键词： Finite impulse response filters Low-pass filters Feature extraction Wavelet analysis Electroencephalography Wavelet packets Discrete wavelet transforms

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An Analysis of Smart Clinical Trial Investigation Methods in Research Medicine Industries

An Analysis of Smart Clinical Trial Investigation Methods in...

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International Conference on Cognitive Computing and Information Processing

作者： Sonia Maria D'Souza Suchethana H C Harshan KG Nanda Ashwin Veeresh KM Praveen Murthy K S Department of AI&ML NHCE Bangalore Department of Information Science and Engineering JNNCE Shimoga Department of Computer science Engineering JSS Academy of Technical Education Noida Department of Information Science and Engineering East Point College of Engineering Department of Computer Science and Engineering HKBK College of Engineering Bangalore India Department of Information Science and Engineering JSSSTU Mysuru

ISBN: (数字)9781665456487

ISBN: (纸本)9781665456494

Research is being done to see if it is possible to preserve the bodies of patients who have died in the absence of a cure and keep them alive after a cure is found. Researchers say that their bodies can be preserved by freezing and treated and brought back to life when a cure for the disease is found. They conduct this research on human and animal bodies by processing the bodies at minus 1% degree temperature with the help of nitrogen. It is questionable whether this experiment will be successful in the presence of nitrogen gas. However, they hope for success in the future. Studies have found that patients who undergo surgery at night are more likely to die without treatment than patients who undergo surgery during the day. In this paper a smart analysis of research medicine industries are evaluated with the help of clinical trial investigation methods. Clinical research is defined as a quality improvement process that seeks to improve patient care and outcomes by systematically reviewing care against defined criteria and implementing change. In a cut-off point, the proposed model reached 99.56% of cross sectional calculations, 97.52% of population computation, 95.18% of Event controlled modelling, 97.53% of clinical trial reports and 96.34% of confounding factors.

关键词： Industries Computational modeling Sociology Surgery Clinical trials Safety Nitrogen

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Intelligent IoT Driven Ecosystem for Real-Time air and Noise Pollution Detection

Intelligent IoT Driven Ecosystem for Real-Time Air and Noise...

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Cognitive Computing in engineering, Communications, sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Shiv Narain Gupta Mohd Firoz Warsi Nitin Kumar Chauhan Rakhi Bhardwaj Mohd Jawed Khan Ashish Choudhary Department of Electronics and Communication Engineering Greater Noida Institute of Technology (Engineering Institute) Greater Noida Uttar Pradesh India Department of Computer Science & Engineering(AIDS) Greater Noida Institute of Technology (Engineering Institute) Greater Noida Uttar Pradesh India Department of Electronics and Communication Engineering Indore Institute of Science and Technology Indore Madhya Pradesh India Department of Artificial Intelligence and Machine Learning College of Smart Computing CORE University Roorkee Haridwar Uttarakhand India Department Computer Science & Engineering (AI&ML) Greater Noida Institute of Technology (Engineering Institute) Greater Noida Uttar Pradesh India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

The high speed of urbanisation and industrialisation necessitate keeping track of environmental parameters like air quality and noise pollution for the sustainability of ecology as well as the health of the environment and public health. By facilitating real-time data collection, analysis, and management, the Internet of Things (IoT) addresses challenges in an efficient way. The present work introduces an IoT based smart system for the real-time detection of both air and noise pollution. The developed system employs strategically placed sensor nodes to gather data on air quality parameters (e.g., particulate matter (PM2. 5, PM10), carbon monoxide (CO), nitrogen dioxide (NO 2), sulfur dioxide (SO 2), and ozone (O 3), along with sound levels measured in décibels (dB). These nodes have inexpensive sensors and wireless communication modules, sending the data to a central server for data processing.

关键词： Wireless communication Pollution Noise air quality Real-time systems Internet of Things Sulfur Sustainable development Monitoring Wireless fidelity

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Enhancing Patient Care: A Data-Driven Approach Using Decision Tree-ELM Models for Improved Safety and Quality

Enhancing Patient Care: A Data-Driven Approach Using Decisio...

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Advancement in Technology (ICONAT), International Conference for

作者： Supriya Shrikant Telsang Akhya Sanjay Singh Simarpreet Kaur Bramah Hazela Shikha Singh Hemanand Chittapragada Vishwakarma Institute of Technology Bibwewadi Pune Maharashtra India Computer Engineering (AI & ML) Shah & Anchor Kutchhi Engineering College Mumbai India Department of Computer Science and Engineering Amity School of Engineering and Technology Lucknow Amity University Uttar Pradesh India Department of Computer Science & Engineering Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

In recent years, educational initiatives that seek to combine teaching medical students about patient safety and quality improvement (QI) have received funding from both the World Health Organization and the American Association of Medical Colleges. When developing curriculum, it is important to keep in mind the interests and values of the target audience. The proposed used a poll to find out how students felt about lessons on patient safety and quality improvement. It would be more efficient to incorporate patient safety and quality improvement into clinical education rather than teach it in separate computer modules or during pre-clinical lectures. This proposed approach consists of three stages, which are feature selection, model training, and preprocessing. Before doing thorough text processing, stop words are typically filtered out in conventional methods of natural language processing and information retrieval. Unigram and bigram selection are part of feature selection. During the entire model training process, a Decision Tree-ELM Ensemble was utilized. This innovative approach outperforms ELM and Decision Tree with an average accuracy of ${9 4 . 3 6 \%}$.

关键词： Training Accuracy Medical services Feature extraction Information filters Natural language processing Safety Decision trees Systems thinking Text processing

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A Deep Reinforcement Learning-Based Task Offloading Framework for Edge-Cloud Computing

A Deep Reinforcement Learning-Based Task Offloading Framewor...

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

作者： Ponugoti Kalpana Muntather Almusawi Yerrolla Chanti V Sunil Kumar M Varaprasad Rao Department of Computer Science and Engineering AVN Institute of Engineering & Technology Telangana India The Islamic University Najaf Iraq School of Computer Science & Artificial Intelligence SR University Warangal India Department of AI and ML Nitte Meenakshi Institute of Technology Bengaluru India Department of Data Science CVR College of Engineering Hyderabad India

By introducing the processing and resources storage to the network edge, edge computing is a new, promising computing paradigm that drastically decreases network traffic and service latency. Many edge computing applications consist of interdependent tasks, wherein the results of one task are the inputs of another. The important and difficult problem of where to position each running task to optimise Quality-of-Service (QoS) is how to offload these tasks to edge of network. In this work, implemented a novel Deep Reinforcement Learning based Task Offloading (DRLTO) method utilized as the intelligent task offloading that uses a Directed Acyclic Graph (DAG) to represent the dependent tasks and off-policy reinforcement learning powered by a Sequence-to-Sequence (S2S) neural network. This research outcomes show that the DRLTO achieved less cloud processing time, and number of single terminal tasks, and percentage of failed task when compared to Deep reinforcement learning-based cloud-edge collaborative mobile computation offloading (DRL-CCMCO) and DeepEdge.

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