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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Meenakshi Maindola Ramy Riad Al-Fatlawy Rakesh Kumar Dr. Nandini Shirish Boob S P Sreeja N Sirisha Department of Computer Science & Eng. Graphic Era Deemed to be University Dehradun Department of Computers Techniques Eng. College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Eng. & Applications GLA University Mathura Saveetha School of Eng. Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu Master of Computer Application New Horizon College of Engineering Bangalore Department of Computer Science and Eng. MLR Institute of Technology Hyderabad Telangana

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Big data's explosive growth has transformed several businesses by making it possible to glean insightful information from big, complex databases. This study examines new developments in big data and data mining methods, concentrating on data fusion as a means of improving decision-making. Data fusion, the act of combining many data sources to get more accurate and thorough insights, is becoming essential in industries including cybersecurity, healthcare, finance, and marketing. The study looks at how cutting-edge data mining methods, such as anomaly detection, classification, and clustering, might increase the efficiency of data fusion. Important issues are also covered, including managing heterogeneous data, guaranteeing data quality, and scalability. This paper shows how data fusion approaches may enhance decision-making processes by offering more comprehensive perspectives and useful insights via an analysis of real-world applications. The paper's conclusion discusses potential future developments, emphasising how machine learning and artificial intelligence may be used to improve data mining and fusion procedures, potentially leading to even higher levels of precision and effectiveness in decision-making across a range of industries.

关键词： Industries Soft sensors Decision making Data integration Medical services Machine learning Big Data Real-time systems Data mining Anomaly detection

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Research and Innovation In Next Generation Security and Privacy In Industry 5.0 Iot

Research and Innovation In Next Generation Security and Priv...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Geeta S. Navale Ravikiran Madala Manjunath Managuli N. Jayalakshmi G. Kadiravan Ruchira Rawat Department of Computer Engineering Sinhgad Institute of Technology and Science Pune Maharashtra India Network and Data Center Engineer Ph.D Information Technology University of Cumberlands Durham North Carolina United States Department of Electronics and Communication Engineering KLS Gogte Institute of Technology Belegavi Karnataka India Master of Computer Applications PSNA College of Engineering and Technology Dindigul Tamilnadu India Department of Computer Science and Information Technology KL Deemed to be University Andhra Pradsh India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttrakhand India

The objective of the newest manufacturing paradigm, known as “Industry 5.0,” is to combine human intelligence with cutting-edge technology like the Internet of Things (IoT), artificial intelligence (AI), and robots to develop production systems that are incredibly effective and adaptable. But as we rely more on these technologies, we face additional privacy and security issues that might affect how dependable and trustworthy these systems are. In this report, we examine the most recent advancements in security and privacy for Industry 5.0 IoT and pinpoint the newest trends, obstacles, and possibilities. We also cover how these changes may affect manufacturing in the future and provide advice to scholars, practitioners, and policymakers.

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Deep Learning based Handwritten Arithmetic Equation Solver

Deep Learning based Handwritten Arithmetic Equation Solver

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International Conference on Emerging Research in Electronics, computer science and technology (ICERECT)

作者： Asha Gowda Karegowda A Leenarani D. Poornima R Pooja Shreetha Bhatt PT Bharathi Master of Computer Applications Siddaganga Institute of Technology Tumkur India Dept. of Computer Science MS Ramaiah College of Arts Science & Commerce Bangalore India School of CSA Reva University Bangalore India

Manual recognition of different styles of handwritten characters, digits, symbols, and operators is quite intricate. This paper attempts to automate the process of recognition of digits (0 to 9), decimal point, opening and closing parenthesis (‘(’ and ‘)’) brackets, and five binary arithmetic operators: sum (+), difference (-), product (×), exponent (A) and division in input handwritten equation followed by evaluation of expression. Firstly the segmentation based on binary threshold and contouring is applied to segregate the components of input handwritten equation. The individual segregated components are further input to Convolution Neural Networks(CNN) for recognition as part of second step, followed by evaluation of valid expression as the final step. The publicly available MNIST dataset handwritten digits (0–9) are considered and rest of the images for parenthesis, decimal point and five arithmetic operators are manually generated. The CNN model identified the handwritten expression with a high accuracy of 99% followed by a correct evaluation of the valid expression.

关键词： Deep learning computer science Handwriting recognition Convolution Computational modeling Symbols Mathematical models

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Machine Learning for Energy Consumption Prediction in Smart Homes

Machine Learning for Energy Consumption Prediction in Smart ...

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Frontier Technologies and Solutions (ICFTS), International Conference on

作者： Anurag Vijay Agrawal Chandrasekharan Dinesh Nagella Venkata Ramana V. T. Vithya Updesh Pandey Theresa Cenate C F Department of Electronics and Communication Engineering Bhagwant Institute of Technology Muzaffarnagar Uttar Pradesh India Master of Computer Applications Vignan's Institute of Information Technology(Autonomous) Visakhapatnam Andhra Pradesh India Department of Management Studies Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India Department of Computer Science Engineering R.M.K. College of Engineering and Technology Thiruvallur Tamil Nadu India Department of Electrical and Electronics Engineering Mahakal Institute of Technology Ujjain Madhya Pradesh India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331502768

ISBN: (纸本)9798331502775

Predictions of energy forecasting in smart homes is very necessary for the energy management and proper directions of development in future. Nevertheless, standard assumptive frameworks of the energy consumption prediction have the problems of inimitability of long range dependence and highly complicated temporal structures. We propose a new method in this paper that increases prediction accuracy of energy consumption by using TCNs with the help of Attention Mechanisms. The model can pay specific attention to time intervals that impact the computation of energy consumption patterns with the help of attention mechanism that the model introduces, used in TCNs to solve problems that deal with data with long range dependencies. This work utilizes real world smart home energy data to benchmark our approach to how these types of data are usually forecast using standard time series models, through our model forecasts are both better in short term and long term. The proposed model is relatively reliable, adaptive, which can be effectively exploited in terms of energy distribution and savings, cost cut downs and energy efficient measures

关键词： Energy consumption Adaptation models Attention mechanisms Computational modeling LoRa Smart homes Predictive models Data models Forecasting Standards

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Fraud Detection in E-commerce Transactions: A Machine Learning Perspective

Fraud Detection in E-commerce Transactions: A Machine Learni...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Geetha Manoharan S Dada Noor Hayath Ali Dr. Manoj Sathe A Karthik Amandeep Nagpal Ajay Sidana SR University School of Business Warangal Telangana India Master of Computer Applications Ballari Institute of Technology and Management Ballari Karnataka State Computer Science PES Modern Institute of Business Studies Pune India Institute of Aeronautical Engineering Dundigal Hyderabad Lovely Professional University Phagwara Amity International business school Amity University Noida

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

As the number of online purchases increases at an alarming rate, fraudulent activities have surfaced as a major concern for retailers and consumers alike. Fraudsters frequently face setbacks when attempting to bypass automated rule-based systems that are specifically engineered to identify fraudulent activities. The objective of this research is to investigate methods by which fraudulent online transactions can be detected using machine learning algorithms. To detect fraudulent activities, an analysis is performed on various machine learning methodologies, including supervised, unsupervised, and semi-supervised learning. This research primarily examines the evaluation criteria, feature engineering, and model selection that are distinct to the domain of electronic commerce. Furthermore, obstacles that manifest during the detection of fraudulent activities in electronic commerce must be confronted, including the dissemination of deceptive ideas and manipulated data. By subjecting machine learning models to rigorous testing on real-world datasets, we demonstrate their efficacy in detecting fraudulent activities and underscore their capacity to dynamically adjust to evolving fraud patterns. In conclusion, we discuss the practical obstacles and possible avenues for future research concerning machine learning-based e-commerce fraud detection systems.

关键词： Machine learning algorithms Machine learning Semisupervised learning Fraud Electronic commerce Informatics Monitoring

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Security Attacks and Key Challenges in Blockchain technology: A survey

Security Attacks and Key Challenges in Blockchain Technology...

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Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), International Conference on

作者： Krishnaraj Rao N S Raghunadan K R Radhakrishna Dodmane Bhavya K Sardar M N Islam Surendra Shetty Information Science and Engineering NMAM Institute of Technology Nitte Karkala INDIA Computer Science and Engineering NMAM Institute of Technology Nitte Karkala INDIA Department of Mathematics NMAM Institute of Technology Nitte Karkala INDIA ISILC Victoria University Melbourne VIC Australia Master of Computer Applications NMAM Institute of Technology Nitte Karkala INDIA

A blockchain is a decentralized, distributed, and open ledger that is used to record transactions across several computer clusters. Without a change in subsequent blocks, the record cannot be changed. Blockchain is a well-known phrase in the IT industry. There are several issues with Blockchain technology that need to be taken into account, despite the fact that it has many applications in the fields of information technology, banking, and other industries. In this paper, we discussed the major challenges and issues associated with Blockchain technology

关键词： Industries Government Banking Blockchains Consensus protocol Security Information technology

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Use of DNN for the Purpose of NOMA Based Segmentation and Distribution of Signals in Industry Applications

Use of DNN for the Purpose of NOMA Based Segmentation and Di...

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Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI), International Conference on

作者： Archana Verma Jamuna K.V. Ashwini Malviya Master of Computer Applications Noida Institute of Engineering & Technology Greater Noida Uttar Pradesh India Department of Forensic Science School of Sciences JAIN (Deemed to be University) Bangalore Karnataka India Department of uGDX ATLAS SkillTech University Mumbai Maharashtra India

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

Non-orthogonal multiple- has become a viable 5G option that transforms user spectrum sharing. One important area of study in NOMA systems is user clustering (UC), which affects spectral utilisation by distributing users across different subcarriers with different power levels. The UC problem has been widely discussed in the literature, with a current emphasis on using artificial neural networks, also called ANN for UC using datasets that were acquired via the Brute-Force look (BF-S) technique. In order to accurately simulate the nonlinearity related to cluster formation, channel plurality, and transmission powers, this research presents a deep neuro network-based UC (DNN-UC). DNN-UC is more adept at managing UC than ANN-UC since it recognises that UC is non-convex NP-complete. In order to evaluate the mean squared error (MSE) and flow capability in an asymmetrical faded NOMA channel, the DNN-UC is trained using particular samples and then validated using testing data. The DNN-UC model, which approximates 97% of the BF-S method's throughput, delivers near-optimal throughput performance and stands out for its capacity to support hyper-parameter optimisations. This study highlights how DNN-UC can effectively tune hyperparameters to maximise learning capabilities and handle UC complications inside NOMA systems.

关键词： Training NOMA 5G mobile communication Artificial neural networks Transforms Throughput Complexity theory Resource management Testing Resilience

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Optimising Diabetic Retinopathy Classification using EfficientNetB7

Optimising Diabetic Retinopathy Classification using Efficie...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Ajisha Mathias J. Relin Francis Raj S R Raja R. Santhana Krishnan C. Puvanadevi S. Sujatha Electronics and Communication Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Avadi Chennai India Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Master of Computer Applications Hindustan Institute of Technology and Science Chennai India Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Information Technology Adhiyamaan College of Engineering Hosur India Department of Information Technology Adhiyamaan College of Engineering Hosur India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Diabetic retinopathy (DR) is a significant cause of vision impairment and blindness in individuals with diabetes, necessitating early and accurate detection of its various stages for effective treatment. This study applies EfficientN etB7, an advanced deep learning model, to classify different stages of diabetic retinopathy and compares its performance with other established models such as DenseN et121, Inception V3,and VGG16. The study uses datasets like the Kaggle Diabetic Retinopathy Detection and Messidor datasets, which provide annotated retinal images across multiple stages of DR: mild, moderate, severe, proliferative diabetic retinopathy, and normal. Key preprocessing techniques are employed to enhance the quality of the input data. These include noise reduction, contrast enhancement using CLAHE, and data augmentation to enhance the model's reslience. EfficientNetB7 is selected due to its optimal network depth, width, and resolution balance, which facilitates high accuracy with fewer computational resources. The model's performance is evaluated using metrics including Fl score, accuracy, precision, and recall, which are crucial for assessing the reliability of medical image classification models. Comparative analysis reveals that EfficientN etB7 outperforms DenseNet121, InceptionV3, and VGG16 across all evaluation metrics. Specifically, EfficientN etB7 achieves superior accuracy, precision, recall, and Fl score in classifying the different stages of diabetic retinopathy. These results highlight the effectiveness of EfficientN etB7 in providing reliable and efficient detection of diabetic retinopathy, highlighting its potential for improving clinical outcomes through early and precise diagnosis.

关键词： Measurement Deep learning Diabetic retinopathy Accuracy Computational modeling Visual impairment Data augmentation Data models Robustness Medical diagnostic imaging

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Distributed Analytics in Smart Grids with Edge Computing and Computational Intelligence

Distributed Analytics in Smart Grids with Edge Computing and...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Apoorva Joshi Preethi D Amandeep Gill Master of Computer Application Noida Institute of Engineering & Technology Greater Noida Uttar Pradesh India Department of Computer Science Jain (Deemed to be University) Bangalore India Department of R&D Cell Vivekananda Global University Jaipur India

Conventional methods like classification or clustering have limits when it comes to handling the complex task for dispersed data mining across heterogeneous sources and divergent data formats. This is especially true in scenarios where stochastic models or mathematical formulas may not be feasible. Data centralization is a prevalent trend that exacerbates problems including communication channel congestion and privacy difficulties related to sensitive data flows. In order to overcome these obstacles, this paper presents the Distributed Academic Engine (DAE), an adaptive edge-based scattered analytic engine designed for distributed networks. The DAE routinely breaks down and divides computations that need data from multiple sources among the nodes that are already in place, enabling the sharing of partial results without disclosing the original data. To get the full expression output, the master node combines these partial results. The expression is decomposed and processing jobs are distributed among nodes using three computational intelligence techniques: genetic algorithm, genetic algorithm using evolution control, the particle swarm optimization. This method's effectiveness is illustrated with a case study on smart grid KPI identification, which shows a noteworthy (over 91%) decrease in communication messages over conventional approaches. This demonstrates how well the suggested method works to reduce the difficulties related to distributed data mining.

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Correction to: Advanced model based machine learning technique for early stage prediction of ankylosing spondylitis under timely analysis with featured textures

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Multimedia Tools and Applications 2024年第26期83卷 68415-68420页

作者： Ahammad, Shaik Hasane Jayaraj, R. Shibu, S. Sujatha, V. Prathima, Ch Leo, L. Megalan Prabu, R. Thandaiah Hossain, Md.Amzad Rashed, Ahmed Nabih Zaki Department of ECE Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Data Science and Buisness Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Master of Computer Applications S.A. Engineering College Chennai India Department of Data Science School of Computing Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Deptartment of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS Saveetha University Chennai India Department of Electrical and Electronic Engineering Jashore University of Science and Technology Jashore Bangladesh Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf Egypt

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