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A Trustworthy Product Rating System Based on the Blockchain with E-Commerce Business Model

A Trustworthy Product Rating System Based on the Blockchain ...

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： Alapati Smitha Kranthi Gotte Ranjith Kumar Nimmagari Tejasri Raviprakash Prateeksha Siddhanti Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaadeswaram India School of Computer Science & Artificial Intelligence SR University Warangal India IT Department Malla Reddy Engineering College for Women Dhulapally Hyderabad India Department of Computer Science and Engineering Kalpataru Institute of Technology Tiptur India Department of AI and ML Nitte Meenakshi Institute of Technology Yelahanka Bengaluru India

This article covers the difficulties that customers and retailers encounter while searching for and getting real items at reasonable costs in the internet marketplace. While the development of e-commerce has made it simpler than ever for people to purchase from the comfort of their own homes, it has also resulted in an explosion of phoney reviews and rating manipulation, causing shoppers to lose trust in both the reviews they read and the rating systems themselves. This article offers a method based on blockchain technology to provide e-commerce enterprises with a trustworthy and transparent product rating system. The recommended method for evaluating things in the paper is based on blockchain technology, which the study believes provides e-commerce enterprises with a trustworthy and transparent rating system. According to the outcome analysis, 90% of users thought the blockchain-based grading system was easier to use and more straightforward than prior ones.

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Average Probability of Error Analysis of Multi Antenna Decode-and-Forward Cooperative Relaying System

Average Probability of Error Analysis of Multi Antenna Decod...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Naman Sachdeva Vinoth Babu Kumaravelu Helen Sheeba J Arthi Murugadass Agbotiname Lucky Imoize Department of Communication Engineering School of Electronics Engineering Vellore Institute of Technology Vellore India Department of Computer Science and Engineering (AI & ML) Sreenivasa Institute of Technology and Management Studies Chittoor India Department of Electrical and Electronics Engineering University of Lagos Akoka Nigeria

Cooperative relaying with multiple input-multiple output (MIMO) is a sort of cooperative communication that uses transmitters, relays, and receivers with multiple antennas to improve coverage, increase data rate, and conserve energy, among other things. The end-to-end performance study of a multi-antenna relay-aided communication system is the main subject of this research. We set out to develop the theoretical average probability of error (APE) expression and to validate it using Monte-Carlo simulations. Additionally, we are evaluating the suggested system's APE at high signal-to-noise ratios (SNR) and contrasting it with the established approaches.

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ai-Based Network Traffic Prioritization for Latency-Sensitive Applications in 6G Networks

AI-Based Network Traffic Prioritization for Latency-Sensitiv...

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

作者： S Dhanalakshmi Talupuri Aswini Devi Rohit Agarwal Laith H. Alzubaidi S. Senthil Kumar S. B. Prakalya Department of CSE(AI&ML) CMR Institute of Technology Hyderabad Telangana India Department of CSE GRIET Hyderabad Telangana India Department of Computer Engineering & Applications GLA University Mathura Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of EEE New Prince Shri Bhavani College of Engineering and Technology Chennai Tamil Nadu India Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798350368109

ISBN: (纸本)9798350368116

This applies because the emergence of 6G networks brings new problems of controlling the growing and heterogonous traffic streams from different connected devices. An important gap refers to the difficulties of providing Quality of Service (QoS) for latency critical traffic like autonomous transportation, tele-operations, tele-surgey or augmented reality. Conventional approaches to QoS do not fit well within the dynamics and variability of 6G traffic, then require additional and more versatile methods of managing the given traffic. This paper focuses on the deployment of Artificial Intelligence enabled traffic prioritization for latency critical applications in the 6G networks. Powered by deep learning models; convolutional neural networks (CNN) and recurrent neural networks (RNN), the proposed system predicts the traffic conditions and responds by proper resource allocation. When the concept of network slicing is combined with ai, the system is even more capable of meeting the needs of the various applications. The performance evaluation from experimental results, conducted by using real traffic datasets in combination with synthetic datasets, reveals that the presented ai-based system offers less latency, better bandwidth utilization and less packet drop rates than other conventional QoS algorithms. Further, the proposed approach based on ai improves energy consumption making the future 6G networks more sustainable. As shown in this research, through ai, the limitations that may be incurred in the management of network traffic for highly latency-sensitive applications in 6G are managed efficiently and on a large scale.

关键词： 6G mobile communication Recurrent neural networks Network slicing Packet loss Quality of service Telecommunication traffic Bandwidth Real-time systems Convolutional neural networks Resource management

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Computing Stock Market Price Behavior Using Machine Learning Approach

Computing Stock Market Price Behavior Using Machine Learning...

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International Conference on Information Systems and computer Networks (ISCON)

作者： Prakash Kumar Sarangi Jayashree Mohanty Srikanta Kumar Mohapatra Premananda Sahu Department of Computer Science & Engineering(AI & ML) Vardhaman College of Engineering Hyderabad India IIIT Bhubaneswar Odisha Chitkara University Institute of Engineering and Technology Chitkara University Punjab India SRM Institute of Science and Technology SRMIST Ghaziabad UP India

Stock markets are the most beneficial invention for creating wealth and are a key feature in any investment plan in developing countries to emphasize on economic resources including financial services. Investment in the stock market is getting popular among common people day by day. Every day almost 5000 Bombay Stock Exchange (BSE) listed trade companies offer appx 240 million stocks which make a total of 2000Cr+ Indian rupees in stock market investment. Small Businesses, Banks, and brokerage firms, all depend on the stock markets to make profits. Thus, analyzing the behaviour of the stock market will help improve the Indian economy and help people predict the upcoming financial risks. As the stock market is frequently fluctuating and contains randomness, it is hard to predict the behaviour using traditional forecasting methods. In this study, we have implemented various machine learning models (Linear Regression, Polynomial Regression &SVR) and have evaluated them based on different criteria to compare the efficiency of these models in predicting the stock market behaviour accurately. Appx. 25 years of BSE historical data is used for training and testing these models and two different ways of these models, firstly by predicting the stock market values and secondly,by predicting its behaviour.

关键词： Support vector machines Training Biological system modeling Computational modeling Machine learning Predictive models Data models

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The Effect of Programming Language in Software Bug Prediction

The Effect of Programming Language in Software Bug Predictio...

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Communication, Computing and Industry 4.0 (C2I4), International Conference on

作者： Gopika Jayadev Kaparthi Gayathri Tina Babu Harika Gandiboina Kavya Sudha Bondu Venkteswalru Department of AI & ML School of Engineering Dayananda Sagar University Bengaluru India Department of Computer Science and Engineering School of Engineering Dayananda Sagar University Bengaluru India

ISBN: (数字)9781665474979

ISBN: (纸本)9781665474986

Software maintenance is a very important phase in the life cycle of software development. As part of maintenance, we need to identify bugs within the code and fix them for every release. Software bug prediction (SBP) allows us to identify modules in the software that may have the tendency to be buggy. This enables us to perform targeted testing and properly plan maintenance cycles. In most research performed, we observed that dataset of software programs written in C language were used. Each programming language are inherently different and has it's own constructs, practices and nuances. In this research we focus on identifying if there exists a specific classifier that works well for each programming language which in turn is used to analyze the effect of programming language on Software Bug Prediction. Datasets of software written in C, C++ and Java has been collected and the most accurate classifier for each dataset of a programming language has been identified. The ml models used in this paper include Naive Bayes, Decision Tree and Random Forest.

关键词： Software maintenance Java computer bugs Forestry Maintenance engineering Software Telecommunication computing

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A Comparative Analysis of Machine Learning Algorithms for Agricultural Drought Forecasting

A Comparative Analysis of Machine Learning Algorithms for Ag...

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International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)

作者： Jayavrinda Vrindavanam Tina Babu Harika Gandiboina Gopika Jayadev Department of AI & ML School of Engineering Dayananda Sagar Univeristy Bengaluru India Department of Computer Science and Engineering School of Engineering Dayananda Sagar Univeristy Bengaluru India

The occurrence of drought is a climatic feature and is a phenomenon that happens over time. Depending on the severity, it can last for a short or long time. Farming households are trying to meet due to rising agricultural operating costs that hinder the country's development. This study aims to forecast the severity of the drought over time. Drought scores vary from 0 to 5, with 0 and 5 indicating the least and highest intensity drought conditions. This is done using weather and soil data of a region consisting of Precipitation, Surface Pressure, Humidity, Temperature, Wind Speed, and Soil data. The main reasons for the cause of drought are first identified. These features are used to train the multivariate time series models like Prophet, VAR (Vector Auto-Regression), LSTM (Long short-term memory), and Comparison of actual v/s predicted values. The results were promising. The study has done an analysis comparing different machine learning algorithms for agricultural drought forecasting and it was found that the LSTM model performed better than VAR and Prophet models.

关键词： Reactive power Analytical models Machine learning algorithms Biological system modeling Wind speed Droughts Time series analysis

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Endoscopic Image Processing: Evaluating the Robustness of Deep Neural Networks

Endoscopic Image Processing: Evaluating the Robustness of De...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： C N Gireesh Babu Kaparapu Satish Babu Sajja Suneel K.Srinivasa Rao Divya Muralitharan R Aarthi Dept.of CSE/ISE BMS Institute of Technology and Management Bengaluru Karnataka Department of Electronics and Communication Engineering Geetanjali College of Engineering and Technology Hyderabad Telangana India Department of CSE(Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad Telangana India Department of AI&ML K.S.R.M. College of Engineering Kadapa Andhra Pradesh Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of ECE R.M.D Engineering College R. S. M Nagar Gummidipoondi

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

The Hybrid CNN-Transformer Model (HCNNT) is a novel model which exploits Convolutional Neural Networks (CNN) for spatial feature extraction and Transformers for capturing contextual and long range dependencies in order to carry complex image classification. In order to evaluate the algorithm proposed, a new model has been introduced and trained on a data set of gastrointestinal (GI) image classification, which is derived from a balanced data set with 226 images available on Kaggle. Although the cascaded CNN part is to learn elaborate visual characteristics, the improved understanding of complex patterns and relationships is achieved by the Transformer component to process these features. When both are integrated, we gain a powerful system that has high accuracy in distinguishing GI bleeding condition from normal condition. Extensive training and optimization of the hybrid model using binary cross-entropy loss and Adam optimizer has achieved state-of-the-art performance. Through the experiments evaluated on the HCNNT model, it is evident that an accuracy of 97.34% proves to be significantly beneficial, its practicality in terms of reliability for medical image classification that essentially improves the accuracy of diagnosis in gastrointestinal healthcare.

关键词： Training Visualization Accuracy Transformers Feature extraction Gastrointestinal tract Robustness Convolutional neural networks Image classification Context modeling

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Application of Internet of Things and Big Data in Improving Supply Chain Financial Risk Management System

Application of Internet of Things and Big Data in Improving ...

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IEEE UP Section Conference on Electrical computer and Electronics (UPCON)

作者： Mohammad Salameh Zaid Almahairah Sushmita Goswami Purushotam Naidu Karri I. Mohan Krishna Mohd. Aarif Geetha Manoharan Management Information System Department A 1- Isra University Institute of Business Management GLA university Mathura Uttar Pradesh Department of Computer Science and Engineering (AI&ML) GVP College of Engineering for Women Andhra University Visakhapatnam Andhra Pradesh Koneru Lakshmaian Education Foundation Vaddeswaram Shri JJT University Jhunjhunu Rajasthan School of Business SR University Warangal Telangana

Technological advances and “Supply Chain Risk Management or SCRM” have a clear relationship since disruptions have an impact on SCM. The practical and quantitative participation in technology-enhanced SCRM also reflects the high number of disruptors and wide-ranging effects in this industry. People's inclination to invest in the “Internet of Things (IoT) and big data analytics (BDA)” appears to be to be affected by either the supply or demand of technological advancements. The required sensor and connectivity technologies have already advanced and are now available to the majority of businesses. On the other hand, businesses require more operational data to deal with the erratic and dynamic nature of supply systems. The state-of-the-art IoT breakthroughs in current supply chain risk management research are objectively summarised in this study. The major goal is to locate scholarly literature examples that demonstrate how businesses can use real-time information from operational objects to inform decisions. IoT device adoption and proliferation meet the needs for data to be gathered and analysed on changing business scenarios across the supply chain. However, it is still unclear how businesses may directly incorporate data recorded by the IoT into their decision-making. There are many physical items with only the most basic data able to manage multiple thanks to the resource-intensive nature of today's SCM tasks including shipping, storing, and servicing. Secondary qualitative method is used to collect relevant and factual information related to this research topic.

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Enhancing Road Visibility by Real-Time Rain, Haze, and Fog Detection and Removal System for Traffic Accident Prevention Using OpenCV

Enhancing Road Visibility by Real-Time Rain, Haze, and Fog D...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Shilpa Choudhary Sandeep Kumar Munish Kumar Monali Gulhane Bhawna Kaliraman Rashmi Verma Department of Computer Science and Engineering (AI & ML) Neil Gogte Institute of Technology Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of CSE Symbiosis Institute of Technology (SIT) Nagpur Symbiosis International (Deemed University) (SIU) Pune India Department of Computer Science and Engineering DPG Institute of Technology and Management Gurugram India

Nowadays, both the corporate and public research sectors are interested in autonomous vehicles. Levels of unpredictability are the reasons self-driving cars have not yet made it to the consumer market. Multiple sensors are frequently used to address this, which helps the vehicle's system become more robust. The most often used sensors are radars, lidars, and cameras, but their costs can increase quickly, making them unaffordable in some markets. Utilizing fewer but stronger sensors for visualization could help with this. This resolves the issue of the decreased view range caused by rainy weather, a specific failure mode for camera sensors. The state-of-the-art object identification with distance estimation technique, You Only Look Once (YOLOv3), is tested with the Kalman filter and discrete wavelet transform with bilateral filtering as rain, haze, and fog removal strategies. With YOLOv3, filtered movies in daytime and dusk conditions were tested, and the results reveal that the accuracy has not increased sufficiently to be valid for use in autonomous vehicles. The study field has potential and thus indicates that more object identification and distance estimate techniques be considered as future work.

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Recognition and Identification of COVID-19 from Chest X-Rays for Earlier Diagnosis

Recognition and Identification of COVID-19 from Chest X-Rays...

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Expert Clouds and Applications (ICOECA), International Conference on

作者： Kavitha P Manish Sharma Pradeep Kumar G M Amit Barve Harshal Patil Ramya Maranan Department of Computer Science and Engineering CMR Institute of Technology Bengaluru Karnataka India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of AI & ML BMS Institute of Technology and Management Bangalore Karnataka India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul Institute of Technology Parul University P.O. Limda Gujarat India Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India Department of Research and Innovation SIMATS Saveetha School of Engineering Chennai Tamil Nadu India

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

Early detection is critical for controlling the spread and death of the COVID-19 worldwide pandemic, which affects global health care and lifestyle. The gold standard diagnosis, Reverse Transcription Polymerase Chain Reaction (RT-PCR) is both costly and has a lengthy process time. So more readily available and fast diagnostic methods are necessary. Many researchers proved that COVID-19 data can be effectively detected by Chest X-rays (CXR). However, diagnosing chest X-ray images for COVID-19 is difficult and error-prone. The only way to improve the COVID-19 diagnosis is to use DL algorithms. In this research, a novel deep learning model Convolutional Neural Network and Autoencoder (CNN-AE) tuned by Bat Optimization Algorithm (BOA) is proposed. The proposed approach would identify whether the CXR images were positive or negative for COVID. We received the CXR images from Kaggle. The pre-processing phases in the proposed technique assist with providing relevant information and reducing biased outputs. This is followed by the classification model. We were able to evaluate the performance of our proposed model by using Kaggle datasets. Our proposed model is evaluated using Kaggle datasets and achieved an average accuracy, True Positive Rate (TPR), True Negative Rate (TNR), precision, F1-score, False Positive Rate (FPR), and False Negative Rate (FNR) of 99.28%, 99.42%, 99.14%, 99.42%, 99.28%, 0.85%, and 0.57% respectively. The outcome of the proposed method is compared with the existing and similar research. A comparative study shows that our proposed method outperforms the state-of-art methods.

关键词： COVID-19 Training Accuracy Pandemics X-rays Convolutional neural networks Polymers

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