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3rd International Conference on Emerging Trends in Expert Applications and Security, ICE-TEAS 2024

作者： Gupta, Keshav Dev Pandey, Rishi Pathak, Devendra Nath Ranjan, Rakesh Gupta, Vijay Department of Computer Science Engineering Poornima College of Engineering Rajsthan Jaipur India Department of Animation & Multimedia Birla Institute of Technology Ranchi Mesra India Department of Computer Science & Engineering Poornima College of Engineering ISI-6 RIICO Institutional Area Sitapura Rajasthan Jaipur India Department of Computer Science & Engineering Babu Banarasi Das University Lucknow India Department of Computer Science International School of Informatics and Management Sector 12 Mahaveer Marg Jaipur Mansarovar India

ISBN: (纸本)9789819739905

The development of technology, especially the use of resources provided by artificial intelligence (AI), has advanced in areas where business is gaining importance and is an important fear of economic growth. The use of technical skills in the creation of products and services is not unique to design. In today's world, artificial intelligence (AI) has become an industry in many countries, and design is no exception. However, the lines of work performed by humans and machines are changing rapidly, and as a result, the global market is also changing a lot. This is the result of very rapidly developing technology. This raises the question of how this progress has and will continue to influence the future work of designers. What skills do designers need to enter or stay in this industry? The purpose of this article is to provide a summary of research findings on the impact of intelligence on the performance of designers, as well as an analysis that includes an evaluation of the potential and benefits of intelligent design solutions. Case studies and other scientific studies are used to analyze the applications and benefits of AI-based solutions. Studies can be theoretical or empirical. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： International trade

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Stock Trading Decision Method Based on Stop Loss Double Threshold 8

Stock Trading Decision Method Based on Stop Loss Double Thre...

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8th International Conference on computer and Communication Systems, ICCCS 2023

作者： Wu, Jimmy Ming-Tai Ma, Shaowei Wang, Ke Yan, Huizhen Zhang, Qinghua College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9781665456128

Artificial intelligence technology is widely used in stock market stock forecasting, which can help investors achieve 'buy at a low price and sell at a high price'. Many scholars are focusing on how to increase investors' returns and reduce their risks. This study takes stocks in the United States and Taiwan as the research objects, and historical data, futures, and options as the data set, in an attempt to find a less risky and more rewarding method of stock trading decisions. Long short-term memory neural networks (LSTM) are used to study stock price fluctuations. The Kelly criterion for fund management is used to calculate the optimal investment ratio, and a stop loss strategy based on double thresholds is added so that investors can sell within a certain period when the stock price falls to a certain range, thereby reducing investors' risks. The particle swarm optimization algorithm (PSO) is used to optimize the critical values of trading threshold and stop loss range. The experimental results show that the stop loss strategy based on double thresholds can effectively reduce investors' risk. The addition of leading indicators such as futures and options can increase the accuracy of stock prediction and increase the return of investors. © 2023 IEEE.

关键词： Long short-term memory

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Bridging the Language Gap: Transformer-Based BERT for Fake News Detection in Low-Resource Settings 2nd

Bridging the Language Gap: Transformer-Based BERT for Fake...

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2nd International Conference on Speech and Language Technologies for Low-Resource Languages, SPELLL 2023

作者： Sivanaiah, Rajalakshmi Suresh, Subhankar Pandian, Sushmithaa Suseelan, Angel Deborah Department of Computer Science and Engineering Sri Sivasubramaniya Nadar College of Engineering Tamil Nadu Chennai India Department of Electronics and Communication Engineering Sri Sivasubramaniya Nadar College of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9783031584947

Global rise in internet usage and access has caused fake news to become an ever spreading phenomenon. Since fake news is intended to influence public opinion, it has a significant impact on the world. False information spreading has the potential to be extremely harmful and can manipulate the public in many ways. Numerous methods for spotting fake news have been developed to stop its spread. However, it should be noted that this challenge is not only confined to widely spoken languages. Low resource languages face challenges in combating the spread of fake news due to limited semantic and computational constraints. This study demonstrates the use of various BERT models which are considered to be state-of-the-art to classify news as real or fake for a low resource language like Malay, closing the knowledge gap in fake news detection of languages with insufficient linguistics and computational resources. With the help of these experiments, we achieve maximum F1 scores of 86% for mBERT, 80% for XLnet, 87% for IndoBert, 88% for MalayBERT and 84% for mT5 respectively, suggesting their potential in addressing the challenges posed by fake news in Malay. The results of these models are compared with each other to study and draw inferences on their performances. These findings hold significant implications for the development of more robust and language-specific fake news detection systems, contributing to the overall effort to curb the spread of misinformation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Classification (of information)

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Deep Learning-Based Diagnosis System for Liver Cancer Through Multimodal Determination for Early Treatment

Deep Learning-Based Diagnosis System for Liver Cancer Throug...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Fatima, N. Sabiyath Singh, Ramendra Swathi, Akuthota Choudhary, Amar Ahmed, Azhar Sharma, Chhaya B.S. Abdur Rahman Crescent Institute of Science & Technology Department of Computer Science and Engineering Chennai India Ghaziabad India Cvr College of Engineering Department of Computer Science and Engineering Mangalpalli Rangareddy India Alliance University Department Electronics and Communication Engineering Bengaluru India Kcc Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India Raj Kumar Goel Institute of Technology Department of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350379716

The present research provides an analysis of the development and implementation of a deep learning-based diagnosis system for liver cancer. The provided system was developed using a dataset encompassing 2300 sensor readings, including imaging, clinical, and genomic data. A range of deep learning features was implemented to improve the early detection of liver cancer such as CNN, VGG 16, VGG 19, and Inception V3. The data were divided into two categories: one is for training, and another used for testing. The results of the data analysis have shown that VGG 19 provides the highest diagnostic accuracy of 98.76%, with VGG 16, Inception V3 and CNN displaying the levels of 94.5%, 93.4%, and 90.25%, respectively. The Model performance analysis was also augmented at the application of precision, recall, and F1 score indicators, as well as using AUC-ROC values which highlighted VGG 19 as the most effective algorithm. Another method of evaluation was the application of confusion matrices for plotting the results based on the number of true positive, true negative, false positive, and false negative detections, where all the samples from the dataset were added to each model calculation. The obtained results have shown the importance of multimodal data for the increase of diagnosis accuracy and revealed the promising potential of advanced computation techniques in medical analytics. In such a way, it can be stated that the developed deep learning models contribute to the ability of healthcare specialists in the definition of liver cancer at early stages, which allows timely interventions and facilitates the development of a range of personalized treatment protocols. The application of such algorithms has the potential to reshape liver cancer diagnostics, where major tasks are associated with further improvement and validation of the developed algorithms based on clinical case studies. © 2024 IEEE.

关键词： Contrastive Learning

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Elite Frequent-Utility Pattern Mining in Hadoop Environments 6

Elite Frequent-Utility Pattern Mining in Hadoop Environments

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6th International Conference on Artificial Intelligence and Big Data, ICAIBD 2023

作者： Wu, Jimmy Ming-Tai Liu, Yadong Shandong University of Science and Technology College of Computer Science and Engineering Qingdao China

ISBN: (纸本)9781665491259

With the extensive research of skyline frequent-utility pattern mining in the field of data mining, various algorithms emerge in an endless stream, but because these algorithms are limited by the size of the data set, traditional pattern algorithms do not perform well in medium and large databases. Therefore, this paper proposed the EFUP-MR algorithm by adding thresholds and applied it to the hadoop platform. It can run in medium and large databases, solve the problem of low efficiency in the traditional pattern, and because of the threshold setting, it can help users obtain the required elite itemsets more accurately. Finally, through experiments in different types of data sets, the proposed algorithm performance in larger datasets is proved. © 2023 IEEE.

关键词： MapReduce

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Enhancing DNS and IoT Firewall Rule-Based Algorithms and Techniques 1st

Enhancing DNS and IoT Firewall Rule-Based Algorithms and Tec...

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1st International Conference on Data engineering and Machine Intelligence, ICDEMI 2023

作者： Senthil Kumar, P. Asha, V. Kanaga Suba Raja, S. Samuel Peter James, I. Shadan Women’s College of Engineering and Technology Telangana Hyderabad India Department of Computer Science and Engineering SRM Institute of Science and Technology Tiruchirappalli India Shadan Women’s College of Engineering and Technology Hyderabad India

ISBN: (纸本)9789819776153

In the realm of network security, next-generation firewalls serve as the primary line of defense against potential threats, ensuring the integrity and confidentiality of data transmission. The research initiative commences by establishing the fundamental concepts of firewalls, DNS, and IoT firewalls as indispensable components in the protective arsenal of network security. The exploration navigates the transition toward the next generation of DNS and IoT firewalls, illuminating their heightened capabilities in countering domain-specific attacks. Their indispensable role in shielding IoT devices and networks from threats takes center stage in this exploration. When evaluating effectiveness, the F1 score functions as a statistical measure for assessing classification models, and the F1 score’s significance within the firewall ecosystem is vividly contextualized. As the inquiry unfurls into the realm of firewall technologies, their burgeoning importance and far-reaching implications within the realms of cyber security and network defense come into sharper focus. This study assesses the efficacy of two firewall methods, namely DNS Firewall and IoT Firewall. In the case of the DNS Firewall, the analysis yielded 93 True Positives (TP), 7 False Positives (FP), 11 False Negatives (FN), and an F1 Score of 0.91. Conversely, the IoT Firewall Technique exhibited 88 True Positives (TP), 15 False Positives (FP), and 4 False Negatives (FN), along with an associated F1 Score of 0.88. These findings shed light on the performance and capabilities of these firewall techniques in bolstering network security. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Network security

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Predictive Management of EV Charging Stations Using Machine Learning 10

Predictive Management of EV Charging Stations Using Machine ...

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10th International Conference on Electrical Energy Systems, ICEES 2024

作者： Alex David, S. Begum, Almas Hemalatha, D. Senthil, K. Monikaa, R. Ruth Naveena, N. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Easwari Engineering College Department of Computer Science and Engineering Chennai India Hindustan Institute of Technology & Science Department of Mathematics Chennai India

ISBN: (纸本)9798350353778

Based on experimental results the predictive accuracy of Random Forest, Decision Tree, and XGBoost was much higher than the Linear Regression algorithm in EV Charging Management using machine learning models. Random Forest handled the charging station dynamics well, and the complex data relationships were managed easily with XGBoost. On the other hand, Linear Regression performed better because results displayed high errors and its model fit numbers were not significant. In contrast, the Decision Tree has glaring issues with overfitting and generalization. The results highlight the importance of advanced data science methods for efficient EV infrastructure management by yielding exact forecasts about demand and energy consumption behaviors. Future endeavors will continue to build upon these models, increasing the scale of analysis and providing state-of-the-art solutions towards developing an approach for both scalable development as well as a push toward more sustainable transportation with improved operational efficiencies and reliability within EV networks. © 2024 IEEE.

关键词： Adversarial machine learning

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Phishing Websites Detection by Using Optimized Stacking Ensemble Model

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computer Systems science & engineering 2022年第4期41卷 109-125页

作者： Zeyad Ghaleb Al-Mekhlafi Badiea Abdulkarem Mohammed Mohammed Al-Sarem Faisal Saeed Tawfik Al-Hadhrami Mohammad T.Alshammari Abdulrahman Alreshidi Talal Sarheed Alshammari College of Computer Science and Engineering University of Ha'ilHa'il81481KSA College of Computer Sciences and Engineering Hodeidah UniversityHodeidah967Yemen College of Computer Science and Engineering Taibah UniversityAl-Madinah42353KSA Nottingham Trent University MansfieldNG185BHUnited Kingdom

Phishing attacks are security attacks that do not affect only individuals’or organizations’websites but may affect Internet of Things(IoT)devices and *** environment is an exposed environment for such *** may use thingbots software for the dispersal of hidden junk emails that are not noticed by *** and deep learning and other methods were used to design detection methods for these ***,there is still a need to enhance detection *** of an ensemble classiﬁcation method for phishing website(PW)detection is proposed in this study.A Genetic Algo-rithm(GA)was used for the proposed method optimization by tuning several ensemble Machine Learning(ML)methods parameters,including Random Forest(RF),AdaBoost(AB),XGBoost(XGB),Bagging(BA),GradientBoost(GB),and LightGBM(LGBM).These were accomplished by ranking the optimized classi-ﬁers to pick out the best classiﬁers as a base for the proposed method.A PW data-set that is made up of 4898 PWs and 6157 legitimate websites(LWs)was used for this study's *** a result,detection accuracy was enhanced and reached 97.16 percent.

关键词： Phishing websites ensemble classiﬁers optimization methods genetic algorithm

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Station-Wise Boarding Passenger Flow Prediction for Public Transport Using Various Machine-Learning Methods 7th

Station-Wise Boarding Passenger Flow Prediction for Public T...

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7th International Conference of Transportation Research Group of India, CTRG 2023

作者： Patel, Madhuri Patel, Samir B. Swain, Debabrata Patel, Shubh Department of Computer Engineering Pandit Deendayal Energy University Gandhingar India Department of Information Technology L D College of Engineering Ahmedabad India Department of Computer Science & Engineering Nirma University Ahmedabad India

ISBN: (纸本)9789819610365

The migration behavior of regular passengers in city transport refers to the movement patterns and preferences of individuals who use public transportation on a regular basis. These passengers may have specific routes, travel times, and preferences for certain modes of transportation. Passenger flow prediction is crucial for understanding and managing this behavior. Accurate predictions enable transportation operators to optimize their services by adjusting vehicle frequency and capacity, deploying additional services during peak periods, providing real-time information, identifying high-demand areas, and expanding the network. This improves the efficiency and reliability of public transportation, catering to the needs of regular passengers. Data science and machine-learning methods allow us to extract correlations from historical data, improving the accuracy of passenger flow prediction. The passenger flow on a station is highly affected by various factors such as the day of the week, holiday, rain, available routes from that station, and some uncertain events like COVID-19. In this study, we have successfully reported passenger flow prediction at various stations using ensemble machine-learning algorithms. Comparative analysis of implemented work has been carried out with the help of statistical parameters and visual infographic details. For accurate prediction model, accurate data cleaning, pre-processing and feature selection based on correlation have been performed on real dataset of Thane Municipal Transport (TMT) from April 2021 to May 2022. We have also compared the performance of prediction models based on month, day and individual station for moderately deviated data and highly deviated data resulted due to effect of COVID-19 pandemic and Taukte cyclone. An exhaustive comparative analysis between train set and test set have been reported with necessary parameters. Comparing to benchmark models, the XGB regressor and Random forest model can reach most accur

关键词： Decision trees

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Plant Disease Prediction Using Efficientnet

Plant Disease Prediction Using Efficientnet

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2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Karthikeyan, P. Arvind, K.R. Ajith Kumar, G. Saranya, V. KCG College of Technology Department of Computer Science and Engineering Karapakkam Chennai600097 India KCG College of Technology Computer Science and Engineering Karapakkam Chennai600097 India

ISBN: (纸本)9798350333244

As population increases the demand for the food and the raw materials for it also increases linearly. And also agriculture and related sectors contribute more to the GDP of a country. But due to disease the cultivation of plants is affected worstly. Which leads to a huge loss in both the GDP food production. It also leads to an increase in the price of food and raw materials. To solve this problem, in this paper we suggested a solution of creating an application which will predict the disease of the plant from the image of the affected plant leaf. The application uses the Machine Learning algorithm EfficientN et B3 model to predict the disease. The Idea is to collect the disease images as a dataset of multiple diseases of different plants, Pre-process the data, Select the ML algorithm, Train and test it, Application development using Streamlit and deploy it in the Streamlit cloud. This system processes the images of different classes, trimming the images numbers and size of those to optimize it to provide to the EfficientN et B3 model. Then Evaluate the model using a test dataset. Save the model with weights of the model to use it in the application. In application load the model and weights to it. The application is created with future scope, as users can select the disease prediction for several plants within a single application. Every plant data is trained with the same data model to simplify the model loading process in application. Changing the weights is enough to predict the disease in different plants. © 2023 IEEE.

关键词： Machine learning

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