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An Effective Analysis of Predicting Clothes types in E-commerce reviews using CatBoost Algorithm with Extreme Gradient Boosting Classifier

An Effective Analysis of Predicting Clothes types in E-comme...

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International Conference on Computational and Characterization Techniques in Engineering & sciences (CCTES)

作者： Pabbisetty Venkata Sai Varshith V Parthipan Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Big Data and Network Security Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

The purpose of this study is to determine whether the Catboost algorithm is superior to the Extreme Gradient Boosting Classifier (XGBoost) method in terms of its ability to generate predictions about the sorts of clothing based on evaluations from online retailers. This study contains 2 groups: Novel Catboost algorithm and Extreme Gradient Boosting Classifier, where each group is iterated for a sample size of 10 calculated using the ClinCalc software with 0.05 as the alpha value, and a CI of 95%. The e-commerce reviews dataset with a size of 23485 was considered from ***. The novel catboost algorithm accuracy is 92%, which appears to be superior to the XGBoost classifier's accuracy of 89%. It can be concluded that there exists a statistically significant distinction between the CatBoost algorithm and the XGBoost classifier algorithm. This is evidenced by a value of p=0.007 (Independent sample t-test p<0.05) in the SPSS statistical analysis prediction of clothing types based on e-commerce reviews.. Conclusion: In analysing various clothes types by E-commerce reviews the CatBoost algorithm results show better than the Extreme Gradient Boosting Classifier.

关键词： Accuracy Reviews Statistical analysis Software algorithms Clothing Prediction algorithms Boosting Software Classification algorithms Electronic commerce

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Online Container Scheduling for Low-Latency IoT Services in Edge Cluster Upgrade: A Reinforcement Learning Approach

Online Container Scheduling for Low-Latency IoT Services in ...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Cui, Hanshuai Tang, Zhiqing Lou, Jiong Jia, Weijia Beijing Normal University Institute of Artificial Intelligence and Future Networks Zhuhai519087 China Beijing Normal University School of Artificial Intelligence Beijing100875 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China BNU-HKBU United International College Guangdong Key Lab of Ai and Multi-Modal Data Processing Zhuhai519087 China

ISBN: (纸本)9798350345384

In Mobile Edge Computing (MEC), Internet of Things (IoT) devices offload computationally-intensive tasks to edge nodes, where they are executed within containers, reducing the reliance on centralized cloud infrastructure. Frequent up-grades are essential to maintain the efficient and secure operation of edge clusters. However, traditional cloud cluster upgrade strategies are ill-suited for edge clusters due to their geographically distributed nature and resource limitations. Therefore, it is crucial to properly schedule containers and upgrade edge clusters to minimize the impact on running tasks. In this paper, we propose a low-latency container scheduling algorithm for edge cluster upgrades. Specifically: 1) We formulate the online container scheduling problem for edge cluster upgrade to minimize the total task latency. 2) We propose a policy gradient-based reinforcement learning algorithm to address this problem, considering the unique characteristics of MEC. 3) Experimental results demonstrate that our algorithm reduces total task latency by approximately 27% compared to baseline algorithms. © 2023 IEEE.

关键词： Reinforcement learning

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Performance Analysis for Prediction of Trending Footwear Classification using DensNet compared with Logistic Regression model

Performance Analysis for Prediction of Trending Footwear Cla...

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International Conference on Computational and Characterization Techniques in Engineering & sciences (CCTES)

作者： E. V. Giridhar V Parthipan Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Big Data and Network Security Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

The purpose of this work is to investigate the effectiveness of DensNet and Logistic Regression in terms of accurately predicting the classification of footwear *** this study, there are two groups: the Novel DensNet model and the Logistic Regression model. Each group is being iterated for a sample size of ten, which is being generated with the ClinCalc software. The alpha value is set at 0.05, and the confidence interval is set at 95%. We took into consideration the footwear prediction dataset that was available on *** and had a size of 15854. The statistical analysis reveals a significant difference between the DensNet algorithm and the Logistic Regression model, with a value of p=0.001 (independent sample t-test p<0.05). The DensNet algorithm exhibits a significantly higher accuracy rate of 94.56% when compared to the Logistic Regression model, which records an accuracy rate of 90.62%.In comparison to the Logistic Regression model, the DensNet algorithm has a higher level of accuracy when it comes to predicting the Novel Footwear Trending situation.

关键词： Logistic regression Analytical models Accuracy Machine learning algorithms Statistical analysis Footwear Predictive models Prediction algorithms Market research Classification algorithms

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Deep Learning Integration in Teletherapy for Modernizing Mental Health Support

Deep Learning Integration in Teletherapy for Modernizing Men...

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Communication & Computing (WCONF), World Conference on

作者： Jagendra Singh Nitin Arvind Shelke Soumi Ghosh Arava Nagasri Anu Yadav Muniyandy Elangovan School of Computer Science Engineering & Technology Bennett University Greater Noida India Department of Information Technology Maharaja Agrasen Institute of Technology Delhi India Department of Computer Science and Engineering/Data Science) CVR College of Engineering Telangana India Department of Computer Science Bharati Vidyapeeth's college of Engineering New Delhi India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

The present research examines the implementation of elaborate deep learning techniques such as Variational Autoencoders (VAE), Facial Expression Recognition, and Sentiment Analysis for teletherapy platforms. In a number of experimental trials, the efficiency and performance of these instruments were judged. As it is evident from the calculations, over all examined tools have shown to deliver promising results. In the case of VAEs, we observed that the average reconstruction loss values were between 0.019 and 0.028, while the average KL divergence values remained in the range of 0.003 and 0.007. Overall, nonlinear generative models such as VAEs can be expected to capture the underlying structure of the input data in a more accurate way, and this is essential when generating personalized interventions for therapy. Furthermore, the Facial Expression Recognition model demonstrated strong performance as well, while the average accuracy ranges between 0.81 and 0.89. From the results, the Facial Expression models showed a high level of accuracy with an average score of 0.78 to 0.91. This means that the models successfully detected and classified facial expressions of a client's emotions into the respective categories. It provided therapists with detailed modeling of clients' emotions during therapy sessions. Similarly, Sentiment Analysis performed well with an average accuracy of between 0.84 and 0.92. It means that the Sentiment Analysis models could effectively classify text data into sentiment categories.

关键词： Deep learning Training Sentiment analysis Analytical models Accuracy Face recognition Medical treatment Mental health Rendering (computer graphics) data models

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Performance Analysis of Predicting LIC Stock Price using Lasso Regression Compared with Random Forest Regression

Performance Analysis of Predicting LIC Stock Price using Las...

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International Conference on Computational and Characterization Techniques in Engineering & sciences (CCTES)

作者： Chennupati Charan Kumar V Parthipan Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Big Data and Network Security Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

With regard to the objective of the entire study, the goal is to improve the accuracy of predicting stock price using Lasso Regression in comparison to Random Forest Regression. The Novel K-Nearest Neighbors and Support Vector Machine Algorithm are the two groups that are the emphasis of this research activity. Each of these groups is carried out ten times. For the purpose of calculating samples, the Clincal software was utilized. The alpha value was determined to be 0.05, and the confidence interval was set at 95%. We took into consideration the stock price prediction dataset that was available on *** and had a size of 12617. With a value of p=0.000 (Independent sample t-test p<0.05), the SPSS statistical analysis prediction of stock price demonstrates that there is a statistically significant difference between the Lasso regression algorithm and Random Forest Regression. This data indicates that the Lasso regression algorithm is superior to Random Forest Regression. For the Random Forest regression technique it obtained a percentage accuracy of 84%, while in the new improved Lasso regression approach it records a greater level of accuracy of 93%. The comparison of Random Forest Regressions and Lasso Regressions in the progress of predicting the stock price analysis shows that the latter was substantially more effective. This was revealed in the accomplishment of the current research development project.

关键词： Support vector machines Accuracy Statistical analysis Software algorithms Prediction algorithms Software Performance analysis Stock markets Random forests Overfitting

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Forensic Investigation of Malicious Activities in Digital Environments

Forensic Investigation of Malicious Activities in Digital En...

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data Engineering and Communication Systems (ICDECS), International Conference on

作者： A. Pasumpon Pandian A.S. Anakath R. Kannadasan K. Ravikumar D Abdul Kareem Department of Computer Science and Engineering CARE College of Engineering Trichy India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Vellore Tamil Nadu India Department of Computer Science & Engineering RRASE College of Engineering Padappai Chennai India Department of Artificial Intelligence and Data Science GRT Institute of Engineering and Technology Tiruttani Tamil Nadu India

Several digital dangers were investigated. Malware dominated analysis with 45 attacks. We found 30 phishing attacks. 22 data breaches, 15 cyber espionage, 18 identity theft. This indicates the kind and frequency of harassment digital forensic specialists endure. Digital evidence was crucial to the case. 65 pieces of evidence were collected on computers. 42 incidents showed smartphones and tablets’ digital investigative potential. There were 55 examples of communication patterns requiring network logs. In 28 and 12, cloud services and removable media provided proof. A wide range of evidence sources stresses the need for extensive collecting. Digital forensics were essential to the inquiry. In 70% of cases, disk imaging software created forensic copies of metadata-and timestamp-containing storage media. Network packet analyzers examined 48 traffic logs and communication patterns. To analyze volatile memory, 35 cases used memory forensics. In 25 cases, malware analysis platforms decrypted malware. In 60 situations, file recovery software recovered lost or hidden files. Digital forensic specialists’ diverse arsenal for thorough investigations is shown here. Legal and ethical considerations dominated the study. The law was important, and all inquiries followed norms. A strong chain of custody protects evidence integrity and admissibility. The data privacy rules were respected. Legal evidence is admissible in court. Ethics ensured objective investigations. This underlines digital forensic investigations’ legal and ethical foundation.

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Prediction Of Student Education Performance Using Googlenet Comparison With Over Alexnet Algorithm

Prediction Of Student Education Performance Using Googlenet ...

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International Conference on Computational and Characterization Techniques in Engineering & sciences (CCTES)

作者： Kotte. Venkata Rama Krishna V Parthipan Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Big Data and Network Security Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

An examination of the degree to which GoogleNet and Alexnet are accurate in predicting the academic success of students is the primary objective of this study. Each of the two groups that make up the program contains ten samples, and there are two groups altogether. For the purpose of determining the sample size, a threshold of 0.05%, a G-power pretest of 80%, and a confidence interval of 95% are utilized. We took into consideration the student education dataset that was available on *** and had a size of 66500. The method that was proposed was found to have an accuracy of 94.63% when compared to the existing system, which has an accuracy of only if. Furthermore, it demonstrates that there is a statistically significant difference between GoogleNet and Alexnet, with a value of p=0.001 (independent sample T test p<0.05) in the prediction of student educational performance using SPSS statistical analysis. When it comes to forecasting the academic success of students, the GoogleNet algorithm is more accurate than the Alexnet algorithm.

关键词： Accuracy Statistical analysis Instruments Education Artificial neural networks Prediction algorithms Forecasting Artificial intelligence

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Efficient DNA Sequence Identification: A Classification Approach Integrating Gaussian RBM 1

Efficient DNA Sequence Identification: A Classification Appr...

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1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Gulati, Chahat Barman, Biswajit Balde, Govind Hanmantrao Arul Mary Rexy, V. Sachdeva, Meena Patil, Harshal Birla Institute of Technology and Science BITS Department of Electronics and Communication Rajasthan Pilani India Asian International University Department of Biotechnology Imphal West Manipur India N.T.V.S'S G.T.Patil Arts Commerce and Science College Department of Zoology Nandurbar India Saveetha College of Liberal Arts and Sciences SIMATS Department of Commerce Chennai India JIMS Engineering Management and Technical Campus Department of Computer Science Greater Noida India Sri Balaji University Department of Data Science Maharashtra Pune India

ISBN: (纸本)9798350371802

Using machine learning techniques, this article examines cancer patients' DNA sequences. Cancer is certainly one of the most common and devastating diseases in the contemporary era, with a large number of new cases and deaths every year. Many people lose their lives each year to this terrible illness. Methods to combat this illness are the focus of intense investigation by scientists from a wide range of fields. Recently, machine learning has improved the analysis of cancer patients' DNA sequences. Sequencing is crucial for preprocessing, feature extraction, and model training. Among the preprocessing stages is cleaning up the sequence, which entails eliminating PCR repetitions, impurities, and vectors. Feature extraction has been carried out on each DNA sequence in order to retrieve statistical characteristics. It is of utmost importance to regulate features accurately when training a Gaussian RBM. A grade of 96.44% demonstrated a considerable improvement in accuracy, according to the results. © 2024 IEEE.

关键词： Polymerase chain reaction

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250m Daytime Real-Time LED-OWC System

250m Daytime Real-Time LED-OWC System

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Shulei Wang Yuanzhe Qu Jian Chen Zongbi Yu Yanhao Chen Yongchun Yuan Shidong Yuan Qianwu Zhang Junjie Zhang Key Laboratory of Specialty Optics and Optical Access Networks Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China Engineering No.1 Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai University Shanghai China Engineering No.1 Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai China Key Laboratory of Specialty Optics and Optical Access Networks Shanghai Institute for Advanced Communication and Data Science Shanghai University Teralink Optical Corporation (Shanghai) Shanghai China

We have demonstrated a simple LED-OWC system based on FPGA. From the indoor and field tests, the realtime extreme communication sensitivity and transmission distance at 1Mbps OOK are of −41.93dBm and 250m respectively.

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Flying Neural Network-Based Optimistic Financial Early Alert System in AI Model 6

Flying Neural Network-Based Optimistic Financial Early Alert...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Boggavarapu, Saikrishna Ali, S. Shabbir Manikandan, G. Mohanraj, R. Singh, Devesh Pratap Revathi, R. University of the Cumberlands Department of Information Technology Prospect HeightsIL United States KL University Department of Electronics & Communication Engineering Guntur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai Thandalam India St.Martin's Engineering College Department of Artificial Intelligence and Data Science Telangana Secunderabad India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttrakhand Dehradun India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India

ISBN: (纸本)9798350304480

An innovative method for early warnings in the area of financial risk management is the Flying Neural Network-based Optimistic Financial Early Alert System (FNN-OFEAS). The FNN-OFEAS outperforms conventional models and rule-based systems because it makes use of the Flying Neural Network architecture and incorporates sentiment analysis. Using a fictitious dataset including past stock prices, financial ratios, economic indicators, and sentiment analysis data, this study details the development, use, and findings of the FNN-OFEAS. The results show that the system is effective at capturing complicated temporal correlations, recognising non-linear patterns, and factoring in market sentiment, all of which contribute to improved risk identification in a timely and accurate manner. Early risk identification, investment decision assistance, regulatory compliance, and financial stability evaluation are just few of the real-world uses for the FNN-OFEAS. Its application is beneficial for financial institutions, investors, regulators, and policymakers since it enhances decision-making, reduces risk, and sheds light on previously hidden information. More work has to be done to improve the model architecture, include other data sources, provide real-time implementations, evaluate the model's resilience, and make it easier to comprehend the results. Addressing financial risks and bolstering financial stability in volatile market settings is one area where the FNN-OFEAS shows promise. © 2023 IEEE.

关键词： 'Flying Neural Network Optimistic Financial Early Alert System (OFEAS)' conventional models early alerts economic indicators Financial anomalies financial ratios financial risk management financial stability historical stock prices hypothetical dataset investment decision support market sentiment practical applications risk detection rule-based systems sentiment analysis

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