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International conference of Electronics, Communication and Aerospace technology (ICECA)

作者： S. Geeitha P. Renuka S. Ramya K. Sinduja Department of Information Technology M.Kumarasamy College of engineering Karur India Deparment of Computer Science and Engineering Paavai engineering college Nammakal India Department of Information Technology Sri Krishna College of engineering and technology Coimbatore India

Cervical cancer is the fourth most prevalent disease in women globally as well as main cause of cancer- related mortality among women. The overall survival rates of cervical carcinoma patients are significantly impacted by the detection of recurrence. The disease is entirely treatable, though, if it is found in its early, precancerous levels. According to current figures, the death percentage a few decades ago was excessive. Women who have previously had treatment with CIN3 are most at riskof developing cervical cancer once more. The dataset includes information on the effects of various treatments on post- recurrence survival. The progression of a disease is detected in this research. Of the 4913 patients who were diagnosed withcervical cancer overall, 260 women with the disease suffered recurrences. The Data has been roughly categorized into 27 characteristics that are important for illness staging and therapy. Thevery important attributes selected are FIGO 2009, chemotherapy,radiotherapy, recurrence confirmed by a pathologist, etc. Five different models are trained to predict the progression. Logistic regression is finally predicted with a high level of accuracy of 96.3%. Two hundred and sixty patients with 27 attributes consistof a number of missing values. Research is implemented through the Orange platform. The missing values are balanced through Average and Most frequent data imputation method. Important attributes are selected through the ReliefF technique, and then disease progression is predicted by 5 different algorithms Logistic regression, Support vector machine, Naive Bayes, Stochastic gradient decent and neural network. Finally logistic regression predict the progression of a disease with highest accuracy of 96.3%.

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Whether Prediction using Auto-correlation and Machine Learning Algorithms

Whether Prediction using Auto-correlation and Machine Learni...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Shruti V. Bharati Sanjay S. Shitole Shakti Sharma Digambar V. Puri Department of Information Technology SNDT University Mumbai India School of Computer Science Engineering and Technology Bennett University Greater Noida U.P India Deparment of Electronics and Telecommunication DYP University Navi-Mumbai India

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

As economic expansion accelerates, the deterioration of air quality persists. The primary catalysts for the initiation and progression of intense air pollution processes are the emissions of high pollution intensity and adverse weather conditions. The current scenario of rapid climate change has rendered traditional weather prediction methods ineffective and more challenging. Consequently, there is a pressing need for enhanced and dependable weather forecasting techniques. These forecasts have significant implications for a country’s economy and the well-being of its populace. The central goal of present study is assess and predict the air quality index (AQI) for Mumbai city of Maharashtra in India based on seasonal auto-regressive moving average (SARMA) with machine learning models. To validate the proposed technique, a six month dataset were utilized that were collected at the Shreemati Nathibai Damodar Thackersey Women’s University, Santacruz (w), Mumbai. This study provides the minimum root mean square error (RMSE) between predicted and recorded AQI. The performance comparative analysis of different kernels of support vector machine (SVM) with K-Nearest Neighbour (KNN) has been evaluated to validate the proposed method.

关键词： Support vector machines Biological system modeling Urban areas Weather forecasting Pressing Machine learning Predictive models

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Fuzzy random proof of work for consensus algorithm in blockchain

Fuzzy random proof of work for consensus algorithm in blockc...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Kumar, Akhilesh Kareen, Z.H. Mudhafar, Mustafa Arnone, Gioia Umaralievich, Mekhmonov Sultonali Bhowmick, Avijit PG Department of Information Technology Gaya College Bihar Gaya India Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University College Babylon Iraq Department of Anesthesia and Health Care Faculty of Altuff College University Iraq Disaq - Università Degli Studi Di Napoli "Parthenope" Italy Tashkent Institute of Finance Tashkent Uzbekistan Deparment of Computer Science and Engineering BBIT Kolkata India

ISBN: (纸本)9781032669663

The consensus method, being one of the most important aspects of blockchain, differs depending on the sector. The commonly used proof-of-work (PoW) consensus technique for public chain application scenarios still has challenges that are difficult to handle, such as security and high computer power. As a result, the PoW method is investigated in terms of enlarging the solution space and improving the adjustment mechanism. A consensus technique based on fuzzy random proof of work (FRMH) is presented. The FRMH algorithm improves the security of the blockchain consensus mechanism by increasing the solution space of the consensus algorithm by incorporating technologies such as a fuzzy transitive closure matrix in fuzzy mathematics. In addition, the FRMH algorithm uses a dual adjustment method to cope with machines with high computational power and thus solves the issue of high computing power being difficult to regulate on the blockchain. Through mathematics, it has been shown that the FRMH algorithm has greatly enhanced solution space and greater processing power control. © 2024 The Author(s).

关键词： Computing power

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Social Service Robot using Gesture recognition technique 4

Social Service Robot using Gesture recognition technique

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4th National Conference on Communication Systems, NCOCS 2022

作者： Jessintha, D. Kumar, P. Praveen Jaisiva, S. Kumar, T. Ananth Ananth, Christo Electronics and Communication Engineering Easwari Engineering College Chennai India Deparment of Information Technology Sri Manakulavinayagar Engineering College Puducherry India Department of EEE M.Kumarasamy College of Engineering Tamilnadu Karur India Computer Science and Engineering IFET College of Engineering Tamilnadu India Department of Natural and Exact Sciences Samarkand State University Uzbekistan

A robot is a machine that can automatically do a task or a series of tasks based on its programming and environment. They are artificially built machines or devices that can perform activities with utmost accuracy and precision minimizing time constraints. Service robots are technologically advanced machines deployed to service and maintain certain activities. Research findings convey the essential fact that serving robots are now being deployed worldwide. Social robotics is one such field that heavily involves an interaction between humans and an artificially built machine. These man-built machines interact with humans and can also understand social terms and words. Modernization has bought changes in design and mechanisms due to this ever-lasting growth in technology and innovation. Therefore, food industries are also dynamically adapting to the new changes in the field of automation to reduce human workload and increase the quality of service. Deployment of a robot in the food industries which help to aid deaf and mute people who face social constraints is an ever-growing challenge faced by engineers for the last few decades. Moreover, a contactless form of speedy service system which accomplishes its task with at most precision and reduced complexity is a feat yet to be perfected. Preservation of personal hygiene, a better quality of service, and reduced labour costs is achieved. © Published under licence by IOP Publishing Ltd.

关键词： Quality of service

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Data Center Traffic Characteristics in the Era of Cloud Application

Data Center Traffic Characteristics in the Era of Cloud Appl...

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OptoElectronics and Communications Conference, OECC

作者： Yanxian Bi Fulong Yan Shengjie Guo Zhihao Chen Xiangliang Ma Jie Zhang CETC Academy of Electronics and Information Technology Group Co. Ltd China Academy of Electronic and Information Technology Deparment of Electrical Engineering Beijing University of Posts and Telecommunications Beijing China Beijing Institute of Computer Technology and ApplicationsChina Aerospace Science and Technology Corporation Beijing China School of Integrated Circuits Beijing University of Posts and Telecommunications Beijing China

The traffic pattern of a cloud data center is presented with intra-Pod traffic ratio of ≥50%. Besides, the quantitative investigation of queues demonstrates that the switch link utilization ratio could approach 0.4 w... 详细信息

ISBN: (数字)9798350308396

ISBN: (纸本)9798350308402

关键词： Data centers Packet loss Optimization methods Switches Queueing analysis

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A Random Forest Stock Prediction Model Based on Bayesian Optimization

A Random Forest Stock Prediction Model Based on Bayesian Opt...

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International Conference on Artificial Intelligence and Big Data (ICAIBD)

作者： Yajuan Zhang Xiuyan Zheng Sihan Yang Shangyu Meng Z. Y. Yang Xinghui Fei College of Information Engineering Hainan Vocational University of Science and Technology Haikou Hainan China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu China Network engineer department Guiyang Shengteng Network Technology Co. LTD Guiyang China Electrical and Computer Engineer Deparment Colorado State University Fort Collins U.S.

ISBN: (数字)9798350385106

ISBN: (纸本)9798350385113

Random forest models often rely on human-determined parameters, which may introduce subjectivity and affect the degree of optimization of the model. To address this issue, we use a random forest approach based on Bayesian optimization to reduce the influence of human factors and improve the fitting. Bayesian optimization prevents the model from falling into the dilemma of local optimum. In order to show the effect of the optimization, we compare the support vector machine, the original random forest and the LGBM model, and perform Bayesian optimization on the latter two to achieve better results. Experiments show that the optimized model performs well in stock return prediction with a reduction of 0.01 in RMSE and MAE, a reduction of 0.4 in MAPE, and an improvement of 0.02 in R2 value compared to the other models. The Bayesian optimization based Random Forest model achieves the lowest prediction error on different datasets and has wide applicability.

关键词： Support vector machines Accuracy Time series analysis Human factors Predictive models Data models Bayes methods

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Retraction Note: Enabling secure and efficient industry 4.0 transformation through trust-authorized anomaly detection in cloud environments with a hybrid AI approach

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Optical and Quantum Electronics 2024年第10期56卷 1-2页

作者： Prakash, N. Vignesh, J. Ashwin, M. Ramadass, Sudhir Veeranjaneyulu, N. V. Athawale, Shashikant Ravuri, Ananda Subramanian, Balambigai Department of Information Technology B.S Abdur Rahman Crescent Institute of Science and Technology Vandalur India Department of ISE Jain University Bengaluru India Deparment of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Vaddeswaram India Sterck Systems Chennai India Department of Information Technology VFSTR Deemed to Be University Vadlamudi Guntur District India Department of Computer Engineering AISSMS COE Pune India Intel Corporation Hillsboro USA Department of ECE Kongu Engineering College Perundurai India

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Geo-SID: A new geocast safety information dissemination protocol in VANET for urban areas 2

Geo-SID: A new geocast safety information dissemination prot...

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2nd International Conference on Computation, Automation and Knowledge Management, ICCAKM 2021

作者： Senouci, Oussama Aliouat, Zibouda Harous, Saad Mohamed EI-Bachir EI-Ibrahimi University Computer Science Deparment Bordj Bou arreridj Algeria LRSD Laboratory Ferhat Abbas Setif1 University Computer Science Dept. Setif Algeria College of Information Technology University Al-Ain United Arab Emirates

ISBN: (纸本)9781728194912

Recently, many researches on Vehicular Ad Hoc Networks (VANETs) have been performed because of the many related applications. Data dissemination is among the challenging issues face by these kinds of networks to overcome several problems, such as broadcast storm, and network fragmentation. In this respect, the present work proposes a new Geocast-based Safety information Dissemination protocol for VANETs, called Geo-SID. The proposed protocol is used for dissemination of safety information in a V2V scenario. It is specifically designed and suitable for an urban environment. The main goal of Geo-SID scheme is to perform the safety information dissemination process within a Zone of Relevance (ZoR) and without using the Zone of Forwarding (ZoF) similar to existing dissemination protocols in the literature. Furthermore, our solution aims to eliminate the broadcast storm problem by avoiding irrelevant alert messages and providing an efficient safety data dissemination across a ZoR area by extending the coverage area, reducing the network overhead and improving the latency. We simulate our proposed protocol using network simulator NS-3 and SUMO environment. Our proposed protocol outperforms similar protocols available in the literature based on the obtained simulation results. © 2021 IEEE.

关键词： Vehicular ad hoc networks

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Exploring the Feasibility of Transformer Based Models on Question Relatedness

Exploring the Feasibility of Transformer Based Models on Que...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Honglin Shu Pei Gao Ziwei Yang Chen Li Man Wu Department of Computing Hong Kong Polytechnic University Hong Kong SAR China Department of Information Science Nara Institute of Science and Technology Nara Japan Department of Bioscience and Bioinformatics Kyushu Institute of Technology Iizuka Japan Deparment of Information and Computer Science Keio University Tokyo Japan

Professional question answering communities, such as Stack Overflow, are becoming a significant aspect of many intellectual endeavors. As a result, developing a strategy for swiftly locating relevant questions and answers can effectively assist experts in issue solving. Various detection models have been presented to address the problem of question-relatedness prediction, but all are sub-optimal since they cannot effectively capture the long-distance dependency of a long sequence. With the invention of the self-attention mechanism and transformer, a better approach to dealing with question-relatedness prediction has just emerged. The primary objective of this study is to investigate the feasibility of the transformer-based model for question-relatedness problems. We turn the question relatedness problem into a text classification problem and introduce a representative transformer-based model, namely Bidirectional Encoder Representation from Transformers (BERT), to deal with text classification. In our experiment, we show that BERT outperforms both SoftSVM and BiDotLSTM, implying that the transformer-based model has considerable potential to address the challenge in the question-relatedness problem. Furthermore, since we can re-formulate text classification problem into a link prediction problem, we discuss the possibility of incorporating the transformer-based approach and graph representation learning.

关键词： Representation learning Technological innovation Smart cities Source coding Text categorization Bit error rate Statistical learning

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Selecting Machine Learning Models for Student Performance Prediction Aligned with Pedagogical Objectives

Selecting Machine Learning Models for Student Performance Pr...

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Computing (ICOCO), IEEE International Conference on

作者： Ijaz Khan Mohammed Hazli Mohamed Zabil Abdul Rahim Ahmad Nafaa Jabeur Department of Computing College of Computing and Informatics Univsiti Tenaga Nasional Kajang Malaysia Information Technology Dept. Buraimi University College Al-Buraimi Oman Deparment of Computing and Informatics' Universiti Tenaga Nasional (UniTen) Kajang Malaysia Computer Science Dept. German University of Technology Muscat Oman

Machine learning classifiers emerge as productive tools to develop prediction models which forecast the final outcome of the students, in a course, and provide an opportunity to the instructor to take appropriate measures. A single prediction model may not be enough to achieve all the objectives of an instructor. The selection of appropriate prediction models is yet a challenging task. This paper proposes a novel framework that applies a set of machine learning classifiers over the training dataset of a course. Along with the training dataset, the instructor clarifies the prime of objective of the binary model whether it must focus over the identification of fail, pass, or both students. The framework recommends a convenient model to achieve the specific objectives of the instructor. This research concludes that models inclined to reduce misclassification of minority class are suitable if the primary objective of the institution is the correct identification of students who are struggling to achieve the minimum course requirements. Further, a model specialized solely in amplifying the correct classification of majority class instance is appropriate if the aim is to focus on the correct identification of excellent students. The empirical analysis in this research leads towards the fact that accuracy alone is not an adequate metric to assess the performance of a model and that systematic selection of evaluation metrics is required to develop constructive models.

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