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2nd international conference on soft computing in data science, scds 2016

2nd International Conference on Soft Computing in Data Scien...

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2nd international conference on soft computing in data science, scds 2016

ISBN: (纸本)9789811027765

The proceedings contain 27 papers. The special focus in this conference is on Artificial Neural Networks. The topics include: shallow network performance in an increasing image dimension;applied neural network model to search for target credit card customers;multi-step time series forecasting using ridge polynomial neural network with error-output feedbacks;comparative feature selection of crime data in Thailand;interactive big data visualization model based on hot issues;metabolites selection and classification of metabolomics data on Alzheimer’s disease using random forest;a multi-objectives genetic algorithm clustering ensembles based approach to summarize relational data;automated generating Thai stupa image descriptions with grid pattern and decision tree;imbalance effects on classification using binary logistic regression;weak classifiers performance measure in handling noisy clinical trial data;assigning different weights to feature values in naive bayes;an automatic construction of Malay stop words based on aggregation method;modeling and forecasting mudharabah investment with risk by using geometric brownian motion;multi-script text detection and classification from natural scenes;algebraic and graphical interpretation of complex fuzzy annulus;a hierarchical fuzzy logic control system for Malaysian motor tariff with risk factors;modeling steam generator system of pressurized water reactor using fuzzy arithmetic;edge detection of flat electroencephalography image via classical and fuzzy approach;judgment of slang based on character feature and feature expression based on slang’s context feature;factors affecting sentiment prediction of Malay news headlines using machine learning approaches.

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PriorHealth: A Priority-Aware Task Scheduling Framework for Managing Healthcare data in Fog computing Applications 2

PriorHealth: A Priority-Aware Task Scheduling Framework for ...

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2nd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Bebortta, Sujit Sekhar Tripathy, Subhranshu Tripathy, Niva Ravenshaw University Department of Computer Science Cuttack753003 India School of Computer Engineering KIIT Deemed to Be University Bhubaneswar751024 India DRIEMS Autonomous College Department of Computer Science and Engineering Cuttack754025 India

ISBN: (纸本)9798350327533

The advent of wearable sensor technology, like oximeters, accelerometers, and gyroscope-based monitoring devices, are being widely used to address the important problem of health monitoring for mobility patients. Unusual patient data prompts healthcare facilities to take immediate action by sending out alerts. The major goal of this paper is to investigate the field of fog computing-assisted healthcare service with regard to priority-aware healthcare task offloading. When it comes to providing end users with extra processing power for time-sensitive tasks, the fog server is essential. But this ever-changing environment is really hard when some jobs demand a far slower response time than others. This problem is solved by introducing a priority-aware scheduling and task offloading technique that prioritizes high-priority jobs, especially those with strict deadlines, when allocating CPU resources. Any CPU resources that are left over are then carefully allocated to provide for a longer average response time for the low priority tasks, which are lower on the task hierarchy. Furthermore, a basic lower-bound is obtained for the mean response time of assignments with hard and soft deadlines. This study demonstrates the effectiveness of the proposed task scheduling strategy with a wide range of simulations, skillfully allocating resources for end users and edge servers by giving hard-deadline tasks top priority and carefully scheduling lower priority soft-deadline tasks. © 2024 IEEE.

关键词： Fog computing Healthcare Priority Scheduling Processing Cost Response Time Task Offloading

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Research on Intrusion Detection Technology Based on Ensemble Learning and Decision Tree Algorithm 2

Research on Intrusion Detection Technology Based on Ensemble...

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2nd international conference on Signal Processing and Intelligent computing, SPIC 2024

作者： Shang, Lei Li, Aihua Shenyang Ligong University School of Information Science and Engineering Shenyang China

ISBN: (纸本)9798350368888

With the development and progress of network technology, network played an increasingly important role in modern life, but with the wide application of network, network security incidents also come, and the overall number of each year has maintained a continuous rising trend. In the face of increasing network intrusion threats, how to effectively identify malicious traffic from network traffic became more and more important, and the research in the field of intrusion detection has developed from the traditional single machine learning algorithm to the combination of multiple algorithms. In this paper, Tree-ensemble, which is a combination of Ensemble learning and decision Tree algorithm, was proposed to carry out the research on intrusion detection technology. KDD99 was selected as the intrusion detection data set, the eigenvalue selection algorithm based on random forest is used to sort the importance of features, and 44 eigenvalues are selected as the classification features of normal traffic and malicious traffic. Decision tree, Random forest, Extreme tree, AdaBoosting Adaptive Boosting, Gradient Boosting Decision Tree, XGBoost, Ensemble Voting (soft-voting),Ensemble Stacking (Logistic Regression as meta-classifier) were used to build a classification model for security analysis of data KDD99. Among the constructed classification models, the XGBoost algorithm model performed best, and the models constructed by the collective voting (soft voting) and set superposition (logistic regression as a meta-classifier) methods performed well. © 2024 IEEE.

关键词： Network intrusion

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Empowering Oral Healthcare: CNN-Assisted Diagnosis for Improved Patient Outcomes 2

Empowering Oral Healthcare: CNN-Assisted Diagnosis for Impro...

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2nd international conference on data science and Information System, ICDSIS 2024

作者： Garrepalli, Vaishnavi Gupta, Vishwanath Abhishek, S. Anjali, T. Amrita School Of Computing Amrita Vishwa Vidyapeetham Department Of Computer Science And Engineering Amritapuri India Verizon Data Services India Pvt Ltd Hyderabad India Rolls-Royce Inc Bangalore India

ISBN: (纸本)9798350381665

In today's world, health is very important, and among the many aspects of well-being, oral wellness is one of the crucial components. Diagnostic investigation in everyday clinical procedures primarily consists of reviewing periapical radiography, which visualizes dental anatomy such as teeth, bone, and soft tissues within the oral cavity. Nonetheless, subjective errors may arise during human interpretation of dental radiographs which also take a lot of time. Most of these dental X-rays have an important role in diagnosing various conditions like Implant, Fillings, Cavity, and Impacted teeth under accurate assessment of classification of dental radiographs. These imaging techniques can help dentists detect anomalies within the mouth through this diagnosis process. To improve the accuracy of diagnosis and reduce the costs of clinical operations, this study presents a novel approach that automates the detection of problematic teeth and other dental diseases using these x-ray images using CNN (Convolutional Neural Networks) architecture VGG19 for making the right diagnosis choices. © 2024 IEEE.

关键词： Image classification

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Application Research of Computer Big data Technology in Enterprise Economic Management System 2

Application Research of Computer Big Data Technology in Ente...

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2nd international conference on data Analytics, computing and Artificial Intelligence, ICDACAI 2023

作者： Wei, Zhang Shanxi Railway Institute Weinan China

ISBN: (纸本)9798350339765

The enterprise asset economic management information is large, the operation is tedious, the traditional operation mode cannot meet the new work needs. Economic asset management system based on B/S system is a typical management information system (MIS), which has the characteristics of strong integrity, simple flow, convenient operation, rich functions and strong versatility. It has realized the comprehensive management of economic assets. This paper combines the SaaS application model with the economic asset management to get the economic asset management system based on SaaS. Then this paper discusses the system architecture, function, security scheme and business model and puts forward a system scheme to realize the economic asset management system under the SaaS model. The goal of this scheme is to realize the third level maturity model of SaaS. © 2023 IEEE.

关键词： Big data

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An Improved soft computing based Congestion Control in Routing the data in Wireless Sensor Network 2

An Improved Soft Computing based Congestion Control in Routi...

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2nd international conference on Electronics and Renewable Systems, ICEARS 2023

作者： Manojkumar, K. Devi, S. Prist Deemed to Be University Department of Computer Science and Engineering Tamil Nadu Thanjavur India Sree Venkateswara College of Engineering Departmtnt of Electronics and Communication Engineering Andhra Pradesh Nellore India

ISBN: (纸本)9798350346640

The congestion control is defined as a contrivance that is used to control and monitor the entry of data packets into the network. This helps to enhance and develop a better infrastructure in the communication network. The wireless sensor network is a communication network with infrastructure that are accompanied with less wireless network that is composed of innumerable nodes that are transportable through several instructions in the system. The nodes are interconnected in the communication system with each other in random fashion. The congestion is denoted as a decrease in the quality of service (QOS) in network that results in packet loss and new connections in the network may get blocked. The congestion can be prevented by implementing open loop congestion control policies. This can be controlled by the source or through the destination. This happens when there is a shortage in the buffer space. The soft computing based congestion control in routing the data is done through the following techniques that are classified as fuzzy logic system, artificial neural network, game theory based and particle swarm optimization techniques. © 2023 IEEE.

关键词： Quality of service

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Efficient and Robust Multirobot Navigation and Task Allocation Using soft Actor Critic 2

Efficient and Robust Multirobot Navigation and Task Allocati...

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2nd international conference on Machine Learning and data Engineering, ICMLDE 2023

作者： Hersi, Abdikafi Hashi Divya Udayan, J. Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

In this research, we present a novel solution to the problem of multi-robot task allocation (MRTA) by utilizing a variant of the state-of-art Deep Reinforcement Learning algorithm, soft Actor-Critic (SAC) within Multirobot Navigation Systems(MRS). Our approach addresses the existing research gaps in MRTA for MRS, which have received limited attention and have proven challenging to solve using conventional methods. Through extensive simulation evaluations, we demonstrate that our system outperforms current state-of-the-art methods in terms of performance metrics. These findings highlight the effectiveness and superiority of our proposed approach in overcoming the inherent challenges of large-scale MRS. Additionally, we contribute to the field by proposing improvements to the Task Allocation Index (TAI) metric, enabling a more comprehensive evaluation of MRTA performance. © 2024 Elsevier B.V.. All rights reserved.

关键词： Reinforcement learning

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Enhancing Cloud Security with Deep Learning-Based Intrusion Detection in Cloud computing Environments 2

Enhancing Cloud Security with Deep Learning-Based Intrusion ...

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2nd international conference on Advancement in Computation and Computer Technologies, InCACCT 2024

作者： Aswini Devi, T. Jain, Arpit Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering AP Vaddeswaram India GRIET Hyderabad India

ISBN: (纸本)9798350371314

Cloud computing (CC) offers a wide range of on-demand resources and services over the internet. However, due to the inherent vulnerability of the cloud's dispersed architecture, guaranteeing the privacy and security of cloud assets and resources remains a tough task. data and service protection in cloud systems is a continuous and serious concern. This article presents a fresh solution to this problem by utilizing Deep Learning (DL) techniques for intrusion detection in cloud computing systems. The present research analyses the strategies used by several Intrusion Detection Systems (IDS). As network infrastructures expand, so do security risks, increasing the need for dependable intrusion detection. Developers have created numerous intrusion detection systems (IDS) in response to the expanding security and privacy issues that saturate modern computer networks. Improving the datasets used for training and testing these security solutions requires equal focus as building defense systems. Improved datasets significantly enhance the detection capabilities of both offline and online intrusion detection models. This essay contributes to the growing corpus of research on CC security by conducting a thorough examination of publicly available network-based IDS datasets. It emphasizes the need to incorporate cutting-edge DL approaches into IDS for increased security. As cloud computing continues to transform the digital world, the findings of this study have major implications for safeguarding sensitive data and key services in cloud-based ecosystems. Furthermore, they provide solutions to the current anomaly detection issues caused by insufficient normal patterns in training data. The detection of accuracy information for two, five, and twenty-three classes and soft-max regression (SMR) feature learning perform similarly for 5-class and 23-class categorization, and it was also found that STL completed all categorization categories with above 98% accuracy. © 2024 IEEE.

关键词： Intrusion detection

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Sustainable Development of Employability and Skill Development in Outcome-based Education: A Machine Learning Approach 2

Sustainable Development of Employability and Skill Developme...

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2nd international conference on Sustainable computing and data Communication Systems, ICscds 2023

作者： Bora, Mousoomi Baruah, Rupam Assam Science and Technology University Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9781665491990

The contribution of technological enhancement has played a vital role in strengthening the country's economy. The Higher Education lnstitutions (HEI) are providing placement assistance to the students by employing various measures as a part of their curriculum. A few of the attempts that HEIs have adopted to enhance soft skills as well as hard skills of the students are, course structure modification, student-mentoring, industry-academia collaboration etc. In spite of all the noble efforts, many unemployed engineers are witnessed in the society. Therefore, this research aims to develop an effective and accurate framework that would be highly beneficial for both students as well as teachers in HEIs. A Semi-Supervised-Sequence-Prediction (SSSP) machine learning framework has been designed to predict students' academic achievement towards the completion of a four-year degree course in an outcome-based education system. A time series analysis of students' data has been conducted to analyze the progression of final cumulative GPA and employability prospect in sequential manner. The results show the effectiveness of the proposed framework in predicting employability of students along with the areas where students need improvement in order to be employable. This research provides an inclusive roadmap, empowering the use of machine learning for employability and skill development in outcome-based education. © 2023 IEEE.

关键词： Students

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Classification of Quora Insincere Questionnaire Using soft computing Paradigm 1

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2nd international conference on Advanced Network Technologies and Intelligent computing (ANTIC)

作者： Vijayeeta, Prachi Pattnayak, Parthasarathi Jawed, Kashis KIIT Deemed Be Univ Sch Comp Applicat Bhubaneswar Odisha India

ISBN: (数字)9783031281839

ISBN: (纸本)9783031281822;9783031281839

The global system of interconnection covers an enormous scope of multiple data resources and feasible access mechanisms. It provides a direction for developing robust frameworks to interact and exchange knowledge in a diversified domain. But a major problem arises when a fast response to queries are raised by end users from different corners of the worldwide forum. Quora is one of the web based platform for receiving questions and suggesting answers accordingly. Many a times it is observed that the queries seems to be very irrelevant and creates chaos situation. Researchers are still struggling to equip new policies and mechanisms to deal with virulent and non-virulent queries. The cognition behind this study, aims at designing of a model to classify sincere and insincere questions using soft computing mechanisms. In this paper, seven algorithms out of which three ensemble techniques are employed on the Kaggle dataset.

关键词： Text classification Multinomial naive bayes SVM Xgboost Quora Tokenization Text classification Multinomial naive bayes SVM

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