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A Multi-syndrome Pathology for Breast Cancer through Intelligent Learning

IOP Conference Series: Materials science and engineering 2021年第1期1055卷

作者： C.N. Vanitha S. Malathy Assistant Professor Department of Computer Science and Engineering Kongu Engineering College Perundurai - 638 060

The field of data analytics dealt with various methods to analyze the available data and help us to drawn into a point of conclusion about that information. Machine will learn the behavior based on the algorithms applied on those data and provide an appropriate conclusion. When these kinds of data analytic metrics applied to medical diagnosis various symptoms obtained from the patients and then the disease could be detected effectively. When certain optimization methods are applied on the data, the overall efficiency of the system will be improved considerably. To meet the challenges in diagnosing breast cancer based on clinical record sources, different symptoms in descriptions, clinical symptoms, a novel method, which consists of choosing the suitable features, multi-class functions, and multi-label parameters, has been proposed. The proposed work will be implemented as two steps such as discriminative symptoms selection and multi-syndrome learning. Public Breast Cancer Wisconsin (Diagnostic) Data Set has been taken for implementation. The breast cancer data set utilized for this work comprises 699 tumour samples. In that 458(65.5 %) samples belong to Benevolent (non-cancer) tumours and 241(34.5%) belongs to malevolent(cancer) tumours. The overall verisimilitude is 95.23% which will be improved to a greater extent when compared to the existing schemes.

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Optimizing Wireless Multimedia Sensor Networks Path Selection using Resource Levelling Technique in Transmitting Endoscopy Biomedical data

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IOP Conference Series: Materials science and engineering 2021年第1期1055卷

作者： C.N. Vanitha S Malathy Assistant Professor Department of Computer Science and Engineering Kongu Engineering College Perundurai - 638 060

The urge to transmit especially biomedical data rapidly is essential to save human life. Every user demands for high speed communication with no delay. But the network suffers from huge delay which degrades the performance of the network and it also dissatisfies the user expectation. To meet the user needs, the proposed work mainly focuses on increasing the speed and minimizing the delay in communication using Internet applied in Things (IoT) and Wireless Multimedia Sensor Networks. It can be achieved by avoiding over allocation of resources in the network i.e., resource level balancing and planning the transmission. These two have greater impact in upgrading the performance of network. This paper introduces a unique methodology for Wireless Multimedia Sensor Networks and end-device assisted system. The transmission time can reduced by using the concept of probability estimation for crashing the interpretative path. It extends the battery level of the motes by enhancing the pace and minimizing the energy level during transmission process. The Crashing Interpretative path protocol is simulated against other literature method. The simulation results shows that the CCP provides better efficiency in terms of transmission time, the lifetime of sensors for biomedical data transmission. This methodology is greatly helpful to save precious human life.

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A System to automate the development of anomaly-based network intrusion detection model

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Journal of Physics: Conference Series 2021年第1期2089卷

作者： B Padmaja K Sai Sravan E Krishna Rao Patro G Chandra Sekhar Associate Professor Department of Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad India Student Department of Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad India Assistant Professor Department of Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad India

Cyber security is the major concern in today's world. Over the past couple of decades, the internet has grown to such an extent that almost every individual living on this planet has the access to the internet today. This can be viewed as one of the major achievements in the human race, but on the flip side of the coin, this gave rise to a lot of security issues for every individual or the company that is accessing the web through the internet. Hackers have become active and are always monitoring the networks to grab every possible opportunity to attack a system and make the best fortune out of its vulnerabilities. To safeguard people's and organization's privacy in this cyberspace, different network intrusion detection systems have been developed to detect the hacker's presence in the networks. These systems fall under signature based and anomaly based intrusion detection systems. This paper deals with using anomaly based intrusion detection technique to develop an automation system to both train and test supervised machine learning models, which is developed to classify real time network traffic as to whether it is malicious or not. Currently the best models by considering both detection success rate and the false positives rate are Artificial Neural Networks(ANN) followed by Support Vector Machines(SVM). In this paper, it is verified that Artificial Neural Network (ANN) based machine learning with wrapper feature selection outperforms support vector machine (SVM) technique while classifying network traffic as harmful or harmless. Initially to evaluate the performance of the system, NSL-KDD dataset is used to train and test the SVM and ANN models and finally classify real time network traffic using these models. This system can be used to carry out model building automatically on the new datasets and also for classifying the behaviour of the provided dataset without having to code.

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Efficient Predictive Model for Utilization of Computing Resources using Machine Learning Techniques

Efficient Predictive Model for Utilization of Computing Reso...

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International Conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： K.S. Sendhil Kumar M. Anbarasi G. Siva Shanmugam Achyut Shankar School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu Assistant Professor Department of CSE Amity University Uttar Pradesh

ISBN: (数字)9781728127910

ISBN: (纸本)9781728127927

Data mining is a viable innovation to break down and extract patterns from crude information, which can change the original data into up-to-date information. Predictive analytics includes an assortment of factual systems that analyze present and historical facts to make forecasts about future or generally obscure occasions. Machine learning incorporates statistical methods for regression and classification. The objective of machine learning is to create a predictive model that is unclear from the correct model. The assessed relative execution qualities were evaluated by Ein-Dor and feldermesser utilizing a linear regression method considering the properties machine cycle time, minimum main memory, maximum main memory, cache memory, minimum channels, and maximum channels. This relationship is communicated as a mathematical statement that predicts the reaction variable published relative performance as a linear function of the parameters. The proposed technique utilizes machine learning work to re-phrase prediction as an optimization problem. Confidence prediction and polynomial regression include imaginative application utilization and promising research. The experimental evaluation platform contains detailed performance analysis of the preferred methods. It is expected that this machine learning approach gives a quick and straightforward approach to fabricate applications.

关键词： Predictive models Mathematical model Data models Regression tree analysis Machine learning Computational modeling Linear regression

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Real-time GB pattern convolution neural network-based brain image classification

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AIP Conference Proceedings 2024年第1期3075卷

作者： Amarendra Reddy Panyala M. Baskar Research Scholar Department of Computer Science and Engineering School of Computing SRM Institute of Science and Technology Kattankulathur Chengalpattu Chennai Tamil Nadu 603 203 India Assistant Professor Department of Information Technology MLR Institute of Technology Hyderabad Telangana 500043 India Associate Professor Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chengalpattu Chennai Tamil Nadu 603 203 India

Medical image classification has been identified as a critical task in several medical solutions. Brain image classification has been identified as a challenging task that has been approached with different methods. The existing methods use different features like shape, texture, and gray to classify brain images. However, the methods need to improve performance in categorizing the brain image and detecting the presence of tumors. An efficient GB Pattern Convolution Neural Network (GBP-CNN) is presented to handle this issue. The method involves preprocessing the brain image given with Adaptive Feature Distribution Normalizer (AFDN) algorithm. The Grey Canvas Segmentation (GCS) technique was used to segment the normalized image. The feature vector is created by extracting the binary and grayscale patterns from the image that has been segmented. A convolution neural network is trained to recognize the features that were extracted. Two convolution layers in the CNN’s design are used to convolve the image’s features. Additionally, a pooling layer is being incorporated into the network’s design, which reduces the data to a single dimension. The neuron predicts support values for several attributes during the assessment stage. The approach then calculates the Class-Based Fitness value (CBFV) in order to categories the image against various tumour classes. The proposed GBP-CNN algorithm improves the performance of brain image classification with less time complexity.

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A Review of Different Approaches for Detecting Emotion from Text

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IOP Conference Series: Materials science and engineering 2021年第1期1110卷

作者： Ashritha R Murthy K M Anil Kumar Assistant Professor Department of Computer Science and Engineering Sri Jayachamarajendra College of Engineering JSS Science and Technology University Associate Professor Department of Computer Science and Engineering Sri Jayachamarajendra College of Engineering JSS Science and Technology University

Emotion detection and analysis is one of the challenging and emerging issues in the field of natural language processing (NLP). Detecting an individual's emotional state from textual data is an active area of study, along with identifying emotions from facial and audio records. The study of emotions can benefit from many applications in various fields, including neuroscience, data mining, psychology, human-computer interaction, e-learning, information filtering systems and cognitive science. The rich source of text available in the Social media, blogs, customer review, news articles can be a useful resource to explore various insights in text mining, including emotions. The purpose of this study is to provide a survey of existing approaches, models, datasets, lexicons, metrics and their limitations in the detection of emotions from the text useful for researchers in carrying out emotion detection activities.

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Predicting biochar yield from biomass pyrolysis: A comprehensive data-driven approach using machine learning and SHAP analysis

Results in Engineering

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Results in engineering 2025年 26卷

作者： Walid Abdelfattah Munthar Kadhim Abosaoda Krunal Vaghela Gowrishankar J Prabhat Kumar Sahu Kamred Udham Singh R. Sivaranjani Rohit Chauhan Siya Singla Samim Sherzod Department of Mathematics College of Science Northern Border University Arar Saudi Arabia College of Pharmacy The Islamic University Najaf Iraq College of Pharmacy The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Marwadi University Research Center Department of Computer Engineering Faculty of Engineering & Technology Marwadi University Rajkot Gujarat India Assistant Professor Department of Computer Science Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of Computer Science and Information Technology Siksha 'O' Anusandhan (Deemed to be University) Bhubaneswar Odisha 751030 India School of Computing Graphic Era Hill University Dehradun India Adjunct Professor Centre for Promotion of Research Graphic Era Deemed to be University Dehradun Uttarakhand 248002 India Department of CSE Raghu Engineering College Visakhapatnam Andhra Pradesh 531162 India Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges-Jhanjeri Mohali Punjab 140307 India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab 140401 India Faculty of Engineering Nangarhar University Nangarhar Afghanistan

Biochar, a pivotal product of biomass pyrolysis, is crucial in sustainable agriculture, carbon sequestration, and renewable energy systems. This study employs a comprehensive data-driven approach to predict biochar yield using machine learning algorithms. The dataset, comprising 14 chemical, physical, and reaction parameters collected from reputable studies, was processed using outlier detection via the Monte Carlo Outlier Detection (MCOD) algorithm and hyperparameter tuning to optimize model performance. Among the evaluated models, the Decision Tree algorithm emerged as the most robust predictor, achieving an R² value of 0.771, a mean square error (MSE) of 16.182, and an average absolute relative error percentage (AARE%) of 11.848 % in testing. The SHAP analysis identified residence time, pyrolysis temperature, and ash content as the most influential predictors of biochar yield. Simplified explanations and interpretability offered by these models support their practical application. These findings underline the potential of machine learning to optimize biochar production, enhance agricultural systems, and create scalable methods for sustainable waste management, directly contributing to renewable energy and environmental remediation industries.

关键词： Biochar yield Machine learning Biomass pyrolysis Decision tree SHAP analysis

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Design and Development of Future Estimate in Confronting Ransomware

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Journal of Physics: Conference Series 2021年第1期1717卷

作者： A. Balachandar S. Abdul Alsowdh K. Arumugam Assistant Professor Department of Computer Science and Engineering IFET College of EngineeringVillupuramIndia UG Scholar Department of Computer Science and Engineering IFET College of Engineering VillupuramIndia

Ransomware might be a one assortment of malignant PC code which is that the quickest spreading malware risk as they focusing on each kind of clients records result in brief or perpetual loss of touchy information issue to ordinary activities, cash misfortunes caused to resuscitate framework documents that partners potential hurt to an association. As a consequences of an essential information's unit encoded on the pc arrange by the programmers which they request the payment to pay a computerized cash style of a bitcoin inside a particular date, if installment isn't done no information are back to unique sort of mystery composing. The proposed framework demonstrates that gathering of bitcoin datasets are accustomed to anticipating the eventual fate of bitcoin rates then examination the ransomware, that investigation are utilized to discover the ransomware extends then the client can shutting the framework doors for keeping away from the trail technique for malware go into the framework. It shows the security measures for the framework from the ransomware prediction.

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IOP Conference Series: Materials science and engineering 2021年第1期1055卷

作者： Kusumalatha Karre Y Rama Devi Research Scholar Department of Computer Science and Engineering Osmania University and Assistant Professor Department of Electronics and Computer Engineering Sreenidhi Institute of Science and Technology Hyderabad Professor Department of Computer Science and Engineering Chaitanya Bharathi Institute of Technology Hyderabad

Autism is a mental condition which hinders social and communication skills. It's a lifelong disability which makes the child's day to day life very difficult. But in most of the cases early intervention has helped the children to develop the skills which are needed to the fullest to overcome autism. As early the intervention, better the development of the child. Most of the research has been carried out to detect the autism using various machine learning algorithms which consider autistic diagnostic tools such as ADI-R, ADOS or CARS. Once autism is detected, different areas which need to be developed are considered and recommendations are given to the child. In this paper, a system is proposed which uses multi dimensional data collected from facp, DST and Diet to perform analytics using machine learning and provide recommendations to the child.

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Modified Chorological Coded Data Compression Methodology for Wireless Sensor Networks

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Journal of Physics: Conference Series 2021年第1期1770卷

作者： S. Jancy Suji Helen Mercy Paul Selven A. Viji Amutha Mary M.D Antopraveena M.S. Roobini Assistant Professor Department of Computer Science and Engineering Sathyabama Institute of Science and Technology ChennaiIndia Associate Professor Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

Wireless Sensor Network (WSN) is a fast evolving current technology which is being employed in various applications. Despite its wide usage, WSNs have a few challenges to overcome to be called as an ideal technology. Some of the challenges are battery life time, memory storage and deployment issues. Batteries are the primary source of power supply to WSN and one of the major challenges is the energy constraint. This paper aims to propose a few techniques to better the energy efficiency of the sensor networks by saving sensor energy using data compression methodology. In this paper, a modified chorological coded data compression methodology is proposed (MCDC). This algorithm deals with assigning the sequence value to the given input information. If the assigned sequence value is a double digit number, it is converted in to single digit number. Double digit numbers and single digit numbers are combined. Separate location tables are generated for all double digit and single digit numbers. This procedure continues until all the sequence values are changed in to single digit number. Then the final single digit is assign with an equivalent Sequence Code (SC). MCDC algorithm is compared with DELTA compression and RUNLENGTH compression and a better compression ratio was achieved when compared with DELTA compression and RUNLENGTH compression algorithms.

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