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检索条件"任意字段=2022 IEEE International Conference on Current Development in Engineering and Technology, CCET 2022"

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An Automated Liver Disease Detection System using Machine Learning and Smart Contract

An Automated Liver Disease Detection System using Machine Le...

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2022 ieee international conference on current development in engineering and technology, ccet 2022

作者： Kaur, Jasleen Rani, Rinkle Kalra, Nidhi Thapar Institute of Engineering and Technology Computer Science and Engineering Department Patiala India

ISBN: (纸本)9781665454155

Liver disease is one of the leading reasons of death in both men and women worldwide. Blood pressure, Bilirubin level, Energy level, SGOT & SGPT level, blood sugar level, and Total Protein are all factors that can be observed to predict liver disease in its early stages. The existing healthcare infrastructure is being transformed by technology. With the development of the Internet of Things (IoT), doctors can now remotely monitor and analyze patients, keep their data, and process it for further examination. However, there is a requirement to introduce advanced and improved secured algorithms for quick event processing and detection. In this work, an integrated Smart Contract (SC) and Machine Learning (ML) based system is presented for detecting liver disease that, in addition to storing data in the blockchain, notifies the doctor about the patient's health status. blockchain technology provides a secure environment for storing confidential patient data. Since blockchain data is tamper-resistant, it effectively prevents unauthorized access and data falsification. Using the concept of ML, decision tree classifier variants, such as J48, Hoeffding Tree, Decision Stump, and Random Forest, are applied to the liver patient dataset for efficient classification in order to design the proposed system. The best classifier is chosen based on performance metrics like accuracy, precision, recall, F- score, etc. Finally, the selected classifier's rules are encoded in the smart contract to allow automatic and timely detection of disease. This work also discusses the proposed work's functioning and the evaluation results. © 2022 ieee.

关键词： Smart contract

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Intermittent Fault Diagnosis in Sensors using Artificial Neural Network: A Comparative Study

Intermittent Fault Diagnosis in Sensors using Artificial Neu...

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2022 ieee international conference on current development in engineering and technology, ccet 2022

作者： Mishra, Aruna Kumar Mohanty, Subrat Kumar Faculty of Engineering Rourkela India Department of Etc Engineering Bhubaneswar India

ISBN: (纸本)9781665454155

Processing algorithms and processing hardware have developed at a very rapid pace in the last three decades. Sensors play a vital role in the condition monitoring of various components of a manufacturing system and to measure different variables that may affect the performance of these systems. Real-time collection of data, processing of the collected data, and decision-making are challenges. Intermittent fault diagnosis is difficult as it occurs randomly and does not follow a pattern. Non-availability of a suitably labeled dataset for testing the algorithms is also a challenge. Keeping these things in mind in the present research fault-injected sensor signals are developed from the publicly available Intel Lab dataset for temperature and light sensor signals. With the help of the proposed algorithm, a Matlab code for intermittent fault injection is developed. The performances of the various machine-learning algorithms extensively used in literature are also compared with the help of accuracy, precision, recall, and F1 score values. The performances of the artificial neural networks are compared with SVM, Ensemble, and k-NN in classifying various intermittent fault modes using the classification learner application of Matlab. The trained bilayer neural network has achieved an F1 score of 0.89 which is the highest among the other tested machine learning algorithms. © 2022 ieee.

关键词： Neural networks

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Software development Framework for Cardiac Disease Prediction Using Machine Learning Applications

Software Development Framework for Cardiac Disease Predictio...

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2022 ieee international conference on current development in engineering and technology, ccet 2022

作者： Kanna, R. Kishore Devi, K. Yamuna Dhivy, A. Josephi Arocki Amutha, Priya. M Diana Gomalavalli, R. Ambikapathy, A. Vels Institute of Science Technology Advanced Studies Department of Biomedical Engineering Chennai India Srm Easwari Engineering College Department of Biomedical Engineering Chennai India Siddharth Institute of Engineering and Technology Department of Electronics and Communication Engineering Andhra Pradesh India Galgotias College of Engineering and Technology Department of Electrical and Electronics Engineering Uttar Pradesh India

ISBN: (纸本)9781665454155

The most difficult task in medicine is making a diagnosis of heart illness. Since the decision is dependent on a huge number of clinical and pathological information, the diagnosis of heart illness is challenging. This is such as resulted in a significant increase in interest among academics and medical professionals in accurate and efficient cardiac disease prediction. Since time is of the essence in cases of heart sickness, getting the appropriate diagnosis quickly is essential. Since heart disease is the leading cause of death globally, early detection of heart disease is crucial. With the proper case of training and testing, machine learning has recently emerged as one of the most advanced, trust worthy, and helpful technologies in the medical industry, offering the most assistance for sickness prediction. The main goal of this endeavor is to examine various heart disease prediction models and choose pertinent heart disease variables using a genetic approach. Genetically optimized prediction models outperform conventional prediction models in terms of performance. Analyzing heart disease using UCI datasets. The Cleveland database is the only one that ML researchers have used thus far. The patient's heart condition is indicated in the 'target' field. It is positioned in the target column and has an integer value between 0 (no presence) and 1 (presence). The goal is the dependent variable, while the other factors are the independent variables. © 2022 ieee.

关键词： Heart

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Performance Analysis of PV Fed DC-DC Converter for Simultaneous Buck-Boost Operation

Performance Analysis of PV Fed DC-DC Converter for Simultane...

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2022 ieee international conference on current development in engineering and technology, ccet 2022

作者： Tiwari, Kaushal Prasad Thakre, Kishor Tiwari, Prakash Narayan Rabindranath Tagore University Department of Electrical and Electronics Engineering Bhopal India

ISBN: (纸本)9781665454155

The increase penetration of Renewable Energy based Power sources (REPS) has led the emphasis on the use of DC power at the load terminals. The most REPS generate DC power and this DC power can be directly used by utilizing DC converters (DCC). Hence, DCC are trustworthy for accomplish the application of REPS. In this work a DCC is designed which is capable of bi-directional current flow as well as voltage profile with diversity. The DCC proposed can function in buck and boost mode simultaneously to meet the power and voltage ratings of various sources as well as applications. The proposed converter is energized via PV-source which supplies the DC power as per the requirement. The switching requirement of the proposed DCC is also very low with two counts. Moreover, switches can be operated with low voltage ratings which in-turn reduces the switching losses. Also, it offers negligible interference even at high switching frequencies. The proposed DC converter is designed for low as well as high power ratings voltage rating. Hence the presented DCC has various advantages in terms of voltage rating, operational modes as well as high efficiency when operated with DC various voltage ratings at the same time. © 2022 ieee.

关键词： Renewable energy resources

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AI Methodologies in Upcoming Modern Warfare Systems

AI Methodologies in Upcoming Modern Warfare Systems

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2022 ieee international conference on current development in engineering and technology, ccet 2022

作者： Jyothi, A.P. Shankar, Anirudh Narayan, Ashwath J R Bhavya, Kanyadara Reddy, Svv Sai Madhu Sudhan Yashwanth, A. Ramaiah University of Applied Sciences Faculty of Engineering &technology Dept. of Cse Karnataka Bengaluru India Ramaiah University of Applied Sciences Branch Ai &ml Faculty of Engineering &technology Dept. of Cse Karnataka Bengaluru India

ISBN: (纸本)9781665454155

Worldwide military influences are energetically at work to exhibit a mutual stand to take care of trials from the ocean bed to outer space, using meeting trouble making machinery, along with different tools enabled by AI. The combination of old war aids with AI expertise is pouring the development of an innovative theory of war based on rapid and precise choices, arrangements, and annihilation of the challenger's capability. In this paper, the authors discuss the state of AI algorithm development today and what the future holds, focusing on military applications in particular. The Tactical networks, in particular, have become essential for carrying out military operations specific behaviours or items of interest are a major difficulty for military networks. Innovative AI techniques deployment for improved performance in targeting and tracking concerning Military applications research is still lacking A robust solution to cybersecurity threats in the military with the help of AI has to be worked on with new research trends in AI. In the middle of conducting cyber military operations are the military focusing on the handling & comparing Rules of Engagement that set up the circumstances & captivities for engagement in each operation. Military Operations AI could help military forces by collecting, categorizing, and analyzing information more swiftly and resourcefully than currently possible. AI which has recently been introduced in military applications has been a game changer and had given military upfront to some countries. © 2022 ieee.

关键词： Neural networks

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Hog Features Based Handwritten Bengali Numerals Recognition Using SVM Classifier: A Comparison with Hopfield Implementation

Hog Features Based Handwritten Bengali Numerals Recognition ...

引用

2022 ieee international conference on current development in engineering and technology, ccet 2022

作者： Gahelot, Parul Sarangi, Pradeepta Kumar Saxena, Merry Jha, Jayant Vajpayee, Amit Sahoo, Ashok Kumar Chitkara University Institute of Engineering and Technology Chitkara University Karnataka India Group 42 Apex Institute of Technology Chandigarh University Karnataka Mohali India Graphic Era Hill University Dehradun India

ISBN: (纸本)9781665454155

Handwritten digit recognition (reading by computer) is a process that gives the technological capabilities to a system to recognize handwritten numerals using Machine Learning. It is very tough to recognize handwritten characters because of the non-similarity in characters by different people. Also, there exists no standard technique available in the industry which can be applicable to provide innovative solutions to all kinds of handwritten characters. Hence, there is a requirement to understand the nature of the script. In order to complete the task, there is a need to use unique extraction methods and a classifier. This work implements a Histogram of Oriented Gradient (HOG) features with Support Vector Machine (SVM) classifier and a Hopfield model to recognize handwritten Bengali numerals into the class of ten segments. SVM works in the way of mapping the input elements to a set of the high-dimensional feature vector. This works both in the case of linearly and nonlinearly and separable data. The experiments have been done using 1200 handwritten digits for 10 different classes (0 to 9). The overall accuracy (productivity) obtained from the implementation is 89.5%. The experimental outcomes from the Hopfield model give an accuracy of 95.5%. Also, it is found the Hopfield model is more capable of handling noisy inputs. © 2022 ieee.

关键词： Support vector machines

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AGC of Deregulated Electric Network using Slime Mould Optimization Search Strategy

AGC of Deregulated Electric Network using Slime Mould Optimi...

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2022 ieee international conference on current development in engineering and technology, ccet 2022

作者： Krishna, Ardhala Bala Sai, M Srinivasa Sesha Kamboj, Vikram Kumar Saxena, Sobhit Veerasamy, Moorthy Rao, D.S.N.M. Kkr & Ksr Institute of Technology and Sciences Department of Electrical and Electronics Engineering Andhra Pradesh India Kkr & Ksr Institute of Technology and Sciences Department of Information Technology Andhra Pradesh India Schulich School of Engineering University of Calgary Alberta Canada School of Electronics and Electrical Engineering Lovely Professional University Karnataka India Gokaraju Rangaraju Institute of Engineering & Technology Department of Electrical and Electronics Engineering Telangana India

ISBN: (纸本)9781665454155

Deregulated electric market is hot theme in the era of modern power utilities, due to the raising utilization of the grids. To regulate the deregulated electric network, a sophisticated optimization tactic is required. In the past decade, researchers have been exercised many optimizer, and find out the feasible solution. The solutions obtained until are not a global solution because these are non-dominated each other. Therefore, finding solution for the deregulated electric network by different optimization technique is not an end global solution. In this paper, a novel augmented Slime Mould Optimizer (SMO) is proposed to obtain the best solution for deregulated electric network. And SMO dependent PI controller was used to mitigate Automatic Generation Control (AGC) difficulties in a two-area hydro - thermal - gas electric network operating in a deregulated environment is emphasized in this research. Additionally, the dynamics of power pool company (POOLCO) strategy of GENCO's with DISCOs in the similar region are permitted to contribute to the power exchange for a modest load disturbance and the comparison findings are also shown to assess the effectiveness of the suggested tactic. The results of the simulations reveal that the SMO dependent PI controller outperforms the traditional AOA dependent PI controller. © 2022 ieee.

关键词： Molds

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Artificial Intelligence Based Virtual Machine Allocation and Migration Policy using Improved MBFD

Artificial Intelligence Based Virtual Machine Allocation and...

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2022 ieee international conference on current development in engineering and technology, ccet 2022

作者： Singh, Gurpreet Rani, Lekha Ghosh, Pinaki Goyal, Subhanshu Vajpayee, Amit Chitkara University Institute of Engineering and Technology Chitkara University Karnataka India School of Advanced Computing Sage University Bhopal India Marwadi University Rajkot India Apex Institute of Technology Chandigarh University Karnataka Mohali India

ISBN: (纸本)9781665454155

With rising demand and an increase in the number of servers in data centres, cloud computing is a new era of technology that is based on pay-per-use. As the number of servers rises, so do the virtual machines (VMs), which must be distributed among physical hosts to satisfy client demands. These VMs must be appropriately assigned;otherwise, their erroneous allocation increases energy consumption. This can be avoided by reallocating these VMs to a suitable physical host that can accommodate their needs. The mechanism for allocating and migrating virtual machines is presented in this work. With the aid of our suggested optimization technique, the uniqueness in the two primary virtualization processes- VM allocation and VM migration-was explained in this study. In terms of VM allocation and migration, the proposed study reduced energy consumption relative to earlier research. The proposed work is broken down into two main sections: 1. VM allocation using Improved MBFD. b. SVM-based allocation verification to cut down on false migrations. This article is divided into five sections: Section 1 introduces virtualization, VM allocation, and migration;A brief summary of the literature is presented in Section 2, while Section 3 provides a suggested allocation and migration policy using an improved MBFD;and Section 4 offers results and discussion, followed by a conclusion and discussion of the article's future directions. © 2022 ieee.

关键词： Virtual machine

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Detection, Location, and Classification of Fault Applying Artificial Neural Networks in Power System Transmission Line

Detection, Location, and Classification of Fault Applying Ar...

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2022 ieee international conference on current development in engineering and technology, ccet 2022

作者： Chowdary, K Yuneela Kumar, Sachin Chandigarh University Department of Electrical Engineering Karnataka Mohali India

ISBN: (纸本)9781665454155

Transmission lines transfer power from generating stations to substations and then to end customers in a power system. A reliable power system is a system with a robust fault location system that reduces downtime in case of any power failure. Therefore, today a technique is required which must be robust and accurate in finding the location of the fault. A technique for detecting the presence of a fault, and locating and classifying faults in transmission lines in the power system through an Artificial Neural Network (ANN) is presented in this paper. This technique through ANN is executed and tested in MATLAB on a 3-Bus power system. In this technique, a backpropagation algorithm is used in the feed-forward network. The Levenberg Marquardt optimization technique is selected. Linear regression and Mean Square Error (MSE) are the performance standard measures in MATLAB. Performance is the best when MSE is very low or close to zero and the Regression value (R) is 1. RMS values of currents, voltages of three phases, and zero-sequence voltage and current are taken as input in this method during fault and without fault. The Regression (R) value which measures the correlation between output and target is 1 and MSE is 1.462e-10 for the Neural Network created for detecting the presence of a fault in the line. ANN which locates and classifies the fault in the transmission line has the performance value R-0.9818 and MSE 0.16178. Thus, the performance parameters of both the ANN models have shown satisfactory and acceptable results. © 2022 ieee.

关键词： Mean square error

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Machine Learning Assisted Early Diagnosis of Skin Cancer

Machine Learning Assisted Early Diagnosis of Skin Cancer

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2022 ieee international conference on current development in engineering and technology, ccet 2022

作者： Gupta, Anish Gupta, Ruchika Kumar, Sandeep Zonal Head North - It Academics Education INurture Eduaction Solution Pvt. Ltd Bengaluru India Abes Business School Ghaziabad India Sharda School of Engineering and Technology U.P Greater Noida India

ISBN: (纸本)9781665454155

Cancer of the skin is the most prevalent form of the disease found anywhere in the world. It's often called melanoma, which means 'bad pigmentation' in Greek. Melanoma is a type of skin cancer that starts when pigmented cells (melanocytes) in the sun-exposed area of the body start to grow and multiply out of control. The more you expose yourself to ultraviolet light, including sunlight, tanning beds and tanning booths, the more likely you are to develop melanoma. Mortality rate in skin cancer is more as compared to other cancer disease which is also called Melanoma. However, the incidence of this disease is lower than those of other form of cancerous cell. It occurs in the skin typically, but may occurs rarely in other body parts like eye, mouth. The treatment for this type of cancer is surgery and still the survival rate is very low if it is at a late-stage diagnosis. Fatality can be reduced significantly if it is detected in early stages. The dermatologist may detect it early through the screening of images. The rate of accurate diagnosis by experts is found to be around only 80%. The purpose of this paper is to provide an explanation of the recent advancements that have been made in the diagnosis of melanomas, as well as an attempt to comprehend the utilisation of features analysis and machine learning techniques for the detection of melanomas, by compiling the results that have been produced by various methods utilising sensitivity and specificity. © 2022 ieee.

关键词： Computer aided diagnosis

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