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检索条件"任意字段=International Conference on Computing in Engineering and Technology, ICCET 2019"

共 12616 条记录，以下是91-100 订阅

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Revolutionizing Healthcare with Data Science: Early Disease Identification and Prediction System 8

Revolutionizing Healthcare with Data Science: Early Disease ...

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8th international conference on computing in engineering and technology, iccet 2023

作者： Tripty Kashyap, Aishani Sambhav, Saurabh Amity School of Engineering & Technology Amity University Patna801503 India

ISBN: (纸本)9781839539237

This study can be helpful in monitoring the health of a person and also in taking required precautions if needed. Also through the developed user interface (Ul), the patient may accurately predict their illness. With the help of Machine learning, we have created a prediction-based system that can help in the early identification of diseases in order to save many lives. We have considered diseases related to the heart and brain as of now but in the future, many more can be added. The medical industry produces vastamount of patient data, which isthen processed inavariety of ways. Asa result, we were able to create a prediction system that can diagnose numerous diseases at once using machine learning. This research is utilizing huge data resources where predictive analytics in healthcare can help practitioners make prompt decisions about the health and treatment of patients. The diseases related to the heart, brain, blood, etc. could potentially threaten mankind if not found at the early stages. So, the early identification of such types of diseases can help in saving a lot of lives. © The Institution of engineering & technology 2023.

关键词： Machine learning

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A Crop Recommendation System for Better Sustainability Using Machine Learning Techniques 8

A Crop Recommendation System for Better Sustainability Using...

引用

8th international conference on computing in engineering and technology, iccet 2023

作者： Kumari, Harsha Sambhav, Saurabh Amity School of Engineering & Technology Amity University Patna801503 India

ISBN: (纸本)9781839539237

In this study, machine learning techniques are proposed to be used to forecast the ideal crop for a specific plot of land using soil factors. There are two models used in this system: Light Gradient Boosting Model and Logistic Regression Model. The two proposed models are important to draw out a comparison about which model is more suitable and accurate for the crop recommendation system. The system uses a significant dataset that was specially made for the crop recommendation system. The dataset contains vital environmental and soil factors that are critical for crop development and growth. The machine learning models were trained and tested using this dataset as the base. The classification report generated for a thorough evaluation of the model's performance offers insights into numerous assessment parameters for each crop type, including precision, Fl-score, recall, and support. The model is connected to a Gradio interface, where farmers may enter location-specific information and get recommendations for the best crop. To fulfill the rising demand for food, farmers are always looking for ways to increase crop yields. The study through datasets and graphs is discussed in the later part of the paper. © The Institution of engineering & technology 2023.

关键词： Crops

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AUTOMATIC EVALUATION OF ANSWER SCRIPTS USING COMPUTATIONAL MODELS 8

AUTOMATIC EVALUATION OF ANSWER SCRIPTS USING COMPUTATIONAL M...

引用

8th international conference on computing in engineering and technology, iccet 2023

作者： Sibi, Navya Phuse, Vedant Patil, Snehal Bidkar, Devdatta Bhurchandi, K.M. Electronics and Communication Engineering Department Visvesvaraya National Institute of Technology Nagpur India

ISBN: (纸本)9781839539237

Answer script evaluation is an important part of assessing students' performance. Typically, an answer script evaluation is done manually that sometimes can be biased, time-consuming and inconsistent. The goal of this work is to introduce a system that computationally evaluates the subjective answers and electronic circuits from the examinee and hence reduces the time and effort of human intervention, as well as makes the evaluation procedure impartial. Our experiment consists of keyword extraction from the answer script, evaluating the order of words and sentences, and measuring various similarities between the input answers and stored correct answer models. Automatic evaluation of answer scripts has been found very useful in our experiments, and often the assigned marks are the same as manually scored marks. Since not all the answers in the exam can be described with words, we have focused on the electronic components and circuits, and also created a model that reads the hand-drawn circuits, and updates those to their digital representation, for evaluation of the related circuit diagrams for the question. The results from the evaluation have an accuracy of 93% are encouraging and consistent with respect to human evaluation. © The Institution of engineering & technology 2023.

关键词： Petroleum reservoir evaluation

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Effective Scheduling and Nature Inspired Hybrid Load Balancing in VANETs 8

Effective Scheduling and Nature Inspired Hybrid Load Balanci...

引用

8th international conference on computing in engineering and technology, iccet 2023

作者： Mehta, Tejasvi Mahato, Dharmendra Prasad Department of Computer Science and Engineering National Institute of Technology Hamirpur India

ISBN: (纸本)9781839539237

Vehicular Fog Networks (VFNs) have emerged as a promising solution for enabling high-bandwidth, low-latency communication between vehicles and infrastructure. However, the limited resources of Roadside Units (RSUs) pose a significant challenge to the efficient and effective scheduling of network traffic. Incoming requests are classified by the proposed architecture as Traditional requests (requiring low computation power) or Smart requests (requiring high computation power). This paper proposes an inter-RSU scheduling approach for Traditional requests and a load-balancing mechanism for Smart Requests to enhance the network performance of VFNs. The proposed approach uses a scheduling algorithm to schedule Traditional requests among RSUs so that energy consumption at the RSUs is uniform and minimal. A Hybrid algorithm is used that integrates the benefits of Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC) optimization-based techniques to achieve effective load balancing in VANETs fog servers. Load balancing reduces the queue waiting time in fog servers. The simulation results display that our energy-efficient scheduling algorithm reduces energy consumption ascompared to other scheduling algorithms and the Hybrid load-balancingalgorithmreducesthequeuewaiting time as compared to other load-balancing techniques. © The Institution of engineering & technology 2023.

关键词： Fog

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Metaheuristic-Based PID Controller Approach for Solar Tracking 8

Metaheuristic-Based PID Controller Approach for Solar Tracki...

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8th international conference on computing in engineering and technology, iccet 2023

作者： Verma, Rohit Singh, Amit K. Nagla, Kuldeep S. Department of Instrumentation and Control Engineering Dr BR Ambedkar National Institute of Engineering Jalandhar India

ISBN: (纸本)9781839539237

With modernization and industrialization, the demand-supply gap for energy is constantly growing. This scenario has led to the development of renewable energy sources, which are clean, safe and boundless. Among renewable resources, the solar energy is considered to be the best option for energy harvesting schemes because of its abundance availability. However, the full consumption of solar energy using single axis sun tracking system is still a challenging task. Hence this paper focuses on the development of new metaheuristic based dual-axis solar tracking system which can accurately track the sun with respect to azimuthal and tilt angle. The movement of solar tracker DC motor is precisely controlled by the optimized Proportional-Integral-Derivative (PID) controller. For the optimum tuning of PID parameters, three metaheuristic algorithms named Whale optimization algorithm (WOA), Ant colony optimization (ACO), and Genetic algorithm (GA) have been employed. The simulation results shows that the proposed system performs better with WOA as compared to ACO, and GA, as it demonstrates a faster convergence rate and better time domain specifications. © The Institution of engineering & technology 2023.

关键词： Three term control systems

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Ultrasound based Breast Cancer Recognition using Mask RCNN 8

Ultrasound based Breast Cancer Recognition using Mask RCNN

引用

8th international conference on computing in engineering and technology, iccet 2023

作者： Kumar, Pradeep Sumandar, K.Venkata Das, G.Victor Swaroop Reddy, G.Kavya Srivastava, Subodh Kuamar, Abhinav Department of Electronics and Communication Engineering VNRVJIET Pragathi Nagar Hyderabad500090 India Department of Electronics and Communication Engineering NIT Patna Patna University Campus Patna800005 India

ISBN: (纸本)9781839539237

As breast cancer is the leading cause of death in women globally, effective treatment isdependent on early identification.. This paper presents a design for the Computer-Aided Diagnostic (CAD) system of Ultrasound based Breast Cancer Recognition using mask regional convolutional neural network (Mask RCNN). The dataset used in theproposed model consists of three classes benign, malignant, and normal. The paperincludes a detailed description of the dataset used, implementation of the proposedmodel, and performance evaluation of the model. To get better efficacy, the dataset ispre-processed. The results of the experiment show that the proposed method detectsbreast cancer in all three classes, i.e., benign, malignant, and normal with an averageaccuracy of 97.93%. The annotated dataset used in this paper can also be used forfuture research in this area. Overall, the proposed method provides a promisingsolution for early breast cancer detection and may help improve patient outcomes. © The Institution of engineering & technology 2023.

关键词： Diseases

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Exploring the Potential of Proximity Coupled Stacked Patches for Millimeter-Wave Applications

Exploring the Potential of Proximity Coupled Stacked Patches...

引用

2024 international conference on engineering and computing, ICECT 2024

作者： Khalil, Wesam Nadeem, Kiran Ayaz, Muhammad Server Security Integration Group Intel Corporation HillsboroOR United States COMSATS University Department of Electrical and Computer Engineering Islamabad Pakistan University of Engineering and Technology Department of Electronics Engineering Abbottabad Campus Pakistan

ISBN: (纸本)9798350349719

This article investigates proximity-coupled stacked patches for millimeter-wave applications ranging from 76 GHz to 81 GHz. The proposed three-dimensional architecture includes aperture feeding, three substrates, two Roha cells, aperture ground, and two patches. Its full wave simulation model is implemented and optimized in Computer Simulation technology (CST) Microwave Studio Suit 2019 for the visual 3D results in the mentioned frequency range. Initially, the design is tested for the reflection coefficients for mmWave applications. The proposed proximity-coupled stacked patches exhibited remarkable efficiency greater than 85% and realized a higher gain of approximately 12 dB at all frequencies. These results demonstrate the validation and optimization of the suggested design, paving the path for its potential applications in real-world millimeter-wave technology. © 2024 IEEE.

关键词： Millimeter waves

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A Swarm intelligence approach for statistical modeling of wind speed and direction: A case study of new york bight

A Swarm intelligence approach for statistical modeling of wi...

引用

ASCE international conference on computing in Civil engineering 2023: Data, Sensing, and Analytics, i3CE 2023

作者： Salami Pargoo, Navid Amini, Erfan Zadeh, Mahshid Mohammad Hajj, Muhammad Dept. of Civil Environmental and Ocean Engineering Stevens Institute of Technology HobokenNJ United States Dept. of Civil and Environmental Engineering Colorado State Univ Fort CollinsCO United States

ISBN: (数字)9780784485224

ISBN: (纸本)9780784485224

We present statistical modeling of wind speed and direction in the New York Bight region using a Weibull distribution for wind speed and a mixture of von Mises distributions for wind direction. The historic wind data for the years 2019-2021 with a 6-min sampling frequency are divided into two half-yearly intervals based on wind power, representing different seasonal behaviors. The parameters of von Mises distributions are estimated using four different metaheuristic optimization algorithms. The suitability of the distributions is judged based on the Pearson correlation coefficient, which indicates good fits for the proposed models. The results demonstrate the flexibility of the proposed models in representing the probability density function of wind direction regimes in zones with several modes or prevailing wind directions. This research provides valuable insights into the evaluation process of wind energy resources of the New York/New Jersey Bight region, including wind farm layout optimization. © international conference on computing in Civil engineering *** rights reserved.

关键词： Wind power

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INCORPORATING CONVOLUTIONAL NEURAL NETWORKS INTO COMPUTED TOMOGRAPHY FOR DETECTING LUNG CANCER 8

INCORPORATING CONVOLUTIONAL NEURAL NETWORKS INTO COMPUTED TO...

引用

8th international conference on computing in engineering and technology, iccet 2023

作者： Jagadeesh, S. Reddy, K. Sivanagi Sony, D. Nandini, K. Rukmini, M. Department Electronics and Communication Engineering India Sridevi Women's Engineering College Telangana India

ISBN: (纸本)9781839539237

The mortality rate from lung cancer is among the highest of all malignancies. Early diagnosis and treatment are crucial to a full recovery, and their importance cannot be overemphasised. Cancer develops when cells proliferate out of control due to a genetic mutation. Lung cancer is a kind of cancer that first appears in the lungs. In addition, there are The Artificial Neural Network (ANN) is one such techniquethathasendured;however, itandotherapproacheslikeSVM, K-Means, and K-Nearest Neighbours (KNN), all machine learning techniques that use a support vector to predict an outcome, also have their limitations. We hypothesised that our method, which uses image processing and CNN to enhance detection accuracy and performance, would overcome these restrictions. The speed and precision with which Convolutional Neural Networks (CNN) classify lung cancer kinds is crucial for establishing patients' prognoses, eligibility for the best treatment regimen, and their success rate. Techniques for image processing can greatly improve manual analysis and diagnosis systems. © The Institution of engineering & technology 2023.

关键词： Diseases

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Enhanced Phishing URL Detection Using Hybrid Feature-Based Machine Learning Method 8

Enhanced Phishing URL Detection Using Hybrid Feature-Based M...

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8th international conference on computing in engineering and technology, iccet 2023

作者： Kumar, Joney Information Technology Meerut Institute of Engineering & Technology Meerut India

ISBN: (纸本)9781839539237

The rapid growth of the internet has led to increased usage of e-commerce, but it hasalso attracted cybercriminals who seek to steal personal information through variousmeans, including phishing schemes. In these schemes, individuals are tricked intodisclosing sensitive information through fake URLs. Due to the semantics-based attackstrategy used in phishing, it can be difficult to distinguish between legitimate andphishing URLs, taking advantage of computer users' vulnerabilities. Although softwarecompanies offer anti-phishing systems that utilize blacklists, heuristics, visuals, andmachine learning, they cannot prevent all phishing attempts. To address this issue, thisresearch proposes five classification methods that use hybrid features, includingnatural language processing (NLP) and principal component analysis (PCA). Thestudy finds that the Random Forest algorithm and XGboost utilizing NLP and wordvector features outperform their competitors, achieving a 99.5% accuracy rate inclassifying phishing URLs. These findings are significant as they demonstrate thepotential of machine learning techniques, such as NLP and PCA, in improving theaccuracy of anti-phishing systems. By incorporating these techniques into theclassification process, it is possible to enhance the ability of such systems todifferentiate between legitimate and phishing URLs. This can help in preventingcybercriminals from stealing sensitive information and provide a safer onlineexperience for users. © The Institution of engineering & technology 2023.

关键词： Principal component analysis

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