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Genetic algorithm based approach for solving Roman {3}-domination problem

Genetic algorithm based approach for solving Roman {3}-domin...

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2023 International Conference on computer, Electronics and Electrical engineering and their Applications, IC2E3 2023

作者： Billore, Aditya Reddy, P. Venkata Subba National Institute of Technology Warangal Department of Computer Science and Engineering Warangal India

ISBN: (纸本)9798350338003

Different Roman domination variants have been proposed by researchers to provide better defense strategies under different constraints. A Roman {3}-Dominating Function (RTDF) h on an undirected, simple graph G is a mapping h : V (G) →{0,1,2,3} with two conditions that $\sum\nolimits_{v \in N(u)} h (v) \geq 3$, if h(u) = 0, and $\sum\nolimits_{v \in N(u)} h (v) \geq 2$, if h(u) = 1, where N(u) is the neighbors of vertex u in G. The weight of a RTDF h is the sum $h(V) = \sum\nolimits_{v \in V} h (v)$. Given a graph G, the problem of determining minimum weight of a RTDF of G (also known as Roman {3}-domination number) is known as the Roman {3}-domination problem (MRTDP) and is known to be NP-hard. However no efficient approximation algorithm has been proposed to solve MRTDP. Hence in this paper, we propose a genetic algorithm based approach for solving MRTDP in which four heuristic algorithms have been proposed and a problem specific crossover operator and feasibility function has been developed. Effectiveness of the proposed meta-heuristic algorithm is tested on the random graphs generated using NetworkX Erdős-Rényi model, a popular model for graph generation. Experimental results show that the proposed genetic algorithm for solving MRTDP gives a near optimal solution in reasonable time. © 2023 IEEE.

关键词： Genetic algorithms

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An Approach for Soil Fertility Assessment Using Geospatial Data and Environmental Covariates 5

An Approach for Soil Fertility Assessment Using Geospatial D...

引用

5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Mundada, Shyamal Jain, Pooa Department of Computer Science and Engineering Indian Institute of Information Technology Nagpur India

ISBN: (纸本)9798400709418

Agriculture is one of the most important industries for the country in terms of sustainability and the economy. Agriculture practises must be improved to preserve a sustainable balance because agriculture's importance and requirement are increasing along with the population. A vital part of the agricultural system is the soil. The accurate and thorough spatial soil information provided by soil composition detection can assist farmers in making decisions that will promote the growth of their crops. As a result, researchers concentrated on using AI and remote sensing to create models for the prediction of soil nutrient information that will aid farmers in exact fertilisation. A precise and economical solution for soil nutrient mapping is now available due to recent developments in sensor technology and machine learning-based soil analysis methods. Therefore, using machine learning (ML) techniques on remote sensing data can aid in developing a precision farming decision support system to increase crop output. For environmental modelling and risk assessment, complete and accurate geographic soil information is crucial. © 2023 ACM.

关键词： Soils

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Top-Performing Unifying Architecture for Network Intrusion Detection in SDN Using Fully Convolutional Network 5

Top-Performing Unifying Architecture for Network Intrusion D...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Roy, Bishwarup Acharya, Indranil Papalkar, Darshil Joseph, Mary Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350321425

Internet's omnipresent adoption over the course of many years leading up to the current era along with Wireless Sensor Networks' (WSN) limitations has only increased the security threats to these high-speed networks. As a result, it is critical to deploy Intrusion Detection Systems (IDS) to monitor malicious activities in the network framework. Software Defined Network (SDN) has served its purpose to a certain extent by enabling centralized control and management of WSN and providing ways to implement IDSs more efficiently. However, like any emerging technology, SDN also has its share of vulnerabilities as there exists a gap for contemporary architectures, which can exploit global network assets and balance network resource consumption while providing programmability in terms of security of the network. To tackle this problem, a model has been developed that uses a fitting feature selection method to cut down on the count of features extracted and minimize the data volume relayed over the routes. Conclusions drawn from the scheme's trials demonstrate the proposed IDS technology's potency and effectiveness. The openly accessible University of Nevada - Reno Intrusion Detection Dataset (UNR-IDD) was used to evaluate the intrusion detection algorithm involving the Fully Convolutional Network (FCN) architecture and its effect using a statistical technique in detecting different types of attacks. © 2023 IEEE.

关键词： Deep neural networks

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Multifarious Face Attendance System using Machine Learning and Deep Learning 7

Multifarious Face Attendance System using Machine Learning a...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Bittal, Vijaylaxmi Jagdale, Vishal Brahme, Akshay Deore, Devyani Shinde, Bhagyashri SVKM's Institute of Technology Department of Computer Engineering Maharashtra Dhule India

ISBN: (纸本)9798350397253

The process for recording attendance depends on a variety of variables including the quantity of participants, locations, events and time intervals where attendance must be collected. Recording and collecting attendance for an event or program is very much essential to keep track of the intended audience. For example, in schools and colleges roll call can be used to take attendance for the presenteeism in the classroom. Similarly, you can use this process for programs or events with a smaller number of participants. If participants have more attendance maintaining system is tedious process, if it is done manually. So, there is a need for an effective attendance system so that it can monitor the presents in all the ways. So here a multifarious face attendance system is implemented which involves machine learning and deep learning for face detection using MT-CNN and VGGFace2 large scale face recognition dataset model. The execution of this attendance system can be demonstrated for the educational institution. So, the problem of fraudulent attendance and proxies can be resolved by utilizing this technique. The main task of this system is to recognize multiple faces using VGGFace2 and map attendance based on it. So that thereby allowing us to analyze the attendees of the program or function. Following this, the discovered faces can be compared by cross-referencing with the student face database. The attendance and records of attendees can be effectively maintained with the help of this effective technique. © 2023 IEEE.

关键词： Face recognition

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Brain Tumor Classification using MR Images and Transfer Learning 2

Brain Tumor Classification using MR Images and Transfer Lear...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Gupta, Manu Shirisha, Gangadi Chandana, Aela Mani Ankitha, Belide Sreenidhi Institute of Science and Technology Department of Electronics and Computer Engineering Hyderabad India

ISBN: (纸本)9798350347579

A brain tumor is the irregular development of a mass tissue within the brain or close to it, which has the capacity to spread and duplicate the wildly affecting organs within the body. Brain tumor classification is a vital assignment to assess the tumors and make a treatment choice concurring with their classes. In this study, a model for the classification of brain tumors from MRI images utilizing transfer learning is developed. The convolution neural network (CNN), VGG16 and InceptionV3 techniques are applied to extract features and perform classification with brain MRI image dataset as input. The precision, accuracy, recall, and f1-score metrics are computed to evaluate the performance of the developed model. The results obtained show that the proposed pre-trained model VGG16 achieved better results than other models with an accuracy of 98%. © 2023 IEEE.

关键词： Magnetic resonance

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Machine Learning Algorithmsfor Diagnosis of Parkinson's disease Based on Voice Characteristics 2

Machine Learning Algorithmsfor Diagnosis of Parkinson's dise...

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2nd IEEE International Conference on Advances in Computing, Communication and Applied informatics, ACCAI 2023

作者： Manoj, Polina Sai Reddy, Kondreddy Sumanth Kumar Sambath, M. Thangakumar, J. Hindustan Institute of Technology and Science Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350315905

Bradykinesia, tremors, rigidity, and postural instability are symptoms of a degenerative neurological condition called Parkinson's disease (PD) affecting the motor movements. Deep brain stimulation, medication, and other therapies can all be used to treat theParkinson's disease symptoms, yet as of today, there is no effective treatment. Parkinson's disease must be recognized as early and precisely as feasible for effective disease treatment and the development of new therapies. The goal of this work is to create a model that can detect Parkinson's disease based on relevant clinical and demographic variables. It employs a number of machinelearning techniques, including Logistic Regression, Support Vector Machine (SVM), Gradient Boosting Classifier, K-Nearest Neighbors (KNN), Random Forest Classifier, and ensemble method, which is a voting classifier. A significant dataset that contains data on changes in speaking patterns was used to train the model. Using measures for accuracy, precision, recall, and F1-score, the machine learning model's performance is assessed and contrasted with that of the most widely used techniques for Parkinson's disease diagnosis. © 2023 IEEE.

关键词： Support vector machines

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Exploring the Performance of VGG16 and Efficient Net Models for Plant Disease Classification: A Comparative Approach 1

Exploring the Performance of VGG16 and Efficient Net Models ...

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1st International Conference on Cyber Physical Systems, Power Electronics and Electric Vehicles, ICPEEV 2023

作者： Sahithi, Boyapati Vigneshwari, S. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350323566

Crop production and food security are both seriously threatened by plant diseases. Convolutional neural networks (CNNs) have gained popularity as a potential tool for automating the process of identifying plant diseases thanks to the development of deep learning techniques. In this study, we investigate the use of the VGG16 and Efficient Net CNN architectures for plant disease classification and compare their results. The research uses a massive dataset that includes many types of plants and diseases. To prepare the dataset for further analysis, we add annotations to the photos and split the data into a training set, a validation set, and a test set. Using transfer learning and the ImageNet-obtained pre-trained weights, the VGG16 and Efficient Net models are trained on the same dataset. Accuracy is only one of the measures we use to assess the models' efficacy. In terms of plant disease categorization, both VGG16 and Efficient Net show very good accuracy in experiments. However, they perform in notably different ways. With a training accuracy of 96.06% and a Testing accuracy of 95.84%, Efficient Net surpasses VGG16, as shown by our comparison research. The Efficient Net model outperforms VGG16 in terms of accuracy while using many fewer parameters and being much simpler to run. These results suggest that Efficient Net is a better tool for identifying and classifying plant diseases. Finally, this paper presents a complete analysis contrasting the VGG16 model with the Efficient Net model for identifying plant diseases. The findings demonstrate that Efficient Net outperforms other similar methods and that this has practical implications for detecting plant diseases in outside environments. © 2023 IEEE.

关键词： Convolutional neural networks

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The Use of Recurrent Neural Networks in the Optimization of computer Science Algorithms 1

The Use of Recurrent Neural Networks in the Optimization of ...

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1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Franklin, Ramya G Ronald Doni, A. Poornima, D. Sabasti Prabu, S. Igni Sathyabama Institute of Science & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359756

A new era of computational efficacy and problem-solving abilities has begun with the combination of Recurrent Neural Networks (RNNs) and computer science methods. It is crucial in modern computing to combine RNNs with methods from the field of computer science. RNNs bring a dynamic learning paradigm, making algorithms more efficient and flexible by allowing them to process sequential input and complicated dependencies. Their usefulness goes beyond one specific area of study, and into others such as optimization issues, recommendation systems, image processing, and natural language processing. RNNs serve a vital role in addressing the severe challenges that algorithms confront, including processing sequential input, identifying temporal connections, and optimizing in high-dimensional environments. By providing dynamic and adaptive modeling, RNNs aid algorithms in navigating these obstacles. Recurrent Neural Network-Based Optimization Algorithm (RNN-OA) is an innovative novel approach presented in this research. RNN-OA is an approach to algorithmic optimization that makes use of RNNs' dynamic learning capability. Mechanisms for selective data processing with attention, regularization methods for model stability, and interpretability improvements for openness are all a part of it. Quickening the process of optimization in many different types of problems is transfer learning and fine-tuning approaches. There are a wide variety of computer science fields where RNN-OA can be useful. Time-series analysis, language modeling, picture recognition, and recommendation systems all benefit greatly from its ability to handle sequential data. The efficacy, scalability, and robustness of the method are thoroughly evaluated by simulating various problem scenarios and data streams. The results of these simulations provide illumination for the benefits and drawbacks of RNN-OA, paving the way for its development toward actual application in algorithmic optimization. © 2023 IEEE.

关键词： Recurrent neural networks

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Blockchain and DL-based Communicable Disease Spread Control Framework by Monitoring Quarantine Activities 20

Blockchain and DL-based Communicable Disease Spread Control ...

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20th IEEE India Council International Conference, INDICON 2023

作者： Shah, Harshit Shaha, Kanisha Jadav, Nilesh Kumar Gupta, Rajesh Tanwar, Sudeep Bodkhe, Umesh Nirma University Institute of Technology Department of Computer Science and Engineering Ahmedabad India

ISBN: (纸本)9798350305593

In the face of global health crises, ensuring compliance with quarantine regulations is paramount for effectively controlling the spread of infectious diseases. This paper presents a novel system for monitoring activities among quarantined patients using wearable devices, artificial intelligence (AI) models, and blockchain technology. Our system enables continuous monitoring of patients' activities and provides real-time predictions of their compliance status while securely storing and utilizing data on a blockchain. By leveraging wearable devices and employing AI techniques, our system offers an automated and efficient solution for quarantine enforcement. The system's effectiveness is demonstrated through its ability to accurately predict compliance and trigger necessary interventions when deviations from designated areas occur. Our approach is able to achieve a 98.92% accuracy score to predict the activity of the subject. We also demonstrate the effect of different optimizers and their performance. The proposed approach, enhanced by blockchain technology, holds promise for reducing manual efforts, enhancing healthcare workers' safety, and ensuring the integrity and privacy of patient data during the monitoring process. © 2023 IEEE.

关键词： Artificial Intelligence Blockchain Communicable diseases Healthcare systems Quarantine activities Wearable devices

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Deep Learning based Target detection and Recognition using YOLO V5 algorithms from UAVs surveillance feeds 2

Deep Learning based Target detection and Recognition using Y...

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2nd International Conference for Advancement in technology, ICONAT 2023

作者： Kumar, Sushil Kumar, C.R.S. Defense Institute of Advanced Technology Computer Science and Engineering Department Pune India

ISBN: (纸本)9781665475174

This study aims to show how the most recent object identification algorithms (YOLO V5 and YOLO V7) may be used to recognise targets in a real-world setting using surveillance feed collected by UAVs/*** Russia-Ukraine conflict provided UAV imagery that was gathered and processed to better understand how object identification might result in deducing vital inputs for rapid and timely decision making in a warlike *** on the video was performed using the best and most accurate models from YOLOV5 which was obtained from open-source UAVs *** of its superior OD capabilities, unmanned aerial vehicles (UAVs) are essential to the realisation of any robot's full *** the YOLO V5 model was trained for a greater total number of epochs to substantially improve the object class detection *** analysing the Confusion matrix and the performance hyperparameters of the algorithm, this study compiles the performance metrics to make conclusions and optimise outcomes. © 2023 IEEE.

关键词： Antennas

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