This research study investigates the potential design of autonomous robots for developing the domestic and industrial applications. The system underwent rigorous testing in both commercial and residential settings, de...
This research study investigates the potential design of autonomous robots for developing the domestic and industrial applications. The system underwent rigorous testing in both commercial and residential settings, demonstrating exceptional performance in navigation and achieving near-perfect scores in manipulation. Autonomous robots possess the capacity to enhance productivity, reduce expenses, and enhance safety in various scenarios, as emphasized in the literature. The report also outlines potential avenues for future research, which encompass the development of enhanced sensors and perceptual systems, collaborative systems, and advanced decision-making algorithms. In general, this study constitutes a crucial contribution to the expanding corpus of information pertaining to self-governing robots and will facilitate their prospective conception, advancement, and integration.
Early diagnosis of the disease helps the patients to get through the disease. Diagnosis of medical datasets are carried out by different machine learning approaches. In this paper, Velocity Enhanced Whale Optimization...
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ISBN:
(数字)9781665420761
ISBN:
(纸本)9781665420778
Early diagnosis of the disease helps the patients to get through the disease. Diagnosis of medical datasets are carried out by different machine learning approaches. In this paper, Velocity Enhanced Whale Optimization Algorithm (VEWOA) is taken as classification algorithm to predict three different varieties of medical datasets like Heart disease, Echocardiogram and Hepatitis and then compared with Whale Optimization Algorithm (WOA). The classification accuracy of VEWOA-NN is 97.2 percentage in heart disease dataset, 96 percentage in echo-cardiogram dataset and 92.5 percentage in Hepatitis dataset. The performance validation is done with the help of Root Mean Square Error (RMSE) and time.
Prediction based language models are considered as one of the major concepts in Natural Language processing which gains knowledge from unstructured text data. Extracting insights from sequential data such as biologica...
ISBN:
(数字)9781728141428
ISBN:
(纸本)9781728141435
Prediction based language models are considered as one of the major concepts in Natural Language processing which gains knowledge from unstructured text data. Extracting insights from sequential data such as biological sequences is an important problem in genomics, proteomics and classifying the secondary structures of protein, helps the researchers in aiding to understand protein functions. This is considered as one of the important preliminaries of Drug development. Traditional techniques such as sequential models, probabilistic techniques and statistical approaches were widely applied in structure prediction which extracts insights from sequence of amino acid. However, handheld feature extraction becomes a tedious task, which eventually leads to less accuracy. Our novel approach creates vectors using word embeddings which is assumed to consider contextual information of amino acids thereby improving the accuracy of secondary structure prediction approach. This is considered as an optimistic solution for secondary structure prediction problem. In this approach a variation of word embeddings - Continuous Bag of Words (CBOW) method is proposed which retains the sequential information of all amino acid in the protein chain. This vector is used as input features of Deep Neural Network classifier and class labels are classified into Helix, Sheet, Coil. We have tested this NLP based approach on GenBank dataset. The infrastructure required for this analysis was leveraged from Google Colab.
The article examines a mathematical model of the selecting phases process of traffic light facilities of the road network section. A Markov decision process with a finite number of actions and states is used as a math...
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ISBN:
(纸本)9781665451635
The article examines a mathematical model of the selecting phases process of traffic light facilities of the road network section. A Markov decision process with a finite number of actions and states is used as a mathematical model, and the minimization problem is reduced to the Multiagent Reinforcement Learning for Integrated Network (MARLIN) problem. A Q-learning algorithm was implemented and a series of computational experiments were conducted in the Anylogic simulation system for a real section of the Krasnoyarsk road network to study the model.
The visible appearance of the emotion state, personality, purpose, psychological feature activity and psychopathology of an individual is the Facial expression. This plays an outgoing role in social affairs. Automatic...
The visible appearance of the emotion state, personality, purpose, psychological feature activity and psychopathology of an individual is the Facial expression. This plays an outgoing role in social affairs. Automatic recognition of facial expressions will be a vital part of natural human-machine interfaces. It should even be applied in activity science and in clinical apply. Fellows in nurturing automatic facial features Recognition system must perform detection and placement of faces during a disordered scene, facial feature extraction and facial features classification. Emotion recognition by facial features utilizes a Deep Learning system, which is enforced victimization Convolution Neural Network (CNN). The CNN model of the project relies on LeNet design. Kaggle facial features dataset with seven facial features labels as fear, anger, happy, surprise, sad, neutral and disgust is employed during this project. The system achieved 60.37 accuracy.
A method for choosing an appropriate initial condition to simulate stochastic differential equations stably is proposed. We apply Lyapunov stability and asymptotic stability theories to show how to choose initial cond...
A method for choosing an appropriate initial condition to simulate stochastic differential equations stably is proposed. We apply Lyapunov stability and asymptotic stability theories to show how to choose initial conditions without causing blow up of numerical simulations, and carry out specific analysis through the stochastic nonlinear Kubo oscillator.
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. ...
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.
作者：
Ananthi Claral Mary T.Arul Leena Rose P. J.Armstrong Doss D.1Research Scholar
Associate Professor Department of Computer Science College of Science & Humanities SRM Institute of Science & Technology Chengalpattu India 2Research Scholar
Associate Professor Department of Computer Science College of Science & Humanities SRM Institute of Science & Technology Chengalpattu India 3Head
Department of Business Administration Madras Christian College Chennai India
Online learning through cloud platforms takes place on the cloud – a virtual space that is not tied to any computer. The cloud-based learning management systems bring vast benefits at all educational levels. These sy...
Online learning through cloud platforms takes place on the cloud – a virtual space that is not tied to any computer. The cloud-based learning management systems bring vast benefits at all educational levels. These systems provide a powerful teaching tool. More educators are adopting them as their main learning management system, and students find them very intuitive to use. An online learning system based on cloud computing infrastructure is possible and it can significantly progress the effectiveness of investment that can make this system into a righteous path and accomplish a win-win situation for students and teachers. This online study comprises of several segments, namely, demographic details of the students, their usage of smart devices, device ownership, device connectivity and number of hours the device is connected in online. It consists of approximately 360 respondents. The statistical analysis was performed using R programming. The results indicated that most of the respondents utilize smartphones to access online classes through cloud platform. During pandemic circumstances the smart devices facilitated faculties and students of higher education sectors to teach and learn their subjects.
Cloud computing has become an undeniably famous assistance for its adaptability and versatility, which rouses numerous associations, foundations and organizations to want to re-appropriate data administrations to clou...
Cloud computing has become an undeniably famous assistance for its adaptability and versatility, which rouses numerous associations, foundations and organizations to want to re-appropriate data administrations to cloud stage. Simultaneously, much consideration has been paid to adapt to the extraordinary security and protection issues in recloud cloud. The general methodology is to scramble data by the data owner (DO) before re-appropriating; the approved query user play out an unpredictable arrangement of encryption and decoding tasks during question execution. In any case, the above plans have accepted that the question clients are completely trusted and have the entrance to the key for scrambling and unscrambling recloud data. It will achieve a few issues in reality. So we propose a novel plan for secure KNN inquiry on encoded cloud data with various keys, in which the data owner and each question client all hold their own various keys, and don't impart them to one another; in the interim, the data owner scrambles and decodes re-appropriated data utilizing the key of his own. Our plan is built by set of conventions to jelly the data classification and question over scrambled cloud data without key-sharing.
The treatment of degenerative spinal disorders requires an understanding of the individual spinal anatomy and curvature in 3D. An upright spinal pose (i.e. standing) under natural weight bearing is crucial for such bi...
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