iosr-JCE   Volume-23 ~ Issue-5 ~ Sep. - Oct. 2021

Abstract: In this paper we have presented a new approach to grid computing for processing video and image using Remote Method Invocation (RMI). Our proposed grid consists of four components: Donor, Server, Client and Service. Donor allows server to use its resources so that server can provide services to the clients using the donors' resources. Donor registers itself to the server as a resource holder. Server receives request along with dataset from a client for a specific service. Server splits the dataset into smaller units and invites the registered donors for processing the smaller units. Each donor sends processed data to the server. Server compiles the processed data received from different donors and performs some basic operations on the processed data and replies the client with the result. Performance of the grid is evaluated in terms of time complexity.

Key Word: Grid computing, RMI, Callback RMI, RMI registry, object registry.

[1] Vladimir Silva, "Grid Computing for Developers", Charles River Media,Inc. ISBN 1-58450-424-2
[2] I. Foster, "What is the Grid? A Three Point Checklist", GRIDToday, July 20, 2002.
[3] M.L.Liu, "Distributed Computing PRINCIPLES and APPLICATIONS",
[4] Rajkumar Kettimuthu, William Allcock, Lee Liming, John-Paul Navarro and Ian Foster,. "GridCopy: Moving Data Fast on the Grid", Proceedings of the Fourth High Performance Grid Computing Workshop to be held in conjunction with International Parallel and Distributed Processing Symposium (IPDPS 2007), March, 2007
[5] I. Foster, C. Kesselman, J. Nick, S. Tuecke, "Grid Services for Distributed System Integration", . Computer, 35(6), 2002

Abstract: Most researches belief that the left and the right retina of an individual are different. However, in this research Legendre moments are used to show that there are some similarities between the left and right retinal images. Some parts of the whole retinal image are used in Legendre moment calculation. Most effective parts are defined and selected for Legendre moment calculation. In this approach Orthogonal Legendre moments are used for feature extraction and dimensionality reduction. Legendre moments can preserve almost all the information of the left and right retinal images in few coefficients. After extracting the Legendre features from different parts of the left and right retinal images, similarity measurement is carried out by calculating the cosine distances between the feature vectors of different parts in a left retina and the feature vectors of different parts in a right retina. Training and evaluation are performed on the publicly available datasets CHASE_DB1 and Messidor-2 respectively. The proposed method provides a high probability of finding similarities between the left and the right retinal images based on different parts.

Key Words: Legendre moments, retina, cosine similarity, biometric, part-based

[1] C. Simon and I. Golstein, "A New Scientific Method of Identification," New York State Journal of Medicine, vol. 35, no. 18, pp. 901–906, 1935.
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Abstract: Currently, we witness the severe spread of the pandemic of the new Corona virus, COVID-19, which causes dangerous symptoms to humans and animals, its complications may lead to death. Although convolutional neural networks (CNNs) is considered the current state of the art image classification technique, it needs massive computational cost for deployment and training. In this paper, we propose an improved hybrid classification approach for COVID-19 images by combining the strengths of CNNs (using a powerful architecture called Inception) to extract features and a swarm based feature selection algorithm (Marine Predators Algorithm) to select the most relevant features. A combination of fractional order and marine predator's algorithm (FO MPA) is considered integration among a robust tool in mathematics named fractional order calculus (FO). The proposed approach was evaluated on two public COVID-19 X-ray datasets which achieves both high performance......

Key Words: Machine Learning, Natural Language Processing, SVM, NB, Sentiment Analysis

[1]. Ge, X.-Y. et al. Isolation and characterization of a bat sars-like coronavirus that uses the ace2 receptor. Nature 503, 535–538 (2013).
[2]. Al-qaness, M. A., Ewees, A. A., Fan, H. & Abd El Aziz, M. Optimization method for forecasting confirmed cases of covid-19 in china. J. Clin. Med. 9, 674 (2020).
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[4]. Huang, P. et al. Use of chest ct in combination with negative rt-pcr assay for the 2019 novel coronavirus but high clinical suspicion. Radiology 295, 22–23 (2020).
[5]. Zhu, H., He, H., Xu, J., Fang, Q. & Wang, W. Medical image segmentation using fruit fly optimization and density peaks clustering. Comput. Math. Methods Med. https://doi.org/10.1155/2018/3052852 (2018)..

Abstract: Purpose-This research manuscript examines the dimension of machine learning methods for detecting crime and predicting future crimes using 2 data sets. ZeroR, Naive Bayes, Linear Regression, Boosted Trees, Decision Tree and K-Nearest are used for crime prediction on the basis of AUC values, K-Fold Cross Validation and F-measure metric. Design/methodology/approach- To accomplish the rationale, study uses 2 data sets; Auto Theft and Rape Victims. Both the data set is key-in WEKA (Waikato Environment for Knowledge Analysis, developed at the University, Waikato, New Zealand). AUC values, K-Fold Cross Validation and F-measure metric are used to predict the estimation.......

Key Words: Naive Bayes, Linear Regression, Boosted Trees, Decision Tree and K-Nearest, Crime Prediction, Artificial Languages and Crime detection applications

[1]. Predict Crime | Predictive Policing Software,‖ PredPol. [Online]. Available: http://www.predpol.com/. [Accessed: 22-Jun-2017].
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[3]. Chitrakar, C. Zhang, G. Warner, and X. Liao, ―Social Media Image Retrieval Using Distilled Convolutional Neural Network for Suspicious e- Crime and Terrorist Account Detection,‖ in Multimedia (ISM), 2016 IEEE International Symposium on, 2016, pp. 493–498.
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Abstract: Theincrea singuse of computersandotherelectronicGadgetsbyanindividualin theirdailylivehasmadearecommendationsystemanecessarycomponent.Itis because of the amount of information
on internet continues to expand dramatically. The Recommendation System (RS) is a system which collects information from users about their preferences for a collection of things and assists them in making judgments from among the available options. As evidencedbydiversecontribution,sincetherecentpast,alargevarietyofRSare growing in tandem with the rise of web-based information; these types of recommendation systems are being used in variety of sectors, ranging from simple to more sophisticated objects. Through this project, we aim to help users where they will get to.......

Key Words: content-based filtering, Collaborative filtering, Knowledge based filtering, Text summarization (Extractive summary), Recommendation system

[1]. Cleyton Souza, Jonathas Magalhaes, Priscylla Silva, Evandro Costa, Joseana Fechine: Improving a recommender system through integration of user profiles: A semantic approach CHI 2009, April 4 - 9, 2009, Boston, Massachusetts, USA.
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[3]. Ivan Jureta, St´ephane Faulkner, and Caroline Herssens: Knowledge- based recommendation: A survey April 2014, International journal of international information and technologies.
[4]. Mehndi Allahyari, Seyedamin Pouriyeh, Mehdi Assefi, SaeidSafaei, Elizabeth Trippe, Juan Gutierrez, Krys Kochut: Text summarization Techniques: A briefsurvey arXiv, July 2017.
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Abstract: Cancer is a condition in which cells in the body become uncontrolled. Lung cancer starts in the lungs and spreads to lymph nodes or other organs, such as the brain, inside the body. Cancer can potentially spread to the lungs from other organs. When cells of cancer travel from one organ to another, metastases are termed. In 2019 alone, America registered 228820 instances, 135720 of them. The current technique for the identification of lung cancer employs X-ray or CT scans to recognize the extra weight of the pulmonary flesh. The traditional method needs that you be diagnosed manually by your doctor, and that sometimes is a long process, and we want to build a manual process machine based on this project which will help you to diagnose your condition quickly. Various CT-Scan pictures were obtained from various hospitals and various techniques such as image segmentation and Gabor filter have been utilized in this research.

Key Words: CT Scan Image, irisation, segmentation, thresholding, watershed, Gabor filter

[1]. Introduction of lung cancer is available: https://en.wikipedia.org/wiki/Lung_cancer
[2]. Makaju, Suren, P. W. C. Prasad, Abeer Alsadoon, A. K. Singh, and A. Elchouemi. "Lung Cancer Detection using CT Scan Images." Procedia Computer Science 125 (2018): 107-114.
[3]. El-Regaily, Salsabil A., Mohammed A. Salem, Mohammed H. Abdel Aziz, and Mohammed I. Roushdy. "Survey of Computer Aided Detection Systems for Lung Cancer in Computed Tomography." Current Medical Imaging Reviews 14, no. 1 (2018): 3-18.
[4]. Kalaivani, S., Pramit Chatterjee, Shikhar Juyal, and Rishi Gupta. "Lung cancer detection using digital image processing and artificial neural networks." In Electronics, Communication and Aerospace Technology (ICECA), 2017 International conference of, vol. 2, IEEE, (2017):100-103.
[5]. Kumar, S. Logesh, M. Swathy, S. Sathish, J. Sivaraman, and M. Rajasekar. "Identification of lung cancer cell using watershed segmentation on CT images." Indian Journal of Science and Technology 9, (2016)

Abstract: The Human facial expressions transmit a great deal of information visually rather than verbally. In the field of human-machine interaction, facial expression recognition is critical. Many applications exist for automatic facial expression recognition systems, but not limited to human behavior understanding, diagnosis of mental illnesses, and synthetic human emotions. Facial expression identification by computer with a high recognition rate remains a difficult challenge. Geometry and appearance are two common techniques used in the literature for automatic FER systems. Pre-processing, face detection, feature extraction, and expression classification are the four phases of facial expression recognition. Convolutional Neural Networks are used to identify the core seven human emotions of the project: anger, disgust, fear, happiness, sad, surprise, and neutrality. FER-2013 dataset.....

Key Words: FER, CNN, Recognition, Emotion

[1]. Min Shi, Lijun Xu, Xiang Chen. "A Novel Facial Expression Intelligent Recognition Method Using Improved Conventional Neural Network." Digital Object Identifier 10.1109/ACCESS.2020.2982286.
[2]. Xiangjian Chen, Di Li, Pingxin Wang AND Xibei Yang. "A Deep Convolutional Neural Network with Fuzzy Rough Sets for FER." Digital Object Identifier 10.1109/ACCESS.2019.2960769.
[3]. Mariana-Iuliana Georgescu, Radu Tudor Ionescu, (Member, IEEE), and Marius POPESCU. "Local Learning with Deep and Handcrafted Features for Facial Expression Recognition." Digital Object Identifier 10.1109/ACCESS.2019.2917266.
[4]. Ji-Hae Kim, Byung-Gyu Kim, Partha Pratim Roy, Da-Mi Jeong, "Efficient Facial Expression Recognition Algorithm Based on Hierarchical Deep Neural Network Structure." Digital Object Identifier 10.1109/ACCESS.2019.2907327.
[5]. Grudin, Maxim A. "Face recognition systems." Pattern recognition 33.7 (2019): 1161-1177.

Abstract: The contribution of Machine Learning Algorithms in the field of education is very huge, especially in the prediction of student performance [15]. Many colleges are failing to address the problems that the students face in their academic performance as well as in their career. This paper aims at defining the right metric for the engineering student's success and defining the better statistical mechanisms in analyzing the performance of students. This also helps mentors to choose the appropriate way in success of their students. In technical education like engineering, most of the colleges won't give much of priority to the non-academic aspects, which is the main aspect in getting the desired results and provide the student satisfaction. The machine learning algorithms helps at defining.....

Key Words: Education Data Mining, Machine Learning Algorithms, Prediction

[1]. Ahmed, A.B.E.D. and Elaraby, "Data Mining: A prediction for Student's Performance Using Classification Method. World Journal of Computer Application and Technology", 2(2), pp.43-47, I.S., 2014.
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[5]. V. Kumar, "An Empirical Study of the Applications of Data Mining Techniques in Higher Education," International. J. Adv. Computer Science Appl., vol. 2, no. 3, pp. 80–84, 2011.

Abstract: The extensive use of computer-based technology in the health care industry has resulted in the accumulation of electronic data. The challenges faced by medical practitioners in the study of symptoms and early sickness detection stem from the substantial amount of data that has to be processed. Supervised machine learning (ML) algorithms have exhibited significant potential in surpassing traditional approaches for illness detection and aiding healthcare practitioners in promptly detecting high-risk conditions. The primary aim of this research is to discern trends in the diagnosis of illnesses through the use of several supervised machine learning models. This will be achieved by assessing performance metrics associated with these models.

Key Words: healthcare, machine learning

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