International Journal of Computer Science and Applications最新文献

{"title":"MultiScale Object Detection in Remote Sensing Images using Deep Learning","authors":"C. Radhika","doi":"10.37624/jcsa/15.1.2023.11-19","DOIUrl":"https://doi.org/10.37624/jcsa/15.1.2023.11-19","url":null,"abstract":"Abstract: With a rapid development in aerial technology, applications of Remote Sensing Images (RSI) have become more diverse. Remote sensing object detection is a difficult task due to complicated background, variations in the scales of the objects and proximity between objects of same scale. RSI’s are commonly captured from satellites with wide views, which leads to largescale images. The proposed model detects the objects at different scales. Feature Extraction and providing additional information about the object is done using Residual Neural Network101 (ResNet101) and ZFNet. Further, single scale and multiscale object detection is implemented using You Only Look Once (YOLOV5) and Faster Region based Convolutional Neural Network (Faster RCNN). A comparative study is done on all these techniques to evaluate the performance measures like Mean Average Precision and Accuracy.","PeriodicalId":39465,"journal":{"name":"International Journal of Computer Science and Applications","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78898473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of Personality Traits and Suitable Job through an Intelligent Interview Agent using Machine Learning","authors":"Ch. Mandakini","doi":"10.37624/jcsa/15.1.2023.21-31","DOIUrl":"https://doi.org/10.37624/jcsa/15.1.2023.21-31","url":null,"abstract":"Abstract: Interviews play a crucial role in an individual’s career. They are often a means through which recruitments are finalized in various companies. To effectively understand the suitability of the candidate for a particular job, the interviewer not only assesses the conceptual knowledge of the candidate but also tries to identify if the personality traits of the prospect match with the job requirements. Facial expressions are crucial in human communication since they assist in understanding others better and are commonly used to assess personality. The automation ensures that the procedure for selecting candidates in an objective manner is not tainted by the interviewer's bias and personal experiences. The proposed Intelligent Interview Agent uses video input of the interviewee to predict the Big Five Personality traits as seen by skilled human resource experts. To achieve this, the system uses VGG16 Convolutional Neural Network (CNN) Model. The system also predicts the suitable job role for the candidate depending on the scores predicted for the Big Five personality traits, by employing a machine learning (ML) model. The system serves the purpose of both the recruiter and the candidate. The recruiter can analyse the candidate’s personality traits and assign him/her the predicted suitable job. On the other hand, the candidate can get an idea of his/her personality traits and know which profession suits the best.","PeriodicalId":39465,"journal":{"name":"International Journal of Computer Science and Applications","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75076377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of Mental Health Instability using Machine Learning and Deep Learning Algorithms","authors":"Ch. Radhika","doi":"10.37624/jcsa/15.1.2023.47-58","DOIUrl":"https://doi.org/10.37624/jcsa/15.1.2023.47-58","url":null,"abstract":"Abstract: One’s mental health instability can hinder the individual’s life that leads to various health issues, like depression and anxiety that in turn results in mental imbalance or severe psychological instability. This psychological instability can lead to bipolar disorder. There are various reasons affecting one’s mental well-being, the reasons can either be modifiable or nonmodifiable. Bipolar disorder causes changes in a person's mood and energy. People will experience intense emotional states because of disorder. Proper diagnosis and treatment is required for the people with this disorder which lead to healthy and active lives. Determination of this psychological instability can be predicted using machine learning and deep learning algorithms and the accuracies will be compared for the same. The dataset used is a survey based real time dataset which identifies the everyday activities and conditions of various individuals. The survey questionnaire consists of various questions determining the stress and psychological feelings among the individuals. This dataset is used in training the models to determine the prevalence of any psychological instability. Comparison of various bipolar classification methods with their performance accuracy against the real- time dataset is done. Detection of psychological instability plays a key role in reducing the risk of severity","PeriodicalId":39465,"journal":{"name":"International Journal of Computer Science and Applications","volume":"299 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74061561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"People Counting and Tracking System in Real-Time Using Deep Learning Techniques","authors":"Dr. O. Obulesu","doi":"10.37624/jcsa/15.1.2023.33-46","DOIUrl":"https://doi.org/10.37624/jcsa/15.1.2023.33-46","url":null,"abstract":"Abstract: The aim of project is to automatically estimate the number of people at indoor and outdoor places. People counting systems can be used in retail environment such as determining conversion ratio, advertising and promotional evaluation. This system can be used for transportation management system and video surveillance. The number of customers is indispensable data for management and decision making in public places like large-scale markets, shopping centers, airports, stations, museums, laboratories, classrooms, cafeteria etc. In this system, firstly extract the frames from the video, then draw a desired reference line on the input frame, detect the people using MobileNet-SSD object detection model, mark the centroid on the detected person, track the movement of that marked centroid and calculate the direction of centroid movement whether it is moving upwards or downwards. If the centroid movement is downward direction, then increment in counter, else if the centroid movement is upward direction, then increment out counter. People counting is essential for retailers of any size, but it's especially important for small businesses that don’t have the benefit of assaying data from multitudinous locales whenmaking pivotal opinions. When used intelligently, people counting can shape businesses in multitudinous ways other than just furnishing information on nethermost business.","PeriodicalId":39465,"journal":{"name":"International Journal of Computer Science and Applications","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89011540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Covid-19 Chest X-ray Images: Lung Segmentation and Diagnosis using Neural Networks","authors":"A. Zhang","doi":"10.5121/ijcsa.2020.10501","DOIUrl":"https://doi.org/10.5121/ijcsa.2020.10501","url":null,"abstract":"COVID-19 has caused world-wide disturbances and the machine learning community has been finding ways to combat the disease. Applications of neural networks in image processing tasks allow COVID-19 Chest X-ray images to be meaningfully processed. In this study, the V7 Darwin COVID-19 Chest X-ray Dataset is used to train a U-Net based network that performs lung-region segmentation and a convolutional neural network that performs diagnosis on Chest X-ray images. This dataset is larger than most of the datasets used to develop existing COVID-19 related neural networks. The lung segmentation network achieved an accuracy of 0.9697 on the training set and an accuracy of 0.9575, an Intersectionover-union of 0.8666, and a dice coefficient of 0.9273 on the validation set. The diagnosis network achieved an accuracy of 0.9620 on the training set and an accuracy of 0.9666 and AUC of 0.985 on the validation set.","PeriodicalId":39465,"journal":{"name":"International Journal of Computer Science and Applications","volume":"12 1","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74220460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Opti2I and ε-precis Methods Selection Algorithm","authors":"Nombre Claude Issa, Brou Konan Marcellin, Kimou Kouadio Prosper","doi":"10.5121/ijcsa.2020.10401","DOIUrl":"https://doi.org/10.5121/ijcsa.2020.10401","url":null,"abstract":"Since the reference algorithm APRIORI [AGR97], other algorithms for optimizing the extraction of association rules have been developed. But no method is generally better than the others. This article deals with the optimization of closed itemsets in the context of highly correlated data. The work in this article responds to one of the perspectives of our article entitled \"A new approach to optimizing the extraction of frequent 2-itemsets\". In this previous article, we had obtained interesting optimization results from the 2-itemsets on a context of extraction of scattered data (weakly correlated data). The present article allowed us to obtain interesting results of the 2-itemsets on dense data (strongly correlated). Our approach was inspired by the research work of {Pas00, CB02, BBR03, CF14]. It has improved the extraction of a concise number of association rules by introducing a margin of error defined by the parameter  in the formula δ δ Ferm (S)-δ <ε (δ an integer, δ>0, Ferm (S) is the -Closure of the","PeriodicalId":39465,"journal":{"name":"International Journal of Computer Science and Applications","volume":"149 1","pages":"1-15"},"PeriodicalIF":0.0,"publicationDate":"2020-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76003538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Video Segmentation & Summarization Using Modified Genetic Algorithm","authors":"S. PrashanthaH","doi":"10.5121/ijcsa.2018.8501","DOIUrl":"https://doi.org/10.5121/ijcsa.2018.8501","url":null,"abstract":"Video summarization of the segmented video is an essential process for video thumbnails, video surveillance and video downloading. Summarization deals with extracting few frames from each scene and creating a summary video which explains all course of action of full video with in short duration of time. The proposed research work discusses about the segmentation and summarization of the frames. A genetic algorithm (GA) for segmentation and summarization is required to view the highlight of an event by selecting few important frames required. The GA is modified to select only key frames for summarization and the comparison of modified GA is done with the GA.","PeriodicalId":39465,"journal":{"name":"International Journal of Computer Science and Applications","volume":"1 1","pages":"01-09"},"PeriodicalIF":0.0,"publicationDate":"2018-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77661116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Relationship between product based loyalty and clustering based on supermarket visit and spending patterns","authors":"Alberto Bullo, Eliana Stavrou, S. Stavrou","doi":"10.22619/IJCSA.2017.100116","DOIUrl":"https://doi.org/10.22619/IJCSA.2017.100116","url":null,"abstract":"Loyalty of customers to a supermarket can be measured in a variety of ways. If a customer tends to buy from certain categories of products, it is likely that the customer is loyal to the supermarket. Another indication of loyalty is based on the tendency of customers to visit the supermarket over a number of weeks. Regular visitors and spenders are more likely to be loyal to the supermarket. Neither one of these two criteria can provide a complete picture of customers’ l The decision regarding the loyalty of a customer will have to take into account the visiting pattern as well as the categories of products purchased. This paper describes results of experiments that attempted to identify customer loyalty using thes e two sets of criteria separately. The experiments were based on transactional data obtained from a supermarket data collection program. Comparisons of results from these parallel sets of experiments were useful in fine tuning both the schemes of estimating the degree of loyalty of a customer. The project also provides useful insights for the development of more sophisticated measures for studying customer loyalty. It is hoped that the understanding of loyal customers will be helpful in identifying better marketing strategies. 1. Introductionloyalty is an important component of marketing analysis in a supermarket. The loyalty of a customer may be apparent through the products bought by the customer. Certain product categories such as bread and eggs may have a higher ability to distinguish between loyal and disloyal customers. Other product categories such as coffee/tea and ketchup may not be deterministic of a customer’s loyalty but may simply enhance their degree of loyalty. Establishing a scoring system based on such key product categories is one possible way of determining customer loyalty. However, the dietary habits of some loyal customers may lead to lower loyalty scores if they are based solely on product categories. Studying patterns in tra nsactional records can also provide important clues about the loyal patrons of the supermarket. It is important to conduct parallel analyses of products purchased and transaction patterns for identifying loyal customers. The two separate analyses can also be used for fine-tuning each other. This paper reports the results of experiments that studied various characteristics of loyal customers based on the products purchased and visiting patterns. The experiments were based on the data obtained from a large national supermarket chain, which was gathered over a thirteen-week period in 2000. The project was divided into two parallel streams: product based and transaction pattern based analyses. The product based analysis started with a preliminary definition of loyal customers, based on spending levels. 1 The authors would like to thank NSERC Canada, the Nova Scotia Cooperative Employment Program, and the Senate Research Grant Committee of Saint Mary’s University for the financial support. The au","PeriodicalId":39465,"journal":{"name":"International Journal of Computer Science and Applications","volume":"479 1","pages":"85-99"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86757271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Survey on Quantum Key Distribution Protocols","authors":"Jasleen Kour, Saboor Koul, Prince Zahid","doi":"10.5121/IJCSA.2017.7302","DOIUrl":"https://doi.org/10.5121/IJCSA.2017.7302","url":null,"abstract":"Quantum cryptography is based on quantum mechanics to guarantee secure communication. It allows two parties to produce a shared random bit string known only to them. These random bits can be used as a key to encrypt and decrypt messages. The most important and unique property of quantum cryptography is the ability of the two communicating users to detect the presence of any third party trying to gain knowledge of the key. It is based on fundamental aspects of quantum mechanics. By using quantum entanglement or quantum super positions and transmitting information in quantum states, a communication system can be implemented which detects eavesdropping. Quantum cryptography is used to produce and distribute a key, not to transmit any message data. This key along with certain encryption algorithm, is used to encrypt (and decrypt) a message, which can then be transmitted over a standard communication channel. This paper concentrates on comparison between classical and quantum cryptography as well as survey on various quantum key distribution protocols used to generate and distribute the key among communicating parties.","PeriodicalId":39465,"journal":{"name":"International Journal of Computer Science and Applications","volume":"31 1","pages":"19-27"},"PeriodicalIF":0.0,"publicationDate":"2017-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86399783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data Analysis and Phase Detection During Natural Disaster Based on Social Data","authors":"M. R. Huq, A. Mosharraf, Khadiza Rahman","doi":"10.5121/IJCSA.2017.7301","DOIUrl":"https://doi.org/10.5121/IJCSA.2017.7301","url":null,"abstract":"","PeriodicalId":39465,"journal":{"name":"International Journal of Computer Science and Applications","volume":"3 1","pages":"1-17"},"PeriodicalIF":0.0,"publicationDate":"2017-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78532061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}