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Computer Science & Engineering: An International Journal最新文献

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Mimeme Attribute Classification using LDV Ensemble Multimodel Learning 基于LDV集成多模型学习的Mimeme属性分类
Computer Science & Engineering: An International Journal Pub Date : 2022-12-30 DOI: 10.5121/cseij.2022.12610
D. K, Akoramurthy B, T. Sivakumar, M. Sathya
{"title":"Mimeme Attribute Classification using LDV Ensemble Multimodel Learning","authors":"D. K, Akoramurthy B, T. Sivakumar, M. Sathya","doi":"10.5121/cseij.2022.12610","DOIUrl":"https://doi.org/10.5121/cseij.2022.12610","url":null,"abstract":"One of the most common types of social networking interaction is memes. Memes are innately multimodal, so studying and processing them is a hot issue currently. This study's analysis of the DV dataset comprises classifying memes according to their irony, humour, motive, and overarching mood. The effectiveness of three different creative transformer-based strategies has been carefully examined. The DV Dataset used here is created by own meme data for this implementation analysis of hateful memes. Out of all of our strategies, the proposed ensemble model LDV obtained a macro F1 score of 0.737 for humour classification, 0.775 for motivation classification, 0.69 for sarcasm classification, and 0.756 for overall sentiment of the meme.","PeriodicalId":361871,"journal":{"name":"Computer Science & Engineering: An International Journal","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129243828","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}
引用次数: 0
Systems using Wireless Sensor Networks for Big Data 使用无线传感器网络的大数据系统
Computer Science & Engineering: An International Journal Pub Date : 2022-12-30 DOI: 10.5121/cseij.2022.12605
Richa Verma, Ravindara Bhatt
{"title":"Systems using Wireless Sensor Networks for Big Data","authors":"Richa Verma, Ravindara Bhatt","doi":"10.5121/cseij.2022.12605","DOIUrl":"https://doi.org/10.5121/cseij.2022.12605","url":null,"abstract":"Wireless sensor networks are continually developing in the big data world and are widely employed in many aspects of life. In the monitoring region, the WSN gathers, analyses, and sends information about the detected item. In recent years, WSN has also made important strides in the management of critical data protection, traffic monitoring, and climate - change detection. The rich big data contributors known as wireless sensor networks provide a significant amount of data from numerous sensor nodes in large-scale networks (WSNs), which are among the numerous potential datasets. However, unlike traditional wireless networks, suffer from significant constraints in communication and data dependability due to the cluster’s constraints. This paper gives a detailed assessment of cutting-edge research on using WSN into large data systems Potential network and effective deployment and scientific problems are presented and discussed in the context of the study topics and aim. Finally, unresolved issues are addressed in order to discuss interesting future research possibilities.","PeriodicalId":361871,"journal":{"name":"Computer Science & Engineering: An International Journal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115740695","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}
引用次数: 2
Recognization Holic- Medicine Detection using Deep Learning Techniques 使用深度学习技术识别全息医学检测
Computer Science & Engineering: An International Journal Pub Date : 2022-12-30 DOI: 10.5121/cseij.2022.12614
Kayethri D, D. R, Harini M
{"title":"Recognization Holic- Medicine Detection using Deep Learning Techniques","authors":"Kayethri D, D. R, Harini M","doi":"10.5121/cseij.2022.12614","DOIUrl":"https://doi.org/10.5121/cseij.2022.12614","url":null,"abstract":"Everyone's life is significantly impacted by medicine. The rate at which a certain disease's treatment is discovered is rising daily. Therefore, it is important for people to be attentive when taking their medications and that they are knowledgeable about them. Providing various medication-related capabilities such as reminders to take medications on time, prescription information, and to aid chronic patients in taking numerous medications appropriately and avoiding taking the wrong medications, which may create drug interactions. The RECOGNIZATION HOLIC programme uses deep learning to create an intelligent system for identifying medications. By matching the text in the image with the dataset, which contains various drug names and their descriptions, the dataset can be displayed in this application. The scanned image is used to extract the text. By using an optical character recognition algorithm and tensor flow character training in the Android operating system, the text is recovered from the scanned image of the medication label. The reminder option with the medicine photo and the time to take their medicine is added into this application to help the people who might take the wrong medicine or forget to acquire the medicine. This software assists patients in taking their medications properly and at the ideal time.","PeriodicalId":361871,"journal":{"name":"Computer Science & Engineering: An International Journal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116829372","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}
引用次数: 1
An Overview of Copy Move Forgery Detection Approaches 拷贝移动伪造检测方法综述
Computer Science & Engineering: An International Journal Pub Date : 2022-12-30 DOI: 10.5121/cseij.2022.12609
G. S, D. S
{"title":"An Overview of Copy Move Forgery Detection Approaches","authors":"G. S, D. S","doi":"10.5121/cseij.2022.12609","DOIUrl":"https://doi.org/10.5121/cseij.2022.12609","url":null,"abstract":"Images have greater expressive power than any other forms of documents. With the Internet, images are widespread in several applications. But the availability of efficient open-source online photo editing tools has made editing these images easy. The fake images look more appealing and original than the real image itself, which makes them indistinguishable and hence difficult to detect. The authenticity of digital images like medical reports, scan images, financial data, crime evidence, legal evidence, etc. is of high importance. Detecting the forgery of images is therefore a major research area. Image forgery is categorized as copy-move forgery, splicing, and retouching. In this work, a review of copy-move forgery is discussed along with the existing research on its detection and localization using both conventional and deep-learning mechanisms. The datasets used and challenges towards improving or developing novel algorithms are also presented.","PeriodicalId":361871,"journal":{"name":"Computer Science & Engineering: An International Journal","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123512304","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}
引用次数: 1
Detection of Face Mask and Glass using Deep Learning Algorithm 基于深度学习算法的面罩和玻璃检测
Computer Science & Engineering: An International Journal Pub Date : 2022-12-30 DOI: 10.5121/cseij.2022.12602
K. Bharathi, Savithadevi M
{"title":"Detection of Face Mask and Glass using Deep Learning Algorithm","authors":"K. Bharathi, Savithadevi M","doi":"10.5121/cseij.2022.12602","DOIUrl":"https://doi.org/10.5121/cseij.2022.12602","url":null,"abstract":"The rapid spread of the coronavirus has been a major health concern for the entire world. Direct human contact is one of the key factors contributing to the virus's rapid transmission. Wearing face masks in public areas is one of the numerous precautions that may be taken to stop the spread of this infection. To reduce the chance of the virus spreading, it is important to find ways to detect face masks in public locations. An automated system for face mask detection utilizing deep learning algorithm has been presented to address these issues and effectively stop the spread of this contagious disease. The proposed method combines a face mask identification model and a glass detection model using an algorithm to assess the results using deep learning models.","PeriodicalId":361871,"journal":{"name":"Computer Science & Engineering: An International Journal","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132795743","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}
引用次数: 0
The Increasing Threat to Digital Assets Due to the Development of Quantum Algorithms 量子算法的发展对数字资产的威胁越来越大
Computer Science & Engineering: An International Journal Pub Date : 2022-12-30 DOI: 10.5121/cseij.2022.12612
Basil Hanafi, M. Bokhari, Imran Khan
{"title":"The Increasing Threat to Digital Assets Due to the Development of Quantum Algorithms","authors":"Basil Hanafi, M. Bokhari, Imran Khan","doi":"10.5121/cseij.2022.12612","DOIUrl":"https://doi.org/10.5121/cseij.2022.12612","url":null,"abstract":"The development in this digital era is fast pacing up to the future where machines will be able to perform tasks more efficiently and rapidly than even today’s supercomputers aren’t able to perform. Increasing technology and exponential development in the domain of Quantum Computing are leading humanity to the future where Computers will be able to solve unsolvable or exponential time-consuming problems in a span of short time. This will be proved advantageous in numerous ways but as every state of development has other aspects, this development will also be able to break down every possible cryptographic algorithm implemented in the classical computing era as they all are based on complex mathematical equations and calculations. A perfectly implemented Quantum Computer will be able to compute the mathematical calculations in parallel due to the phenomenon of Superposition and entanglement. All possible progress in Quantum Algorithms is discussed in the context of digital security in this paper which can be a possible threat to every executed cryptographic algorithm and securities instigated through them.","PeriodicalId":361871,"journal":{"name":"Computer Science & Engineering: An International Journal","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127129692","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}
引用次数: 0
Analysis of Seismic Signal and Detection of Abnormalities 地震信号分析与异常探测
Computer Science & Engineering: An International Journal Pub Date : 2022-12-30 DOI: 10.5121/cseij.2022.12607
Sujata Kulkarni, U. Bhosle, V. T
{"title":"Analysis of Seismic Signal and Detection of Abnormalities","authors":"Sujata Kulkarni, U. Bhosle, V. T","doi":"10.5121/cseij.2022.12607","DOIUrl":"https://doi.org/10.5121/cseij.2022.12607","url":null,"abstract":"Seismic signals are ground vibrations used to detect seismic events. However, seismic signal captured from sensors is distorted signal contains noise and makes actual event detection difficult. In most cases, external noise such as manmade or any heavy vehicle vibration always overlaps with the seismic reflections over time. The presence of noise in the seismic signal makes it difficult to determine the magnitude at which the seismic events have occurred. The aim of our study is to process the signals received from seismic sensor and identify it as seismic events signal and non-seismic events signal based on the magnitude. The authors propose a robust noise suppression method using bandpass filter, IIR Wiener filter and event detection using recursive Short-Term Average (STA)/Long Term Average (LTA) and Carl Short Term Average (STA)/Long Term Average (LTA). The proposed study determines reference magnitude to distinguish seismic and non-seismic activity. The projected study is based on the analysis of seismic signal received from single sensor and sensor networks (SN) and determines the magnitude to distinguish seismic and nonseismic events and time of an actual earthquake event. The experimental dataset is a broadband seismic signal from BSVK and CUKG station sensors located at Basavakalyan, Karnataka, and the Central University of Karnataka respectively. The proposed approach helps to extract the information about preseismic event, actual seismic event, post-seismic event activities and identify the abnormal pattern that supports to detect heearth’s activities before the actual seismic event.","PeriodicalId":361871,"journal":{"name":"Computer Science & Engineering: An International Journal","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126570001","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}
引用次数: 1
Convolutional Neural Network based Retinal Vessel Segmentation 基于卷积神经网络的视网膜血管分割
Computer Science & Engineering: An International Journal Pub Date : 2022-12-30 DOI: 10.5121/cseij.2022.12613
Savithadevi M
{"title":"Convolutional Neural Network based Retinal Vessel Segmentation","authors":"Savithadevi M","doi":"10.5121/cseij.2022.12613","DOIUrl":"https://doi.org/10.5121/cseij.2022.12613","url":null,"abstract":"In human eye, the state of the blood vessel is a crucial diagnostic factor. The segmentation of blood vessel from the fundus image is difficult due to the spatial complexity, adjacency, overlapping and variability of blood vessel. The detection of ophthalmic pathologies like hypertensive disorders, diabetic retinopathy and cardiovascular diseases are remain challenging task due to the wide-ranging distribution of blood vessels. In this paper, Stacked Autoencoder and CNN (Convolutional Neural Network) technique is proposed to extract the blood vessel from the fundus image. Based on the experiments conducted using the Stacked Autoencoder and Convolutional Neural Network gives 90% & 95% accuracy for segmentation.","PeriodicalId":361871,"journal":{"name":"Computer Science & Engineering: An International Journal","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114361893","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}
引用次数: 1
Preprocessing Challenges for Real World Affect Recognition 现实世界情感识别的预处理挑战
Computer Science & Engineering: An International Journal Pub Date : 2022-12-30 DOI: 10.5121/cseij.2022.12606
Karishma Raut, S. Kulkarni
{"title":"Preprocessing Challenges for Real World Affect Recognition","authors":"Karishma Raut, S. Kulkarni","doi":"10.5121/cseij.2022.12606","DOIUrl":"https://doi.org/10.5121/cseij.2022.12606","url":null,"abstract":"Real world human affect recognition requires immediate attention which is a significant aspect of humancomputer interaction. Audio-visual modalities can make a significant contribution by providing rich contextual information. Preprocessing is an important step in which the relevant information is extracted. It has a crucial impact on prominent feature extraction and further processing. The main aim is to highlight the challenges in preprocessing real world data. The research focuses on experimental testing and comparative analysis for preprocessing using OpenCV, Single Shot MultiBox Detector (SSD), DLib, Multi-Task Cascaded Convolutional Neural Networks (MTCNN), and RetinaFace detectors. The comparative analysis shows that MTCNN and RetinaFace give better performance in real world data. The performance of facial affect recognition using a pre-trained CNN model is analysed with a lab-controlled dataset CK+ and a representative wild dataset AFEW. This comparative analysis demonstrates the impact of preprocessing issues on feature engineering framework in real world affect recognition.","PeriodicalId":361871,"journal":{"name":"Computer Science & Engineering: An International Journal","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122384983","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}
引用次数: 0
Data Management Issues and Study on Heterogeneous Data Storage in the Internet of Things 物联网数据管理问题及异构数据存储研究
Computer Science & Engineering: An International Journal Pub Date : 2022-12-30 DOI: 10.5121/cseij.2022.12604
T. Selvi, S. Sasirakha
{"title":"Data Management Issues and Study on Heterogeneous Data Storage in the Internet of Things","authors":"T. Selvi, S. Sasirakha","doi":"10.5121/cseij.2022.12604","DOIUrl":"https://doi.org/10.5121/cseij.2022.12604","url":null,"abstract":"The Internet of Things is a networking standard that connects various hardware, including digital, physical, and virtual things that may communicate with one another and carry out user-requested tasks. Traditional database management methods cannot be used in this entity because of the variety, large volume and heterogeneous data generated by them. The rapid growth of heterogeneous data can only be managed by distributed and parallel computer systems and databases. When it comes to handling vast amount of diverse data, most relational databases have a variety of drawbacks because they were designed for a certain format. One of the most difficulties in data management is investigating such heterogeneous data. Consequently, IoT data management system design has to be considered with some distinct principles. These various guiding concepts enable the suggestion of various IoT data management system strategies. The solution should provide a unified format for the conversion of various heterogeneous data which are generated by the sensors. The integration of generated data is made simple by some middleware or architecture-oriented solutions. Other methods also offer effective storage of the unified data generated. This paper surveys the challenges of IoT Data management and provides a survey about the storage of heterogeneous data and the type of data used.","PeriodicalId":361871,"journal":{"name":"Computer Science & Engineering: An International Journal","volume":"180 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132706088","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}
引用次数: 0
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