International Journal of Advanced Trends in Computer Science and Engineering最新文献

{"title":"Spoken Language Identification using CNN with Log Mel Spectrogram Features in Indian Context","authors":"Sreedhar Potla, D. B. V. Vardhan","doi":"10.30534/ijatcse/2022/071162022","DOIUrl":"https://doi.org/10.30534/ijatcse/2022/071162022","url":null,"abstract":"This study demonstrates a novel application of Log Mel Spectrogram coefficients to image classification via Convolutional Neural Networks (CNN). The acoustic features obtained as log mel spectrogram images are used in this article. Log mel spectrogram pictures, a novel technique, ensure that the system is noise-resistant and free of channel mismatch. The majority of Indian languages from our own dataset were used.With the use of auditory features integrated in CNN, we hope to quickly and accurately detect a language. InceptionV3 and Resnet50 models are also used in this study for performance analysis. When compared to the existing system, these approaches achieved significant improvements in language identification accuracy.","PeriodicalId":129636,"journal":{"name":"International Journal of Advanced Trends in Computer Science and Engineering","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127992236","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":"Framework for Intelligent Early Warning Systems of Crop Diseases","authors":"","doi":"10.30534/ijatcse/2022/021152022","DOIUrl":"https://doi.org/10.30534/ijatcse/2022/021152022","url":null,"abstract":"The Early Warning System (EWS) is a critical tool for efficiently preventing hazards in agricultural productivity, as well as pests and illnesses. Early detection of plant diseases helps in increasing crop yields and decreasing losses. EWS can acquire relevant and timely information in areas where this information or data is unavailable. This paper presents a framework aimed to warning farmers of the expected crop diseases that might affect their crops. It will support a timely recommendation for the appropriate agriculture practices directed towards correct farm management. The proposed framework objective is to design a model for utilizing weather forecasting and domain knowledge that is related to the effect of weather on plant diseases. The framework output depends on the integration of weather data, which might affect crop diseases and farmers’ databases that include farmers’ locations and cultivated crops. Furthermore, it will enable agriculture extension agents to communicate with farmers and provide them with advices about weather data and how to deal with it to preserve crops and increase yields.","PeriodicalId":129636,"journal":{"name":"International Journal of Advanced Trends in Computer Science and Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128849362","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 Deep Learning Approach for The Detection of Structured Query Language Injection Vulnerability","authors":"","doi":"10.30534/ijatcse/2022/051152022","DOIUrl":"https://doi.org/10.30534/ijatcse/2022/051152022","url":null,"abstract":"With the rapid development of Web 2.0 technology, network applications have gradually become an indispensable part of our lives. At the same time, Web applications are confronted with more challenges. As announced by the OWASP (open web application security project) organization, injection attack has been the first of the top 10 security vulnerabilities in 2013 and 2017, and SQL injection attack is one of the most important types among the injection attacks. Due to the rapid growth of SQL injection attacks on web application, this research developed a deep learning model in detecting SQL injection attack. The model was trained on a dataset that contains about 30,635 queries, which includes both injected and non-injected queries. The dataset was gotten from Kaggle database. The dataset was then processed by removing null and duplicate values. Further pre-processing was carried out in terms of tokenization and conversion of text to arrays. CountVectorizer () function was used for data normalization in converting the dataset to arrays in form of 0s and 1s. After the pre-processing stage, Feature selection was done on the dataset using the tfidvectoriser. The selected features were passed to the deep feed forward neural network for training. The model was trained on a step of 20 epochs, the model achieved an accuracy of 97.65%. Confusion matrix depicts the total number of correct prediction and the total number of false classifications. The confusion matrix shows that out of 590 classifications on attacks that are of normal, the model predicted correctly for 572 and predicted falsely for 16 times. Then for attacks that are of SQL injection, the model predicted correctly 251 times and predicted falsely for just 1. This shows the performance of the model is in good shape. The model was saved and deployed to web using python flask for easy testing and usage. The model was compared with other existing models and it outperformed the existing model in terms of accuracy. This research can further be extended by using combinations of deep learning algorithms. It can further be extended by deploying the model to android applications.","PeriodicalId":129636,"journal":{"name":"International Journal of Advanced Trends in Computer Science and Engineering","volume":"231 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121087631","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":"Exploring the Integration of Lisp into a Modern Reinforcement Learning Project Through the Use of Hy","authors":"","doi":"10.30534/ijatcse/2022/011152022","DOIUrl":"https://doi.org/10.30534/ijatcse/2022/011152022","url":null,"abstract":"This paper explores the usage of Lisp in a small modern Reinforcement Learning (RL) project. The Lisp dialect, Hy programming language, is used to incorporate the traditional libraries and packages in up-to-date workflows. This project is centered around the usage of NetHack for RL. The MiniHack sandbox framework and NetHack Learning Environment (NLE) are used to create custom training/testing environments and tasks. The MiniHack sandbox framework creates a simple level editor and creation interface for use in the training and evaluation process of the agent. NLE is chosen as the working environment. For the agent model, this project adopts Torchbeast’s PolyBeast, a PyTorch implementation of the IMPALA architecture. The usage of Hy within this project is forefront, and so it is implemented as much as possible to accomplish the tasks.","PeriodicalId":129636,"journal":{"name":"International Journal of Advanced Trends in Computer Science and Engineering","volume":"215 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123158741","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":"Improvement of Bat Algorithm Classification Accuracy Using Image Fusion Techniques","authors":"","doi":"10.30534/ijatcse/2022/031152022","DOIUrl":"https://doi.org/10.30534/ijatcse/2022/031152022","url":null,"abstract":"This paper investigates the pansharpening influence on satellite images-classification using Bat algorithm (BA). To this end, experiments are proceed using two fusion techniques: Brovey Transform and Intensity-Hue-Saturation transform, in order to merge the characteristics of images of the same area. Considering the classification as an optimization problem, BA can be applied on a fully-featured image. For this research, recent Landsat 8 panchromatic and multispectral images taken over the city of Oran (Algeria) are used to show the performance of BA and the benefit of using fusion techniques to improve classification. This paper shows improvement in the results when a fusion step is applied. Additionally, BA performance is compared against K- Means and Particle Swarm Optimization. From the obtained results, it can be concluded that BA can be successfully applied to solve unsupervised classification problems.","PeriodicalId":129636,"journal":{"name":"International Journal of Advanced Trends in Computer Science and Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130080457","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":"Arabic Handwritten Word Recognition System Based on the Wavelet Packet Decomposition","authors":"","doi":"10.30534/ijatcse/2022/041152022","DOIUrl":"https://doi.org/10.30534/ijatcse/2022/041152022","url":null,"abstract":"This paper attempts to recognize Arabic handwriting based on the Wavelet Packet Decomposition (WPD) using two different classifiers (Support Vector Machine SVM with three kernels and k-Nearest Neighbors K-NN). The proposed approach of recognizing Arabic handwriting contains three major stages including image preprocessing, extracting the features of the image, and classification. Firstly, the diacritics are removed using the opening morphological operation (i.e image preprocessing). Secondly, extracting the structure of the paragraph using the morphological method. Finally, the word image size is converted into a suitable size for the next stages. To extract features from the image, the WPD method was adopted to extract the features of Arabic handwriting as the transformation method of feature space. This extracts the Arabic global features to be classified in the last stage using the SVM with polynomial kernel and K-NN. The proposed approach of recognizing Arabic handwriting was tested on IFN/ENIT dataset by rescaling images into various sizes, 93.7% when the SVM with polynomial kernel is used, K-NN classifier achieved accuracy rate is 88.4%.","PeriodicalId":129636,"journal":{"name":"International Journal of Advanced Trends in Computer Science and Engineering","volume":"1081 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116030705","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":"User Authentication Techniques of the Internet of Things","authors":"","doi":"10.30534/ijatcse/2022/071142022","DOIUrl":"https://doi.org/10.30534/ijatcse/2022/071142022","url":null,"abstract":"Internet of Things (IoT) is a growing technology that has got the attention of researchers from different fields. As more of IoT resources increasing, the number of users' sensitive data also increasing. Authentication process is one of the security factors which used to protect the users' sensitive data and identify whether the user is legitimate or not. IoT facing many authentication challenges due to its complicated environment it contains a massive number of connected devices, different layers, big data, cloud computing, and the open channels. The truly important challenge of the user authentication process for IoT resources is how to do it more securely and easily utilize for computer-illiterate users. In general, the password is the first defense line of user confidentiality, but the use of alphanumeric-based passwords is still widely, even with the existence of alternatives designed to overcome its weakness. Graphical-based Password is one of the practical alternatives which is developed to improve the security of the user authentication. This paper aims to explore the various authentication aspects that can be used in the IoT technology. Also, an overview of the graphical passwords which can be used as an alternative of the weak alphanumeric-based passwords in IoT technology.","PeriodicalId":129636,"journal":{"name":"International Journal of Advanced Trends in Computer Science and Engineering","volume":"185 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124690386","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":"Framework of Nutritious Food in Technology of Vending Machine for People Health","authors":"","doi":"10.30534/ijatcse/2022/051142022","DOIUrl":"https://doi.org/10.30534/ijatcse/2022/051142022","url":null,"abstract":"the vending machine make our life easier. When we buy at vending machine, it will save our time which is we could not go to shop to buy food. But many of vending machine foods are not healthy enough for our body to eat. This research is discussing about the healthier of vending machine which is we must think what type of food are our body need means that the healthy food not likes snack or carbonate drink. The objective of the research is to measure the healthier vending machine for human life. It means the food in vending machine should be healthy to our body. The methodology of the research is three phase firstly preliminary research, identify the element of healthy vending machine and last method is the finding research.","PeriodicalId":129636,"journal":{"name":"International Journal of Advanced Trends in Computer Science and Engineering","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123102508","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 Review Paper on Real Time Sign Language Detection for the Deaf and Dumb","authors":"","doi":"10.30534/ijatcse/2022/021142022","DOIUrl":"https://doi.org/10.30534/ijatcse/2022/021142022","url":null,"abstract":"Hand gesture is one of the styles used in sign language for non-verbal communication. It's most generally used by deaf & dumb people who have hearing or speech problems to communicate among themselves or with normal people. Plathora sign language systems had been developed by numerous makers around the world but they're neither flexible nor cost-effective for the end druggies. Hence, it's a software which presents a system prototype that's suitable to automatically recognize sign language to help deaf and dumb people to communicate more effectively with each other or normal people. Dumb people are generally deprived of normal communication with other people in the society, also normal people find it hard to understand and communicate with them. These people have to depend on an interpreter or on some kind of visual communication. An interpreter won’t be always available and visual communication is substantially delicate to understand. As a normal person is ignorant of the grammar or meaning of numerous gestures that are part of a sign language, it's primarily limited to their families and/ or deaf and dumb community","PeriodicalId":129636,"journal":{"name":"International Journal of Advanced Trends in Computer Science and Engineering","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127780220","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":"Application of Remote Sensing Techniques to Detect Roads and its Neighborhood by Using Graph-Based Algorithms","authors":"","doi":"10.30534/ijatcse/2022/061142022","DOIUrl":"https://doi.org/10.30534/ijatcse/2022/061142022","url":null,"abstract":"The application of remote sensing techniques is widely used in the analysis of remote sensing objects. The extraction and analysis of roads using this technique provide efficient data for the stakeholders which can be utilized in various occasions. The road structures are always incubated with rich neighboring objects. The extraction and the segregation of these neighborhood objects is a challenging task. The rich set of image processing algorithms provides a flexible extraction of road and its neighborhood. This paper proposes a graph technique to extract the road and its neighborhood. The graph cut algorithm is applied to 4 sets of image datasets. The datasets are taken at different intervals. The paper is aimed at detecting the road and its neighborhood. The segmentation accuracy in detecting roads and its neighborhood is also discussed in the article","PeriodicalId":129636,"journal":{"name":"International Journal of Advanced Trends in Computer Science and Engineering","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132862453","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}