Pub Date : 2024-04-20DOI: 10.21817/indjcse/2024/v15i2/241502029
Danar Gumilang Putera, Ruki Harwahyu
{"title":"EVALUATION OF FUZZING ON WEB API FROM OFFENSIVE AND DEFENSIVE PERSPECTIVES","authors":"Danar Gumilang Putera, Ruki Harwahyu","doi":"10.21817/indjcse/2024/v15i2/241502029","DOIUrl":"https://doi.org/10.21817/indjcse/2024/v15i2/241502029","url":null,"abstract":"","PeriodicalId":52250,"journal":{"name":"Indian Journal of Computer Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140680974","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}
Pub Date : 2024-04-20DOI: 10.21817/indjcse/2024/v15i1/241501032
Hanif Aditya Pradana, Ahmad Ardra Damarjati, Isman Kurniawan, W. Kusuma
Cancer has become one of the deadliest diseases in the world, mainly caused by the accumulation of somatic and inherited mutations. However, this phenomenon can be traced back to the molecular level, specifically, to proteins. Proteins are molecules responsible for various bioprocesses in the human body through their interactions with other molecules. Abnormalities in these interactions can lead to various undesirable outcomes, including disease and cancer. Peptides have the potential to serve as molecules that can be used in protein interactions to treat cancer. However, identification of peptides corresponding to target proteins in the laboratory is time-consuming and expensive. Therefore, there is a need for computational methods to aid identification. TabNet, a deep learning-based computational method was used in this study. For comparison purposes, we selected techniques from ensemble learning, including Random Forest and Extreme Gradient Boosting, along with methods from deep learning such as Convolutional Neural Network and Stacked Autoencoder-Deep Neural Network. Predictions are performed on a multi-feature peptide-protein interaction dataset, and the features include position-specific scoring matrices, intrinsic disorder, amino acid sequence, and physicochemical properties. Among our selected metrics, we found that TabNet achieved a better score in AUC of 0.7 and lower false negatives compared to other models.
{"title":"A DEEP LEARNING MODEL IMPLEMENTATION OF TABNET FOR PREDICTING PEPTIDE-PROTEIN INTERACTION IN CANCER","authors":"Hanif Aditya Pradana, Ahmad Ardra Damarjati, Isman Kurniawan, W. Kusuma","doi":"10.21817/indjcse/2024/v15i1/241501032","DOIUrl":"https://doi.org/10.21817/indjcse/2024/v15i1/241501032","url":null,"abstract":"Cancer has become one of the deadliest diseases in the world, mainly caused by the accumulation of somatic and inherited mutations. However, this phenomenon can be traced back to the molecular level, specifically, to proteins. Proteins are molecules responsible for various bioprocesses in the human body through their interactions with other molecules. Abnormalities in these interactions can lead to various undesirable outcomes, including disease and cancer. Peptides have the potential to serve as molecules that can be used in protein interactions to treat cancer. However, identification of peptides corresponding to target proteins in the laboratory is time-consuming and expensive. Therefore, there is a need for computational methods to aid identification. TabNet, a deep learning-based computational method was used in this study. For comparison purposes, we selected techniques from ensemble learning, including Random Forest and Extreme Gradient Boosting, along with methods from deep learning such as Convolutional Neural Network and Stacked Autoencoder-Deep Neural Network. Predictions are performed on a multi-feature peptide-protein interaction dataset, and the features include position-specific scoring matrices, intrinsic disorder, amino acid sequence, and physicochemical properties. Among our selected metrics, we found that TabNet achieved a better score in AUC of 0.7 and lower false negatives compared to other models.","PeriodicalId":52250,"journal":{"name":"Indian Journal of Computer Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140680122","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":"MACHINE LEARNING MODEL FOR ANOMALY-BASED INTRUSION DETECTION USING RANDOM FOREST CLASSIFIER","authors":"Ebiesuwa Seun, Nwachukwu Victor, Falana Taye, Adegbenjo Aderonke, Dipo Tepede, Adio Adesina","doi":"10.21817/indjcse/2024/v15i2/241502004","DOIUrl":"https://doi.org/10.21817/indjcse/2024/v15i2/241502004","url":null,"abstract":"","PeriodicalId":52250,"journal":{"name":"Indian Journal of Computer Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140680939","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}
Pub Date : 2024-04-20DOI: 10.21817/indjcse/2024/v15i1/241501028
P. Vijaya, V. Reddy
The paper recommends a new hybrid model for software projects delivery with fine blend of the both agile and waterfall models by addressing the drawbacks of both models. The traditional waterfall model development goes one phase after the other and any changes to previous phase is addressed late in the delivery cycle thus most of the times implemented software and customer expectation will not by in sync or sometimes poles apart. The trending agile focuses on small chunks development with iterative cycles, going back to the customer for feedback at every phase and reaching the expectation and delivering the functionality which client was looking for. The agile model may not be suitable for projects where the requirements are very intricate and cannot be easily fragmented into smaller iterations and requirements are definite and constant, and where deviations to the requirements are improbable. The waterfall model may not be suitable for projects where the requirements are uncertain or likely to change, as changes to the requirements need changes to previous phases. The paper recommends a new fine blended model with the best of agile and waterfall approaches with the collaboration of Delivery with Sprints and POD teams, and gets the delivery with optimal cost and speed. The projected novel model is a fine blend of best of both models by eliminating shortfalls of the Agile and Waterfall.
本文针对敏捷和瀑布模型的缺点，为软件项目交付推荐了一种新的混合模型。传统的瀑布式开发模式一个阶段接一个阶段地进行，对前一阶段的任何修改都要在交付周期的后期进行，因此大多数情况下，实施的软件与客户的期望并不同步，有时甚至相差甚远。时下流行的敏捷模式侧重于小块开发和迭代周期，在每个阶段都会向客户反馈，以达到客户的期望并交付客户所需的功能。敏捷模式可能不适合需求非常复杂、无法轻易分割成较小迭代周期、需求明确且恒定、不可能偏离需求的项目。瀑布模型可能不适合需求不确定或可能发生变化的项目，因为需求的变化需要对之前的阶段进行修改。本文推荐了一种新的精细混合模式，它结合了敏捷和瀑布式方法的优点，并与 Sprints 交付和 POD 团队合作，以最佳成本和速度完成交付。通过消除敏捷法和瀑布法的不足之处，预计的新模式是两种模式的最佳融合。
{"title":"New Hybrid Model with Fine Blend of Agile and Waterfall","authors":"P. Vijaya, V. Reddy","doi":"10.21817/indjcse/2024/v15i1/241501028","DOIUrl":"https://doi.org/10.21817/indjcse/2024/v15i1/241501028","url":null,"abstract":"The paper recommends a new hybrid model for software projects delivery with fine blend of the both agile and waterfall models by addressing the drawbacks of both models. The traditional waterfall model development goes one phase after the other and any changes to previous phase is addressed late in the delivery cycle thus most of the times implemented software and customer expectation will not by in sync or sometimes poles apart. The trending agile focuses on small chunks development with iterative cycles, going back to the customer for feedback at every phase and reaching the expectation and delivering the functionality which client was looking for. The agile model may not be suitable for projects where the requirements are very intricate and cannot be easily fragmented into smaller iterations and requirements are definite and constant, and where deviations to the requirements are improbable. The waterfall model may not be suitable for projects where the requirements are uncertain or likely to change, as changes to the requirements need changes to previous phases. The paper recommends a new fine blended model with the best of agile and waterfall approaches with the collaboration of Delivery with Sprints and POD teams, and gets the delivery with optimal cost and speed. The projected novel model is a fine blend of best of both models by eliminating shortfalls of the Agile and Waterfall.","PeriodicalId":52250,"journal":{"name":"Indian Journal of Computer Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140681898","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}
Pub Date : 2024-04-20DOI: 10.21817/indjcse/2024/v15i2/241502035
Ayman A. Aly, Mousa G.
{"title":"POSITION CONTROL FOR AN ELECTROHYDRAULIC VERTICAL LAUNCHING SYSTEM BASED ON PSOPID STRATEGY","authors":"Ayman A. Aly, Mousa G.","doi":"10.21817/indjcse/2024/v15i2/241502035","DOIUrl":"https://doi.org/10.21817/indjcse/2024/v15i2/241502035","url":null,"abstract":"","PeriodicalId":52250,"journal":{"name":"Indian Journal of Computer Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140681485","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}
Pub Date : 2024-04-20DOI: 10.21817/indjcse/2023/v15i1/241501008
Ahmad M. J. AL Moustafa, Mohd Shafry Mohd Rahim, M. Khattab, Akram M. Zeki, Safaa S. Matter, Amr Mohmed Soliman, Abdelmoty M. Ahmed
{"title":"ARABIC SIGN LANGUAGE RECOGNITION SYSTEMS: A SYSTEMATIC REVIEW","authors":"Ahmad M. J. AL Moustafa, Mohd Shafry Mohd Rahim, M. Khattab, Akram M. Zeki, Safaa S. Matter, Amr Mohmed Soliman, Abdelmoty M. Ahmed","doi":"10.21817/indjcse/2023/v15i1/241501008","DOIUrl":"https://doi.org/10.21817/indjcse/2023/v15i1/241501008","url":null,"abstract":"","PeriodicalId":52250,"journal":{"name":"Indian Journal of Computer Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140682116","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}
Pub Date : 2024-04-20DOI: 10.21817/indjcse/2024/v15i2/241502003
Parvati Kanaki, Dr Gyanappa A. Walikar
{"title":"MACHINE LEARNING MODELS FOR HEART DISEASE PREDICTION-A REVIEW","authors":"Parvati Kanaki, Dr Gyanappa A. Walikar","doi":"10.21817/indjcse/2024/v15i2/241502003","DOIUrl":"https://doi.org/10.21817/indjcse/2024/v15i2/241502003","url":null,"abstract":"","PeriodicalId":52250,"journal":{"name":"Indian Journal of Computer Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140679532","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}
Pub Date : 2024-04-20DOI: 10.21817/indjcse/2024/v15i1/241501051
Cho Cho Htet, Aye Myat Thu
The feature of Blockchain as distributed ledger that are shared among nodes within a computer network, renowned for its pivotal role in cryptocurrency systems by ensuring a secure and decentralized the role of transaction record which is ensured for maintenance of security and decentralization in cryptocurrency systems. The Linux Foundation host the open-source framework of private blockchain, the Hyperledger Fabric (HLF). Smart contracts are utilized for transaction management and a modular architecture of blockchain framework, providing a foundation for the development of blockchain-based applications through plug-and-play components. In the realm of distributed systems, scalability emerges as a crucial design goal for developers. The most appropriate blockchain platform for the operations of the business industry, which need for the seamless addition of more users and resources without perceptible performance loss. An assessment of scalability is required as a large number of nodes involvement in the implementation of blockchain frameworks. In this paper, the impact of system configurations such as, transaction volume, node types is focused in the transition of V2.2.4 with the various significant issues with the architecture. The throughput, latency, processor, and memory usages are mainly analyzed based on the different number of transactions. According to the performance results of the proposed system, the scalability of the possible number of transactions and the different peer nodes can be supported in the implementation of blockchain-based system for HLF blockchain.
{"title":"SCALABILITY ASSESSMENT AND PERFORMANCE OPTIMIZATION OF HYPERLEDGER FABRIC","authors":"Cho Cho Htet, Aye Myat Thu","doi":"10.21817/indjcse/2024/v15i1/241501051","DOIUrl":"https://doi.org/10.21817/indjcse/2024/v15i1/241501051","url":null,"abstract":"The feature of Blockchain as distributed ledger that are shared among nodes within a computer network, renowned for its pivotal role in cryptocurrency systems by ensuring a secure and decentralized the role of transaction record which is ensured for maintenance of security and decentralization in cryptocurrency systems. The Linux Foundation host the open-source framework of private blockchain, the Hyperledger Fabric (HLF). Smart contracts are utilized for transaction management and a modular architecture of blockchain framework, providing a foundation for the development of blockchain-based applications through plug-and-play components. In the realm of distributed systems, scalability emerges as a crucial design goal for developers. The most appropriate blockchain platform for the operations of the business industry, which need for the seamless addition of more users and resources without perceptible performance loss. An assessment of scalability is required as a large number of nodes involvement in the implementation of blockchain frameworks. In this paper, the impact of system configurations such as, transaction volume, node types is focused in the transition of V2.2.4 with the various significant issues with the architecture. The throughput, latency, processor, and memory usages are mainly analyzed based on the different number of transactions. According to the performance results of the proposed system, the scalability of the possible number of transactions and the different peer nodes can be supported in the implementation of blockchain-based system for HLF blockchain.","PeriodicalId":52250,"journal":{"name":"Indian Journal of Computer Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140681779","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}
Pub Date : 2024-04-20DOI: 10.21817/indjcse/2024/v15i1/241501036
S. A. Kumar, A. Dharani, Deepak Mb, Aishwarya K Kamble
Detecting diseases in plant at an early stage is important for ensuring healthy crops and reducing economic losses. Traditional methods are slow and require expertise. The recent technological developments bring in a lot of computational techniques that enables the detection of diseases at an early stage and more accurate. The proposed work has been implemented using deep learning algorithms The work focuses on identifying the diseases in Arecanut leaf and analyzing the efficiency of the deep learning techniques in detecting the type of diseases. Different CNN algorithms like ReNet, MobiNet and VGG Net have been implemented and tested for thier efficiency. The appropriate model is then optimized and deployed in an Android device so as to enable the farmer to use the application efficiently. The proposed work is implemented by collecting a dataset of arecanut diseased leaf images and dividing it for training, validation, and testing. The performance of the models are compared using the parameters (trainable and non-trainable) and the utilisation of the memory during runtime. The models are evaluated based on accuracy and precision. For the given dataset, ResNet performed with 79% accuracy, MobiNet with 86% and VGG with 92% accuracy. The performance efficiency of VGGNet is better than the other two architectures and deployed in Android device to help the stakeholders.
{"title":"AN AUTOMATAED DEEP LEARNING MODEL TO CLASSIFY DISEASES IN AREACANUT PLANT","authors":"S. A. Kumar, A. Dharani, Deepak Mb, Aishwarya K Kamble","doi":"10.21817/indjcse/2024/v15i1/241501036","DOIUrl":"https://doi.org/10.21817/indjcse/2024/v15i1/241501036","url":null,"abstract":"Detecting diseases in plant at an early stage is important for ensuring healthy crops and reducing economic losses. Traditional methods are slow and require expertise. The recent technological developments bring in a lot of computational techniques that enables the detection of diseases at an early stage and more accurate. The proposed work has been implemented using deep learning algorithms The work focuses on identifying the diseases in Arecanut leaf and analyzing the efficiency of the deep learning techniques in detecting the type of diseases. Different CNN algorithms like ReNet, MobiNet and VGG Net have been implemented and tested for thier efficiency. The appropriate model is then optimized and deployed in an Android device so as to enable the farmer to use the application efficiently. The proposed work is implemented by collecting a dataset of arecanut diseased leaf images and dividing it for training, validation, and testing. The performance of the models are compared using the parameters (trainable and non-trainable) and the utilisation of the memory during runtime. The models are evaluated based on accuracy and precision. For the given dataset, ResNet performed with 79% accuracy, MobiNet with 86% and VGG with 92% accuracy. The performance efficiency of VGGNet is better than the other two architectures and deployed in Android device to help the stakeholders.","PeriodicalId":52250,"journal":{"name":"Indian Journal of Computer Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140680921","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}
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