2023 6th International Conference on Information Systems and Computer Networks (ISCON)最新文献_第2页

Supervised Machine Learning for Cloud Security 云安全的监督机器学习

2023 6th International Conference on Information Systems and Computer Networks (ISCON) Pub Date : 2023-03-03 DOI: 10.1109/ISCON57294.2023.10112078

S. Singhal, Rishabh Srivastava, R. Shyam, Deepak Mangal

{"title":"Supervised Machine Learning for Cloud Security","authors":"S. Singhal, Rishabh Srivastava, R. Shyam, Deepak Mangal","doi":"10.1109/ISCON57294.2023.10112078","DOIUrl":"https://doi.org/10.1109/ISCON57294.2023.10112078","url":null,"abstract":"Although there is a lot of interest in cloud computing, security concerns have prevented it from becoming mainstream. Users of cloud services frequently worry about the loss of data, the compromise of security, and the unavailability of the services at important moments. Security applications that employ learning-based solutions are gaining attraction in the literature thanks to recent advancements. However, the most challenging aspect of these approaches is the objective datasets. Numerous internal datasets are off-limits for public usage for various reasons, including privacy and the possibility of missing statistical information. Even though there is some lacking, researchers are using these datasets for training and testing in experimental settings. Using a single dataset to train a machine learning model often produces misleading findings. How well these models perform when applied to data from a variety of sources and contexts is an open question, although it hasn’t been thoroughly explored in the literature. As, cloud problems are unique, therefore it is crucial to evaluate the performance of these models over a wide range of circumstances. To train the supervised machine learning models used in this research, we make use of the dataset made available by UNSW. For evaluating the performance of these models, we have used the ISOT dataset.","PeriodicalId":280183,"journal":{"name":"2023 6th International Conference on Information Systems and Computer Networks (ISCON)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129028039","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

Smart Piscis Monitoring System Using IoT 使用物联网的智能双鱼座监控系统

2023 6th International Conference on Information Systems and Computer Networks (ISCON) Pub Date : 2023-03-03 DOI: 10.1109/ISCON57294.2023.10112143

Malathy Sathyamoorthy, C. Vanitha, K. Kaliswary, Rakesh Kumar, B. Sharma, Subrata Chowdhury

{"title":"Smart Piscis Monitoring System Using IoT","authors":"Malathy Sathyamoorthy, C. Vanitha, K. Kaliswary, Rakesh Kumar, B. Sharma, Subrata Chowdhury","doi":"10.1109/ISCON57294.2023.10112143","DOIUrl":"https://doi.org/10.1109/ISCON57294.2023.10112143","url":null,"abstract":"In the world, there are many species gifted by the nature. One of the most important and beautiful creatures in the world is fish species. It creates the marine world and plays a main role in aquaculture. In this modern generation, many lives going on endangered species, due to inadequate feeding of food also not having the proper maintenance to enhance the species. This causes the food chain to be affected. So, this can become one of the major environmental issues. Using IOT sensors we can closely monitor marine species. To rectify these difficulties, sensors are replaced in the place of humans, so that an immediate solution is provided for the problem. By using the PH water sensor is familiar with sensing the water quality system. Temperature sensor will find the water temperature to achieve a good environment. To feed the fish we use the feeding valve is rotated by a servo motor and it is automated (on or off) using an Android device where data are read by Node MCU. It helps to improve the efficiency of the aqua species’ health. An IOT makes humans free from the workload and provides a good environment for the fish. Tools used here such as Android device, Node MCU, PH sensor, Servo motor, and temperature sensor. The paper’s goal is to keep track of the factors required for aquaculture’s survival.","PeriodicalId":280183,"journal":{"name":"2023 6th International Conference on Information Systems and Computer Networks (ISCON)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123993728","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

Detection of Tomato leaf diseases using Attention Embedded Hyper-parameter Learning Optimization in CNN 基于CNN关注嵌入超参数学习优化的番茄叶片病害检测

2023 6th International Conference on Information Systems and Computer Networks (ISCON) Pub Date : 2023-03-03 DOI: 10.1109/ISCON57294.2023.10111992

Jenifer Mahilraj, P. Sivaram, B. Sharma, Ns Lokesh, B. Bobinath, Rahul Moriwal

{"title":"Detection of Tomato leaf diseases using Attention Embedded Hyper-parameter Learning Optimization in CNN","authors":"Jenifer Mahilraj, P. Sivaram, B. Sharma, Ns Lokesh, B. Bobinath, Rahul Moriwal","doi":"10.1109/ISCON57294.2023.10111992","DOIUrl":"https://doi.org/10.1109/ISCON57294.2023.10111992","url":null,"abstract":"India’s agricultural productivity is vital, as its economy depends heavily on it. Plant disease identification in plants is significant in the agricultural sector, as a disease in plants is very natural. In this setting, plants are susceptible to damage that reduces their quality, quantity, or production if proper precautions are not followed. Tomatoes are the most common crop globally, and they can be used in various ways in any kitchen, regardless of cuisine. It is the third most widely grown crop globally, after potatoes and sweet potatoes. India was ranked second in tomato production. However, the quality and quantity of tomato crops suffer from numerous diseases. As a result, the paper discusses a deep learning-based approach to disease detection. Outdated approaches focus on handcrafted features extracted from obtained images to regulate infection. The quality of the handmade elements selected is also crucial to the success of these works. This problem may be handled by using Convolutional Neural Networks for automated feature learning (CNN). The research presented here illustrates two different methods for identifying infected tomato leaves. Hyper-parameter learning with an optimization technique is employed first to study the important features, and the second design employs an attention mechanism. Finally, this model is tested by identifying three diseases–leaf mould, late blight, and early diseases–and classifying them in a publicly accessible dataset called Plant Village Dataset. Data augmentation has to be created as future work to increase categorization accuracy.","PeriodicalId":280183,"journal":{"name":"2023 6th International Conference on Information Systems and Computer Networks (ISCON)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114213889","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

Prediction of Fruits and Vegetable Diseases Using Machine Learning and IoT 利用机器学习和物联网预测果蔬病害

2023 6th International Conference on Information Systems and Computer Networks (ISCON) Pub Date : 2023-03-03 DOI: 10.1109/ISCON57294.2023.10112097

Dharmendra Kumar, Kamal narayan Kamlesh, Amresh Kumar, Shilpi Banerjee, Dr. Kumar Vishal

{"title":"Prediction of Fruits and Vegetable Diseases Using Machine Learning and IoT","authors":"Dharmendra Kumar, Kamal narayan Kamlesh, Amresh Kumar, Shilpi Banerjee, Dr. Kumar Vishal","doi":"10.1109/ISCON57294.2023.10112097","DOIUrl":"https://doi.org/10.1109/ISCON57294.2023.10112097","url":null,"abstract":"Sensors and the Internet of Things (IoT) will be integral to making agriculture more sustainable and productive in the future. Most of the pressing environmental, economic, and technological problems can be solved by taking advantage of IoT, WSNs, and ICT (Information and Communications Technology). Adding more and more connected devices generates a large volume of data with various modalities. Additionally, the increase in the number of interconnected devices occurs due to geographical and temporal factors. This vast amount of data, once intelligently processed and analyzed, will provide a higher level of insights that will improve forecasting, decision making, and sensor dependency management in the future. In this article, we will cover a comprehensive overview of how machine learning algorithms can assist in the analysis of agricultural sensor data. We also discuss a prototype for an integrated food, energy, and water (FEW) system utilizing IoT data. The majority of previous papers in the literature on fruits and vegetables disease detection focused on just one type of disease. Nevertheless, this paper reviews several types of fruits and vegetables diseases.","PeriodicalId":280183,"journal":{"name":"2023 6th International Conference on Information Systems and Computer Networks (ISCON)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121561000","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

Colon Cancer Tissue Classification Using ML 使用ML进行结肠癌组织分类

2023 6th International Conference on Information Systems and Computer Networks (ISCON) Pub Date : 2023-03-03 DOI: 10.1109/ISCON57294.2023.10112181

Ashish Tripathi, Kuldeep Kumar, Anuradha Misra, B. Chaurasia

{"title":"Colon Cancer Tissue Classification Using ML","authors":"Ashish Tripathi, Kuldeep Kumar, Anuradha Misra, B. Chaurasia","doi":"10.1109/ISCON57294.2023.10112181","DOIUrl":"https://doi.org/10.1109/ISCON57294.2023.10112181","url":null,"abstract":"In this paper, the classification of colon cancer tissues by means of machine learning approaches is evaluated. In today’s world, a revolutionary advancement has come in the classification and diagnosis of diseases in the medical and healthcare sectors. Deep learning classifiers and machine learning methods are now broadly applied to accurately diagnose a number of diseases. Cancer is one of the world’s most significant roots of death, appealing to the lives of one person out of every six. As per the national library of medicine, the third leading cause of death worldwide is colorectal cancer. Identifying an illness at a premature stage increases the chances of survival. Automated diagnosis and the classification of tissues from images can be completed much more quickly with the use of artificial intelligence. A publicly available IoT dataset CRC–VAL–HE–7K consisting of 7180 images, distributed among nine types of colorectal tissues: background, lymphocytes, adipose, mucus, colorectal adenocarcinoma epithelium, normal colon mucosa, debris, cancer-associated stroma, and, smooth muscle is used after preprocessing. Feature extraction is done by applying Differential-Box-Count on all blocks of images. The dataset is evaluated by these Machine Learning (ML) procedures: K-Nearest Neighbor, Support Vector Machine, Decision Tree, Random Forest, Extreme Gradient Boosting, and Gaussian Naive Bayes. Results show that the performance of Extreme Gradient Boosting is the best and most viable approach.","PeriodicalId":280183,"journal":{"name":"2023 6th International Conference on Information Systems and Computer Networks (ISCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124379281","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

FedCER - Emotion Recognition Using 2D-CNN in Decentralized Federated Learning Environment FedCER -在分散联邦学习环境中使用2D-CNN的情绪识别

2023 6th International Conference on Information Systems and Computer Networks (ISCON) Pub Date : 2023-03-03 DOI: 10.1109/ISCON57294.2023.10112028

Manan Agrawal, M. Anwar, Rajni Jindal

{"title":"FedCER - Emotion Recognition Using 2D-CNN in Decentralized Federated Learning Environment","authors":"Manan Agrawal, M. Anwar, Rajni Jindal","doi":"10.1109/ISCON57294.2023.10112028","DOIUrl":"https://doi.org/10.1109/ISCON57294.2023.10112028","url":null,"abstract":"Emotion recognition using physiological signals has received much attention in recent literature. However, current development relies on the use of centralized datasets for training prediction models. But this approach raises a significant risk of privacy violation, especially in cases where the researchers use medically sensitive data like EEG recordings. The following paper proposes a privacy-preserving emotion recognition framework using Federated Learning. It is a decentralized method of training machine learning models. We validate our results by comparing them against a baseline model and discuss the privacy-performance trade-off in Federated Learning. Our proposed model is a convolutional neural network that works upon EEG signal recordings directly and does not rely upon extracted features from the DEAP dataset recordings of each subject. Instead, we have kept the non-IID data in the dataset intact. The proposed architecture achieves 72.22 percent, 70.10 percent, and 66.99 percent accuracy scores for the Dominance, Arousal, and Valence labels on the public DEAP dataset.","PeriodicalId":280183,"journal":{"name":"2023 6th International Conference on Information Systems and Computer Networks (ISCON)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124068184","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

A Novel Computational Model for Hardware-Level Security of Cloud Databases in Public Clouds 公共云中云数据库硬件级安全的新计算模型

2023 6th International Conference on Information Systems and Computer Networks (ISCON) Pub Date : 2023-03-03 DOI: 10.1109/ISCON57294.2023.10111989

A. Yadav, R. Bharti, R. Raw

{"title":"A Novel Computational Model for Hardware-Level Security of Cloud Databases in Public Clouds","authors":"A. Yadav, R. Bharti, R. Raw","doi":"10.1109/ISCON57294.2023.10111989","DOIUrl":"https://doi.org/10.1109/ISCON57294.2023.10111989","url":null,"abstract":"Today, all cloud-based social media applications are data-intensive, and the large amounts of data they generate are managed on the public cloud, which compromises user data security and privacy. Many government and nongovernment organizations that manage users’ sensitive and personal data do not choose to use the public cloud to administer their databases because of the numerous security risks it has. However, these organizations are jeopardizing the performance of the application by handling the user’s data on the private cloud. Without jeopardizing user data security and privacy or application performance, a security solution will encourage these organizations to manage their databases on the public cloud. This paper presents an L-PNR computational model for the hardware-level security of cloud databases in a multi-tenant public cloud environment. The L-PNR model’s performance has been simulated and evaluated using VMware vCenter Simulator. According to experimental findings, the proposed model outperforms the current models THR-RS, LR-MU, DDPA, and DSRBACA in terms of performance and security. The suggested L-PNR model may be used by cloud service providers (CSPs) to offer data security over the public cloud, where organizations can affordably manage their sensitive data.","PeriodicalId":280183,"journal":{"name":"2023 6th International Conference on Information Systems and Computer Networks (ISCON)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127700571","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

Establishing the Correlation of Powers Skills with Program success 建立权力技能与项目成功的关系

2023 6th International Conference on Information Systems and Computer Networks (ISCON) Pub Date : 2023-03-03 DOI: 10.1109/ISCON57294.2023.10112066

Nadeem Akhter, Birendra Goswami, Ekbal Rashid

引用次数: 0

A LSTM based Deep Learning Model for Smart Manufacturing 基于LSTM的智能制造深度学习模型

2023 6th International Conference on Information Systems and Computer Networks (ISCON) Pub Date : 2023-03-03 DOI: 10.1109/ISCON57294.2023.10111964

Babli Mandloi, Ghanshyam Prasad Dubey, Komal Tahiliani

引用次数: 0

Human Pose Estimation using Artificial Intelligence with Virtual Gym Tracker 基于虚拟健身跟踪器的人工智能人体姿态估计

2023 6th International Conference on Information Systems and Computer Networks (ISCON) Pub Date : 2023-03-03 DOI: 10.1109/ISCON57294.2023.10112064

Neetu Faujdar, Shipra Saraswat, Sachin Sharma

{"title":"Human Pose Estimation using Artificial Intelligence with Virtual Gym Tracker","authors":"Neetu Faujdar, Shipra Saraswat, Sachin Sharma","doi":"10.1109/ISCON57294.2023.10112064","DOIUrl":"https://doi.org/10.1109/ISCON57294.2023.10112064","url":null,"abstract":"Artificial intelligence has become essential in a wide range of industries, including the fitness industry. Human pose estimation is becoming increasingly popular. Human pose assessment can be established based on Artificial Intelligence or Machine Learning techniques, where sample data is employed in system with the help of trained models, after that place the joints of human body by video or picture. After that joints of that person’s body have been confined and further utilize in a variety of purposes, such as determining a person’s gait cycle or their subsequent motions of a specialized athlete in order to study about the physical methods and approaches for acquiring his or her achievement. One major application of Human pose valuation could be in the area of gym trainer tracker which helps in struggling gymnasts in order to accomplish their goals. Machine learning technology can aid in counting repetitions of any exercise during weightlifting or CrossFit events. Pose estimation is used to identify key points, and the angle between key points (elbow and shoulder) is measured. In this research paper, we can estimate the up and down stages in the Gym tracker based on the angle’s threshold following that, the tracker predicts all 33 position key points.","PeriodicalId":280183,"journal":{"name":"2023 6th International Conference on Information Systems and Computer Networks (ISCON)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128173954","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