Data Analytics and Artificial Intelligence最新文献_第5页

A Study on Mixed Inverse Center-Smooth Set of Some Graphs and its application 若干图的混合逆中心光滑集及其应用研究

Data Analytics and Artificial Intelligence Pub Date : 2023-02-01 DOI: 10.46632/daai/3/2/32

引用次数: 0

Malware detection in IOMT (MDI) using RNN-LSTM 基于RNN-LSTM的IOMT (MDI)恶意软件检测

Data Analytics and Artificial Intelligence Pub Date : 2023-02-01 DOI: 10.46632/daai/3/2/19

M. Uma Maheshwari, M. Suguna

{"title":"Malware detection in IOMT (MDI) using RNN-LSTM","authors":"M. Uma Maheshwari, M. Suguna","doi":"10.46632/daai/3/2/19","DOIUrl":"https://doi.org/10.46632/daai/3/2/19","url":null,"abstract":"The Internet of Things (IoT) has recently emerged as a cutting-edge technology for creating smart environments. The Internet of Things (IoT) connects systems, applications, data storage, and services, which may be a new entry point for cyber-attacks as they provide continuous services in the organization. At the current time, software piracy and malware attacks pose significant threats to IoT security. These threats may grab vital information, causing economic and reputational harm. The Internet of Medical Things (IoMT) is a subset of the Internet of Things in which medical equipment exchanges highly confidential with one another. These advancements allow the healthcare industry to maintain a higher level of touch and care for its patients. Security is viewed as a significant challenge in any technology's reliance on the IoT. Remote hijacking, impersonation, denial of service attacks, password guessing, and man-in-the-middle are all security concerns. Critical data associated with IoT connectivity may be revealed, altered, or even rendered inaccessible to authenticated persons in the event of such attacks. the deep recurrent neural network is used to detect malicious infections in IoT networks by displaying color images. In this paper, we propose a method for detecting cyber-attacks on IoMT systems that tends to make use of innovative deep learning. Specifically, our method incorporates a set of long short-term memory (LSTM) modules into a detector ensemble using a recurrent neural network.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126970254","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

Skin Disease Prediction Machine Learning Model Using Ensemble Classifier with PCA 基于PCA集成分类器的皮肤病预测机器学习模型

Data Analytics and Artificial Intelligence Pub Date : 2023-02-01 DOI: 10.46632/daai/3/2/31

{"title":"Skin Disease Prediction Machine Learning Model Using Ensemble Classifier with PCA","authors":"","doi":"10.46632/daai/3/2/31","DOIUrl":"https://doi.org/10.46632/daai/3/2/31","url":null,"abstract":"In the medical era, skin disease is considered one of the most common diseases among humans. Skin cancer is the most dangerous type, which can be curable if identified at the initial stage. The severity of skin cancer and the rapid count of affected people make it necessary to introduce an automatic detection scheme. Generally, analyzing and identifying skin disease in a short time is the most complex and challenging task. Several deep learning (DL) and machine learning (ML) are introduced to achieve this. However, the still fulfilling the skin cancer diagnosis is not accomplished completely. To achieve this, we proposed a machine learning model using an ensemble classifier with PCA to predict skin disease with maximum accuracy. The proposed Ensemble classifier is based on similar features and classifies several stages. It is executed by labeling vertebral disorder images according to these statistical features. The performance obtained by the ensemble classifier is compared with Support Vector Machine (SVM) and Resent with several evaluation metrics. The analysis shows that the accuracy attained by the proposed ensemble classifier is 97 % which is far better than the others in terms of classification and accuracy.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122493974","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

Object Recognition in Earth Surface Satellite Images Using Digital Image Processing and Machine Learning Techniques with Big Data Technologies 基于大数据技术的数字图像处理和机器学习技术在地表卫星图像中的目标识别

Data Analytics and Artificial Intelligence Pub Date : 2023-02-01 DOI: 10.46632/daai/3/2/27

Misba Khan k

引用次数: 0

Certain Iterative Methods to Solve System of Equations by Python Programming 用 Python 编程求解方程组的某些迭代法

Data Analytics and Artificial Intelligence Pub Date : 2023-02-01 DOI: 10.46632/daai/3/2/37

引用次数: 0

Block Chain for IOT Security Using Consensus Algorithms 使用共识算法的物联网安全区块链

Data Analytics and Artificial Intelligence Pub Date : 2023-02-01 DOI: 10.46632/daai/3/2/16

P. Kalpana, I. Anusha Prem

{"title":"Block Chain for IOT Security Using Consensus Algorithms","authors":"P. Kalpana, I. Anusha Prem","doi":"10.46632/daai/3/2/16","DOIUrl":"https://doi.org/10.46632/daai/3/2/16","url":null,"abstract":"The first distributed recordkeeping system with a built-in trust structure is the block chain. It creates a dependable architecture for decentralized control through information redundancy across multiple nodes. Based on this, this study suggests a minimal block chain-based IoT information exchange security framework. The framework uses a double-chain approach that combines the data block chain and the transaction block chain. Distributed storage and tamper-proof data are implemented in the data block chain, and the consensus process is improved using the improved practical Byzantine fault-tolerant (PBFT) mechanism. Data registration efficiency, resource and data transfers, and privacy protection are all enhanced by better partial blind signature-based algorithms in the transaction block chain. This article focuses on how well the consensus algorithms employed in a block chain system for the Internet of Things perform (IoT). Such systems' time requirements to accomplish. Consensus ought to be minimal. The three most popular consensus algorithms—modified proof of work, realistic byzantine fault tolerance, and binary consensus—are assessed under various conditions, including mote type, number of participating nodes, and radio propagation model. To enable an IoT node to switch between different consensus algorithms, a comprehensive solution is put forward. The Contiki IoT operating system simulations display strong performance (time to achieve consensus less than seconds)","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129371398","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

Prediction of Unemployment In India Using Fb Prophet 用Fb预言家预测印度失业率

Data Analytics and Artificial Intelligence Pub Date : 2023-02-01 DOI: 10.46632/daai/3/1/5

引用次数: 0

Public Web Chat Application Monitering System 公共网络聊天应用监控系统

Data Analytics and Artificial Intelligence Pub Date : 2023-02-01 DOI: 10.46632/daai/3/1/1

引用次数: 0

Voice Control Hot-Cold Water Dispenser System Using Arduino 基于Arduino的冷热水机语音控制系统

Data Analytics and Artificial Intelligence Pub Date : 2023-02-01 DOI: 10.46632/daai/3/1/7

{"title":"Voice Control Hot-Cold Water Dispenser System Using Arduino","authors":"","doi":"10.46632/daai/3/1/7","DOIUrl":"https://doi.org/10.46632/daai/3/1/7","url":null,"abstract":"The Technology is transforming human life into a smart world due to its rapid expansion. To make people's lives easier, smart sensors connected to physical items give data. We show a case study of a smart water dispenser that uses weight sensors, temperature sensors, and Arduino to track how much water customers and water bottle suppliers use on a daily basis. When the water in the dispenser is ready to run out, the smart water dispenser weighs the water that is still within and sends out a warning. It takes the temperature and sends the user notifications regarding water use. Here, we propose an Arduino and Relay-based completely automated RFID-based water dispenser system. Using solenoid tap and sensors, the device may fully automate the water distribution process. In order to prevent water deterioration if no glass is placed at the counter panel, the system also detects the presence of glass there. Infrared (IR) sensors are used by the system to identify glass, after which the sensors provide a signal to the microcontroller. Now that the sensors have provided information, the microcontroller is processing it to see if glass is present. The system features an RFID Reader that may be used to read specific tags and provide information about valid tags to the microcontroller. When a valid tag is found, the system now sends a signal to the controller, which then determines whether glass is there before starting the motor to pour water into the glass while the glass is still there. If glass is removed while the process is running, the mechanism shuts off the water flow until glass is found. So, in this article, we propose a smart water dispenser system with a water-saving feature.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125459883","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

Deep Neural Certificate less Hessian Heap Sign cryption for Secure Data Transmission in Wireless Network 面向无线网络数据安全传输的深度神经证书无黑森堆签名加密

Data Analytics and Artificial Intelligence Pub Date : 2023-02-01 DOI: 10.46632/daai/3/2/23

N. Shoba, V. Sathya

{"title":"Deep Neural Certificate less Hessian Heap Sign cryption for Secure Data Transmission in Wireless Network","authors":"N. Shoba, V. Sathya","doi":"10.46632/daai/3/2/23","DOIUrl":"https://doi.org/10.46632/daai/3/2/23","url":null,"abstract":"Systematic and well grounded data transmission over wireless networks has been substance of uninterrupted research over the last few years. The paramount is scrutinizing the amount of security provisioning owing to the security challenges during transmission over wireless network. In fact, it is moderate to eavesdrop and alter data packets. Accessing the personal computer and public network possess the potentiality to apprehend the network traffic possibly compromising the privacy. Therefore for wireless applications, it is essential to ensure data integrity during data transmission. To efficiently address the above issues, a Deep Neural Certificate less Hessian Curve Heap Sign cryption (DNC-HCHS) method for secured data transmission in wireless network is proposed. Compared with the conventional, Certificate less Sign cryption DNC-HCHS method improves the data confidentiality and data integrity by generating smaller keys employing the Hessian Curve Heap function. Additionally with the assistance of the access point or the aggregator, the sensitivity of heaped sign crypted cipher text can improve the security of data transmission and reduce the message delivery time. Aimed at reducing the delay in data transmission, application of Certificate less Hessian Curve Heap Sign cryption in Deep Learning (i.e., Deep Neural Network) performs the overall process in a swift manner and performs a much better encryption. Simulation is performed to validate the viability and efficiency of the proposed method. The results show that the data confidentiality and data integrity rate are strongly improved, while the delay is minimized.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126923587","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