2021 2nd International Conference on Intelligent Engineering and Management (ICIEM)最新文献_第8页

Face Detection for Real World Application 人脸检测在现实世界中的应用

2021 2nd International Conference on Intelligent Engineering and Management (ICIEM) Pub Date : 2021-04-28 DOI: 10.1109/ICIEM51511.2021.9445287

D. Brar, Amit Kumar, Pallavi, Usha Mittal, Pooja Rana

引用次数: 3

Analytical Review of Data Security in Cloud Computing 云计算中的数据安全分析综述

2021 2nd International Conference on Intelligent Engineering and Management (ICIEM) Pub Date : 2021-04-28 DOI: 10.1109/ICIEM51511.2021.9445355

Mukesh Joshi, S. Budhani, Naveen Tewari, Satyam Prakash

引用次数: 11

Comparative study of different denoising techniques on Brain NCCT images 脑NCCT图像不同去噪技术的比较研究

2021 2nd International Conference on Intelligent Engineering and Management (ICIEM) Pub Date : 2021-04-28 DOI: 10.1109/ICIEM51511.2021.9445309

Simarjeet Kaur, Jimmy Singla, Nikita Nikita

引用次数: 2

Precision Pig Farming Image Analysis Using Random Forest and Boruta Predictive Big Data Analysis Using Neural Network and K- Nearest Neighbor 基于随机森林的精准养猪图像分析和基于神经网络和K近邻的Boruta预测大数据分析

2021 2nd International Conference on Intelligent Engineering and Management (ICIEM) Pub Date : 2021-04-28 DOI: 10.1109/ICIEM51511.2021.9445328

S. A. Shaik Mazhar, G. Suseendran

{"title":"Precision Pig Farming Image Analysis Using Random Forest and Boruta Predictive Big Data Analysis Using Neural Network and K- Nearest Neighbor","authors":"S. A. Shaik Mazhar, G. Suseendran","doi":"10.1109/ICIEM51511.2021.9445328","DOIUrl":"https://doi.org/10.1109/ICIEM51511.2021.9445328","url":null,"abstract":"Conditions and monitoring for production are significant issues in livestock accuracy Agriculture, in which image measurement and smart data collection are required. Dynamical surveillance and review of this Article Man are suggested as a device for scientific identification and growth evaluation of pigs. The Watershed enhanced algorithm is adapted to each human animal's section in chronic occlusion, depending on the depth of the photos captured during flight Camera in the chosen area of interest. For swine's weight, the rate of development is calculated from the image-based calculations and predicted using a segmented linear fitting form. Related results will then be used to interpret and explain incidents. As real-time feedback to the farmers, it happens in the pig hen. Preliminary studies have demonstrated a high potential for precision farming methods for livestock farming to increase efficiency and animal health. In this paper, Machine Learning is used in IMAGE analysis using Random forest and boruta with Predictive Big Data analysis on the pig farming data using the neural network and k- nearest neighbor algorithm for advanced predictive data analysis of our pig farming agriculture.","PeriodicalId":264094,"journal":{"name":"2021 2nd International Conference on Intelligent Engineering and Management (ICIEM)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125714686","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

Beyond 5G: Reinventing Network Architecture With 6G 超越5G:用6G重塑网络架构

2021 2nd International Conference on Intelligent Engineering and Management (ICIEM) Pub Date : 2021-04-28 DOI: 10.1109/ICIEM51511.2021.9445274

Zahid A. Bhat, Hamza Mushtaq, Jameel A. Mantoo, V. S. Yadav, Akash Kumar Shrivastava, Swati Swati

引用次数: 1

Ensemble method to predict impact of student intelligent quotient and academic achievement on placement 集成方法预测学生智商和学业成绩对分班的影响

2021 2nd International Conference on Intelligent Engineering and Management (ICIEM) Pub Date : 2021-04-28 DOI: 10.1109/ICIEM51511.2021.9445323

Kanika Thakur, K. Lal, Vinay Kumar

{"title":"Ensemble method to predict impact of student intelligent quotient and academic achievement on placement","authors":"Kanika Thakur, K. Lal, Vinay Kumar","doi":"10.1109/ICIEM51511.2021.9445323","DOIUrl":"https://doi.org/10.1109/ICIEM51511.2021.9445323","url":null,"abstract":"The study's aim is to see how academic achievement and student Intelligence Quotient influence placement. This paper will attempt to predict whether a student's intelligence quotient or academic score plays a significant role in placement. On a dataset of 193 students, we used a machine learning algorithm to compare the impact of student intelligence, behavior, and academic achievement on placement. We have used a Voting Classifier architecture to predict and classify the probability of a student being placed or not. The motivating force behind this research was to figure out why a group of students scoring the same marks in the same branch studying under the supervision of the same faculty are not able to fulfill the demands of an organization in order to be employed. The aim of this research was to combine conceptually different machine learning classifiers and predict the probability of a student being hired using a majority vote or the average expected probabilities. A classifier like this can be useful for balancing out the weaknesses of a group of models that are all performing well. Experiments show that student intelligence and attitude play a significant role in the recruiting process.","PeriodicalId":264094,"journal":{"name":"2021 2nd International Conference on Intelligent Engineering and Management (ICIEM)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133761857","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

Dynamic Key Management Scheme for Clustered Sensor Networks with Node Addition Support 支持节点添加的集群传感器网络动态密钥管理方案

2021 2nd International Conference on Intelligent Engineering and Management (ICIEM) Pub Date : 2021-04-28 DOI: 10.1109/ICIEM51511.2021.9445393

Vipin Kumar, Navneet Malik

{"title":"Dynamic Key Management Scheme for Clustered Sensor Networks with Node Addition Support","authors":"Vipin Kumar, Navneet Malik","doi":"10.1109/ICIEM51511.2021.9445393","DOIUrl":"https://doi.org/10.1109/ICIEM51511.2021.9445393","url":null,"abstract":"A sensor network is wireless with tiny nodes and widely used in various applications. To track the event and collect the data from a remote area or a hostile area sensor network is used. A WSN collects wirelessly connected tiny sensors with minimal resources like the battery, computation power, and memory. When a sensor collects data, it must be transferred to the control center through the gateway (Sink), and it must be transferred safely. For secure transfer of data in the network, the routing protocol must be safe and can use the cryptography method for authentication and confidentiality. An essential issue in WSN structure is the key management. WSN relies on the strength of the communicating devices, battery power, and sensor nodes to communicate in the wireless environment over a limited region. Due to energy and memory limitations, the construction of a fully functional network needs to be well arranged. Several techniques are available in the current literature for such key management techniques. Among the distribution of key over the network, sharing private and public keys is the most important. Network security is not an easy problem because of its limited resources, and these networks are deployed in unattended areas where they work without any human intervention. These networks are used to monitor buildings and airports, so security is always a major issue for these networks. In this paper, we proposed a dynamic key management scheme for the clustered sensor network that also supports the addition of a new node in the network later. Keys are dynamically generated and securely distributed to communication parties with the help of a cluster head. We verify the immunity of the scheme against various attacks like replay attack and node captured attacker. A simulation study was also done on energy consumption for key setup and refreshed the keys. Security analysis of scheme shows batter resiliency against node capture attack.","PeriodicalId":264094,"journal":{"name":"2021 2nd International Conference on Intelligent Engineering and Management (ICIEM)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131441251","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: 3

Technology Progress and Availability Effect on Hungarian and Indian Student for Real-Time 技术进步和可用性对匈牙利和印度学生的实时影响

2021 2nd International Conference on Intelligent Engineering and Management (ICIEM) Pub Date : 2021-04-28 DOI: 10.1109/ICIEM51511.2021.9445369

C. Verma, Z. Illés, Veronika Stoffová

引用次数: 0

Face Detection System for Health Care Units Using Raspberry PI 使用树莓派的医疗单位人脸检测系统

2021 2nd International Conference on Intelligent Engineering and Management (ICIEM) Pub Date : 2021-04-28 DOI: 10.1109/ICIEM51511.2021.9445367

Pronaya Bhattacharya, Anand Handa, Mohd. Zuhair

引用次数: 1

Application of Machine Learning in Precision Agriculture using IoT 机器学习在物联网精准农业中的应用

2021 2nd International Conference on Intelligent Engineering and Management (ICIEM) Pub Date : 2021-04-28 DOI: 10.1109/ICIEM51511.2021.9445312

Sharvane Murlidharan, V. Shukla, A. Chaubey

{"title":"Application of Machine Learning in Precision Agriculture using IoT","authors":"Sharvane Murlidharan, V. Shukla, A. Chaubey","doi":"10.1109/ICIEM51511.2021.9445312","DOIUrl":"https://doi.org/10.1109/ICIEM51511.2021.9445312","url":null,"abstract":"The demand for food has been increasing over the past six decades with the global population increase. Scientists have been finding different ways to meet this demand, such as; green revolution and genetically modified crop methods. These involve an unnatural technique to increase the yield, such as chemical fertilizers, pesticides, and modified seeds; these might be beneficial in the short term but might slowly disturb the internal body mechanism. In recent years, consumers are becoming more concerned about their food intake and prefer food with no adulteration and harmful pesticides. This has brought in the hype for a subdivision of framing, organic farming, where organic fertilizers and pesticides are used to retain the quality and nutrition values of the crop bring harvested. In organic farming, the right crop must be chosen according to the soil type and climate. This reduces the chance of pre-harvest crop losses caused by the abiotic stress in the environment, such as the soil pH levels, improper irrigation, climate, and temperature. However, when the desired conditions are provided to the crop, we can reduce the pre-harvest loss up to 35%. This paper offers a practical approach to reduce this loss by predicting what crop can be planted according to the present soil conditions and climate to prevent pre-harvest losses. The model involves a temperature and humidity sensor, a soil moisture sensor, a soil pH sensor, IoT, and a water pump under a greenhouse environment connected with the help of a development board, Raspberry pi, and machine learning techniques.","PeriodicalId":264094,"journal":{"name":"2021 2nd International Conference on Intelligent Engineering and Management (ICIEM)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130744563","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: 3