Pub Date : 2022-12-14DOI: 10.1109/IC3I56241.2022.10073338
V. Patil, Prasad H Kadam, Sudhir Bussa, N. S. Bohra, A. Rao, K. Dharani
One of the most important needs of the present period is energy optimization. Energy dissipation and waste result from the current traditional power grid system’s inability to properly optimize energy and power utilization. To stop the future extinction of the nonrenewable resource that supplies energy and power, energy optimization can be very helpful. We will need to bring the idea of an intelligent distribution system into a traditional power distribution system that is smart grids through LoRa based communication system, for it to become a power optimization system. A typical power distribution system can be transformed into a smart grid system employing LoRa-based communication to maximize reliable energy and power with real-time identification and acknowledgement at every stage of distribution and consumption.
{"title":"Wireless Communication in Smart Grid using LoRa Technology","authors":"V. Patil, Prasad H Kadam, Sudhir Bussa, N. S. Bohra, A. Rao, K. Dharani","doi":"10.1109/IC3I56241.2022.10073338","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10073338","url":null,"abstract":"One of the most important needs of the present period is energy optimization. Energy dissipation and waste result from the current traditional power grid system’s inability to properly optimize energy and power utilization. To stop the future extinction of the nonrenewable resource that supplies energy and power, energy optimization can be very helpful. We will need to bring the idea of an intelligent distribution system into a traditional power distribution system that is smart grids through LoRa based communication system, for it to become a power optimization system. A typical power distribution system can be transformed into a smart grid system employing LoRa-based communication to maximize reliable energy and power with real-time identification and acknowledgement at every stage of distribution and consumption.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123108589","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}
The burgeoning population of the world is directly proportional to the quantity of food produced in agricultural fields. Considering the emerging challenges in climatic conditions and inefficient agricultural production. It is imperative to smart our agricultural practises. One of the ways to achieve this, is with the implementation of AI with IoT and Cloud Computing techniques. It is very pertinent to equip our agricultural practises with the AI with IoT and cloud computing techniques so as to magnify the qualitative and quantitative food production and aid the world population. This review paper provides a brief overview of implantation of AI with IoT and cloud computing techniques in the agriculture practices which in turn will make the agricultural practises smart and efficiently equipped to meet the growing food production requirements.
{"title":"AI, IoT and Cloud Computing Based Smart Agriculture","authors":"Shaktija Singh Baghel, Poonam Rawat, Rajesh Singh, S. Akram, Shweta Pandey, AishwaryVikram Singh Baghel","doi":"10.1109/IC3I56241.2022.10072567","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10072567","url":null,"abstract":"The burgeoning population of the world is directly proportional to the quantity of food produced in agricultural fields. Considering the emerging challenges in climatic conditions and inefficient agricultural production. It is imperative to smart our agricultural practises. One of the ways to achieve this, is with the implementation of AI with IoT and Cloud Computing techniques. It is very pertinent to equip our agricultural practises with the AI with IoT and cloud computing techniques so as to magnify the qualitative and quantitative food production and aid the world population. This review paper provides a brief overview of implantation of AI with IoT and cloud computing techniques in the agriculture practices which in turn will make the agricultural practises smart and efficiently equipped to meet the growing food production requirements.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"118 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123100803","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 : 2022-12-14DOI: 10.1109/IC3I56241.2022.10073060
Mizan Ali Khan, Abhishek Sharma
K-nearest Neighbor (KNN) is one of the most widely used ML (Machine Learning) methods for data includes organizational, and categorizing illnesses and faults. This is important due to frequent changes in the training sample, for which it would be costly to create a new classifier using most methods each time. As a result, KNN may be employed successfully since it does not need the creation of a residual classifier in before. KNN is simple to use and has a wide range of application possibilities. Here, an unique KNN classification method is proposed that optimizes utilizing the Bayesian Optimization Algorithm (BOA). In order to exploit knowledge about the dataset’s architecture and the cosine similarity of distance, this study proposes changes to the closest neighbour K value in an effort to improve classification accuracy. The results of experimental work based on datasets from the University of California Irvine (UCI) repository indicate enhanced classifier performance relative to traditional KNN and increased reliability without a substantial speed penalty.
k -最近邻(KNN)是最广泛使用的ML(机器学习)方法之一，用于数据包括组织和分类疾病和故障。这一点很重要，因为训练样本经常发生变化，因此每次使用大多数方法创建新分类器的成本都很高。因此，KNN可以被成功使用，因为它不需要在之前创建残差分类器。KNN使用简单，具有广泛的应用可能性。本文提出了一种独特的利用贝叶斯优化算法(BOA)进行优化的KNN分类方法。为了利用数据集的结构知识和距离的余弦相似度，本研究提出改变最近邻K值以提高分类精度。基于加州大学欧文分校(UCI)存储库的数据集的实验结果表明，相对于传统的KNN，分类器性能得到了增强，可靠性得到了提高，而速度却没有大幅下降。
{"title":"Implementation of KNN Algorithm with BOA to Predict the Cancer with more Accurate Way","authors":"Mizan Ali Khan, Abhishek Sharma","doi":"10.1109/IC3I56241.2022.10073060","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10073060","url":null,"abstract":"K-nearest Neighbor (KNN) is one of the most widely used ML (Machine Learning) methods for data includes organizational, and categorizing illnesses and faults. This is important due to frequent changes in the training sample, for which it would be costly to create a new classifier using most methods each time. As a result, KNN may be employed successfully since it does not need the creation of a residual classifier in before. KNN is simple to use and has a wide range of application possibilities. Here, an unique KNN classification method is proposed that optimizes utilizing the Bayesian Optimization Algorithm (BOA). In order to exploit knowledge about the dataset’s architecture and the cosine similarity of distance, this study proposes changes to the closest neighbour K value in an effort to improve classification accuracy. The results of experimental work based on datasets from the University of California Irvine (UCI) repository indicate enhanced classifier performance relative to traditional KNN and increased reliability without a substantial speed penalty.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124284907","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 : 2022-12-14DOI: 10.1109/IC3I56241.2022.10072785
T. Anitha, S. Sridhar
The main aim of the research is to achieve packet transmission steadiness for any abnormal condition and to detect the invalid path in WSN, a novel improved communication steadiness routing (ICSR) over Self-organized Tree-Based Energy Balance Routing (STB). Materials and Methods: ICSR and STB are implemented in this research work to increase path stability and minimize energy consumption in order to improve communication. Sample size is calculated using G power software and determined as 10 per group with pretest power 80%, threshold 0.05% and CI 95%. Results: To discover the incorrect path, a void path identification technique is used. If a path is not kept active for an extended period of time, nodes in the route may miss data packets while communicating to each other. As a result, an energy-efficient stable way for routing is required to minimize energy consumption and increase path stability. Path stability, energy consumption, network overhead, packet delivery ratio, network lifetime, and end to end delay are the metrics used to measure the performance of ICSR and STB models in different study groups with p<0.05. ICSR provides a higher of 94.05% compared to STB with 78.06% in minimizing energy consumption and increasing path stability. The significant value is 0.007 (P<0.05), which shows that two groups are statistically significant. Conclusion: The ICSR routing model’s performance is compared with the Self-organized Tree-Based Energy Balance Routing Protocol (STB) model. From the results, it is clear that ICSR outperforms the STB model in all the parameters.
{"title":"Analysis of a Wireless Sensor Network’s Performance using Novel Improved Communication Steadiness Routing over Self-organized Tree-Based Energy Balance Routing","authors":"T. Anitha, S. Sridhar","doi":"10.1109/IC3I56241.2022.10072785","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10072785","url":null,"abstract":"The main aim of the research is to achieve packet transmission steadiness for any abnormal condition and to detect the invalid path in WSN, a novel improved communication steadiness routing (ICSR) over Self-organized Tree-Based Energy Balance Routing (STB). Materials and Methods: ICSR and STB are implemented in this research work to increase path stability and minimize energy consumption in order to improve communication. Sample size is calculated using G power software and determined as 10 per group with pretest power 80%, threshold 0.05% and CI 95%. Results: To discover the incorrect path, a void path identification technique is used. If a path is not kept active for an extended period of time, nodes in the route may miss data packets while communicating to each other. As a result, an energy-efficient stable way for routing is required to minimize energy consumption and increase path stability. Path stability, energy consumption, network overhead, packet delivery ratio, network lifetime, and end to end delay are the metrics used to measure the performance of ICSR and STB models in different study groups with p<0.05. ICSR provides a higher of 94.05% compared to STB with 78.06% in minimizing energy consumption and increasing path stability. The significant value is 0.007 (P<0.05), which shows that two groups are statistically significant. Conclusion: The ICSR routing model’s performance is compared with the Self-organized Tree-Based Energy Balance Routing Protocol (STB) model. From the results, it is clear that ICSR outperforms the STB model in all the parameters.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116683713","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 : 2022-12-14DOI: 10.1109/IC3I56241.2022.10072488
Ashutosh Kumar Singh, Anoop Kumar
The Internet of Things (IoT) necessitates a new processing paradigm that incorporates cloud scalability while reducing network latency by utilising resources closer to the network edge. On the one hand, it’s difficult to achieve such flexibility within the edge-to-cloud continuum, which consists of a distributed networked ecosystem of heterogeneous computing resources. IoT traffic dynamics, on the other hand, and the growing need for low-latency services necessitate decreasing reaction time and balancing service location. For cost-effective system administration and operations, fog computing load-balancing will become a cornerstone. Though virtualization attempts to instantaneously balance the load of the overall network, there’s still the possibility of capacity excessive usage or under development. Heavily loaded systems degrade efficiency, while undercharged systems use bandwidth inefficiently. Because of inadequate load distribution, overburdened systems emit additional energy, driving up the cost of coolers as well as adding significantly to the warming of the planet. Throughout most situations, cooling towers consume higher electricity than core IT technology. Despite the benefits of cloud computing as a distributed pool of resources and services, certain new IoT applications are not cloud-ready. Wind farms and smart traffic light systems, for example, have unique characteristics and requirements “(e.g., large-scale, geo-distribution) (e.g., very low and predictable latency)”. This research paper has considered secondary method of data collection to gather relevant and statistical data related to research topic.
{"title":"Task Scheduling and Load Balancing for Minimization of Response Time in IoT Assisted Cloud Environments","authors":"Ashutosh Kumar Singh, Anoop Kumar","doi":"10.1109/IC3I56241.2022.10072488","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10072488","url":null,"abstract":"The Internet of Things (IoT) necessitates a new processing paradigm that incorporates cloud scalability while reducing network latency by utilising resources closer to the network edge. On the one hand, it’s difficult to achieve such flexibility within the edge-to-cloud continuum, which consists of a distributed networked ecosystem of heterogeneous computing resources. IoT traffic dynamics, on the other hand, and the growing need for low-latency services necessitate decreasing reaction time and balancing service location. For cost-effective system administration and operations, fog computing load-balancing will become a cornerstone. Though virtualization attempts to instantaneously balance the load of the overall network, there’s still the possibility of capacity excessive usage or under development. Heavily loaded systems degrade efficiency, while undercharged systems use bandwidth inefficiently. Because of inadequate load distribution, overburdened systems emit additional energy, driving up the cost of coolers as well as adding significantly to the warming of the planet. Throughout most situations, cooling towers consume higher electricity than core IT technology. Despite the benefits of cloud computing as a distributed pool of resources and services, certain new IoT applications are not cloud-ready. Wind farms and smart traffic light systems, for example, have unique characteristics and requirements “(e.g., large-scale, geo-distribution) (e.g., very low and predictable latency)”. This research paper has considered secondary method of data collection to gather relevant and statistical data related to research topic.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127101376","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 : 2022-12-14DOI: 10.1109/IC3I56241.2022.10073127
Naru Venkata Pavan Saish, J. Vijayashree
X-rays have been the best support for medical research to make better diagnoses that help in predicting the type of disease. Several machines capture X-ray images of different body parts like the Lungs, Teeth, hands, legs, etc. The role of X-ray images came up in medical research and became very important in diagnosing the health condition of a lung X-ray. In this paper, we propose a new pooling layer before sending the image into the dense neural network by considering the lung X-rays dataset where normal and pneumonia images are taken and using the convolutional neural network (CNN) we determine the condition of the X-ray and classify them into a Normal or Pneumonia. We evaluated our model using a confusion matrix, noted the metrics of precision and recall scores, and compared them with existing models. This paper explains the CNN algorithm deeply and tries to confirm that: (I) X-ray pictures of diseased lungs can be classified using deep learning techniques if the training data is substantial. (II) Adding the average pool layer at the end of the architecture can get better results than many standard existing models. (III) Hyperparameter tuning can improve the deep learning model accuracies and helps the model to perform better. (IV) With a proper amount of training, hyperparameter tweaking, and using data augmentation we can be able to get better accuracy than many existing CNN models with the lowest number of trainable parameters. This makes it possible to accurately automate the process of interpreting X-ray images that could avoid deep MRI and CT scans which may affect patients with high radioactive waves.
{"title":"Image Classification of Lung X-ray Images using Deep learning","authors":"Naru Venkata Pavan Saish, J. Vijayashree","doi":"10.1109/IC3I56241.2022.10073127","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10073127","url":null,"abstract":"X-rays have been the best support for medical research to make better diagnoses that help in predicting the type of disease. Several machines capture X-ray images of different body parts like the Lungs, Teeth, hands, legs, etc. The role of X-ray images came up in medical research and became very important in diagnosing the health condition of a lung X-ray. In this paper, we propose a new pooling layer before sending the image into the dense neural network by considering the lung X-rays dataset where normal and pneumonia images are taken and using the convolutional neural network (CNN) we determine the condition of the X-ray and classify them into a Normal or Pneumonia. We evaluated our model using a confusion matrix, noted the metrics of precision and recall scores, and compared them with existing models. This paper explains the CNN algorithm deeply and tries to confirm that: (I) X-ray pictures of diseased lungs can be classified using deep learning techniques if the training data is substantial. (II) Adding the average pool layer at the end of the architecture can get better results than many standard existing models. (III) Hyperparameter tuning can improve the deep learning model accuracies and helps the model to perform better. (IV) With a proper amount of training, hyperparameter tweaking, and using data augmentation we can be able to get better accuracy than many existing CNN models with the lowest number of trainable parameters. This makes it possible to accurately automate the process of interpreting X-ray images that could avoid deep MRI and CT scans which may affect patients with high radioactive waves.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124949860","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 : 2022-12-14DOI: 10.1109/IC3I56241.2022.10072946
Preeti, Chhavi Rana
The health care sector is focusing on in-home health care services, where the patients can receive medical care in the privacy of their own home. A patient in a rural region can use a remote health monitoring system to communicate with a doctor in a city who is in a larger city. Machine learning has been used for smart health monitoring systems. They used a wearable sensor to identify a set of five parameters, including Electrocardiogram (ECG), pulse rate, pressure, temperature, and position detection. The technology uses machine learning algorithms to identify doctors for consultation and to identify and predict ailments. In the study, IoT technology and health monitoring have been coupled to give more personalized and responsive health care. The primary purpose of the system is to monitor patients' vital signs in real-time monitoring. The authorized individual can access the patient' s vital signs from their smartphone or PC using a cloud server. The Decision Tree (DT) attained the best accuracy of 99.1 percent after testing the suggested model, which is promising for their purposes. It is observed that the DT achieves best accuracy, while Random Forest is the second-best classifier for this problem.
{"title":"IoT and Cloud Based health monitoring system Using Machine learning","authors":"Preeti, Chhavi Rana","doi":"10.1109/IC3I56241.2022.10072946","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10072946","url":null,"abstract":"The health care sector is focusing on in-home health care services, where the patients can receive medical care in the privacy of their own home. A patient in a rural region can use a remote health monitoring system to communicate with a doctor in a city who is in a larger city. Machine learning has been used for smart health monitoring systems. They used a wearable sensor to identify a set of five parameters, including Electrocardiogram (ECG), pulse rate, pressure, temperature, and position detection. The technology uses machine learning algorithms to identify doctors for consultation and to identify and predict ailments. In the study, IoT technology and health monitoring have been coupled to give more personalized and responsive health care. The primary purpose of the system is to monitor patients' vital signs in real-time monitoring. The authorized individual can access the patient' s vital signs from their smartphone or PC using a cloud server. The Decision Tree (DT) attained the best accuracy of 99.1 percent after testing the suggested model, which is promising for their purposes. It is observed that the DT achieves best accuracy, while Random Forest is the second-best classifier for this problem.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123252038","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 : 2022-12-14DOI: 10.1109/IC3I56241.2022.10072626
Shashikant Suman, P. Kaushik, Sai Sri Nandan Challapalli, B. P. Lohani, Pradeep Kushwaha, A. Gupta
Commodity markets are physical or virtual marketplaces where market players meet to buy or sell positions in commodities such as crude oil, gold, copper, silver, cotton, and wheat. People invest their hard-earned money based on some predictions to gain some profit from commodity market. Although, traditional methods such as technical analysis & fundamental analysis are very popular among traders, they are not as accurate as analysis by long short-term memory (LSTM) algorithm. In this paper, we have developed a model of well-known efficient LSTM algorithm to predict the commodity market price by utilizing a freely accessible dataset for commodity markets having open, high, low, and closing prices from historical data.
{"title":"Commodity Price Prediction for making informed Decisions while trading using Long Short-Term Memory (LSTM) Algorithm","authors":"Shashikant Suman, P. Kaushik, Sai Sri Nandan Challapalli, B. P. Lohani, Pradeep Kushwaha, A. Gupta","doi":"10.1109/IC3I56241.2022.10072626","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10072626","url":null,"abstract":"Commodity markets are physical or virtual marketplaces where market players meet to buy or sell positions in commodities such as crude oil, gold, copper, silver, cotton, and wheat. People invest their hard-earned money based on some predictions to gain some profit from commodity market. Although, traditional methods such as technical analysis & fundamental analysis are very popular among traders, they are not as accurate as analysis by long short-term memory (LSTM) algorithm. In this paper, we have developed a model of well-known efficient LSTM algorithm to predict the commodity market price by utilizing a freely accessible dataset for commodity markets having open, high, low, and closing prices from historical data.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123272329","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}
Network architectures are improving, as are the environments they serve. Recently, there has been a lot of research interest in Software-Defined Networking. The reason of its attention is because of its flexibility, manageability, scalability. And its affects can’t be avoided throughout the networking world. As well as the advantages of the visibility of the network are discussed. In SDN, all network traffic patterns are managed by a centralized network that manages, secures, and optimizes network resources. In this literature, most recent approaches centered on changing SDN architecture using blockchain technology. Blockchain is now seen as one of the key developments in technology. In order to protect their exchanges, many applications may rely on the blockchain. Since the launch of blockchain technology, many security flaws have been eliminated. SDN based on Blockchain has the potential to change the lifestyle of people in Some field will continue to have an impact in many places because of its great influence on many businesses or sectors, and what it can do. While blockchain technologies can provide us with more reliable and convenient features, Services, safety issues, and concerns are also important topics to consider in this innovative approach. A collection of conclusions and conclusions, by categorizing the current work, Proposals for potential directions for study are discussed.
{"title":"Data Plane Layer Modification of SDN Architecture with The Help of Blockchain","authors":"Taiwo Soewu, Md Abu Hanif, Manik Rakhra, Harpreet Kaur, Dalwinder Singh","doi":"10.1109/IC3I56241.2022.10073119","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10073119","url":null,"abstract":"Network architectures are improving, as are the environments they serve. Recently, there has been a lot of research interest in Software-Defined Networking. The reason of its attention is because of its flexibility, manageability, scalability. And its affects can’t be avoided throughout the networking world. As well as the advantages of the visibility of the network are discussed. In SDN, all network traffic patterns are managed by a centralized network that manages, secures, and optimizes network resources. In this literature, most recent approaches centered on changing SDN architecture using blockchain technology. Blockchain is now seen as one of the key developments in technology. In order to protect their exchanges, many applications may rely on the blockchain. Since the launch of blockchain technology, many security flaws have been eliminated. SDN based on Blockchain has the potential to change the lifestyle of people in Some field will continue to have an impact in many places because of its great influence on many businesses or sectors, and what it can do. While blockchain technologies can provide us with more reliable and convenient features, Services, safety issues, and concerns are also important topics to consider in this innovative approach. A collection of conclusions and conclusions, by categorizing the current work, Proposals for potential directions for study are discussed.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"445 1-2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123453298","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 : 2022-12-14DOI: 10.1109/IC3I56241.2022.10072298
S. Aggarwal, Amit Verma
Farming is the need and will consistently be. We human get by on food that is either from plant or from creature. Individuals have developed grounds and breed creatures to acquire nourishment for their endurance since old occasions. This training, known as farming, has advanced after a long haul and reformist cycle, going from Agriculture 1.0 to 4.0. As indicated by the UN Food and Agriculture Organization (FAO) 800 million individuals experiencing hunger and around 8% (650 million) of total populace will be as yet undernourished by 2030. Likewise horticulture portion of worldwide GDP has recently contracted 3% just and we should create 70% more food by 2050. Without a doubt, high requests for food from the overall developing populace are affecting the climate and putting numerous weights on horticultural efficiency. The customary methodology of the horticulture is going through a principal change. Throughout the last numerous long stretches of our set of experiences we have built up a few types of farming, 1.0, 2.0 and now moving towards 3.0 and 4.0 in coming many years. Indeed, today these manifestations are being rehearsed in different places on earth. This paper describes that how the transformation from Agriculture 1.0 to Agriculture 4.0 takes place and the changes in the practices of the agriculture from ancient times to present.
{"title":"Transformations in The Ways of Improving from Agriculture 1.0 to 4.0","authors":"S. Aggarwal, Amit Verma","doi":"10.1109/IC3I56241.2022.10072298","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10072298","url":null,"abstract":"Farming is the need and will consistently be. We human get by on food that is either from plant or from creature. Individuals have developed grounds and breed creatures to acquire nourishment for their endurance since old occasions. This training, known as farming, has advanced after a long haul and reformist cycle, going from Agriculture 1.0 to 4.0. As indicated by the UN Food and Agriculture Organization (FAO) 800 million individuals experiencing hunger and around 8% (650 million) of total populace will be as yet undernourished by 2030. Likewise horticulture portion of worldwide GDP has recently contracted 3% just and we should create 70% more food by 2050. Without a doubt, high requests for food from the overall developing populace are affecting the climate and putting numerous weights on horticultural efficiency. The customary methodology of the horticulture is going through a principal change. Throughout the last numerous long stretches of our set of experiences we have built up a few types of farming, 1.0, 2.0 and now moving towards 3.0 and 4.0 in coming many years. Indeed, today these manifestations are being rehearsed in different places on earth. This paper describes that how the transformation from Agriculture 1.0 to Agriculture 4.0 takes place and the changes in the practices of the agriculture from ancient times to present.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115044900","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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