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Technical Note on computing and Fourth Industrial Revolution (C&4IR): Shaping the Future

Technical Note on Computing and Fourth Industrial Revolution...

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Proceedings of the international conference on data science, Machine Learning and Artificial Intelligence

作者： Dharm Singh Jat Colin Stanley Anicia Peters Kétévi Adiklè Assamagan Ghita Rahal Happy M. Sithole Gaurav Bajpai Namibia University of Science and Technology Namibia University of Namibia Namibia Brookhaven National Laboratory New York USA CNRS-IN2P3 Lyon France CSIR South Africa South Africa University of Rwanda Rwanda

ISBN: (纸本)9781450387637

The Third Industrial Revolution or Digital Revolution happened in the late 20th century. It referred to the extensive use of digital technologies in industrial applications, particularly the computer and communication and systems, to automate processes in the production industry through electronics and computers and the Internet. Internet of Things (IoT), term coined by Kevin Ashton in 1999, which the machine got the capability of communicating and communication. The Fourth Industrial Revolution refers to how a combination of internet-connected computing and communication technologies integrates our physical lives. These technologies include the Internet of Things, blockchain, cloud computing, edge computing, robotics, big data, and artificial intelligence (AI). The automation economy, resulting from the fourth industrial revolution (4IR) technologies, is changing the way we live and work.

关键词： 4IR

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Comparison of knn and svm algorithms to detect clinical mastitis in cows using internet of animal health things 8th

Comparison of knn and svm algorithms to detect clinical mast...

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8th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2020

作者： Ankitha, K. Manjaiah, D.H. Department of Computer Science Mangalore University MangaluruKarnataka India

ISBN: (纸本)9789811556784

The clinical mastitis is a harmful disease in cows and many researchers working on milk parameters to detect clinical mastitis. Internet of things (IoT) is a developing era of technology where every object is connected to the Internet using sensors. Sensors are an essential unit of an IoT to collect the data for analysis. The proposed method concentrates on deploying sensors on cows to monitor the health issues and will state IoT as an Internet of Animal Health Things (IoAHT). Dairy cows are an essential unit of the Indian economy because India is a top country in milk production. Clinical mastitis affects dairy cows in the production of milk. Recent studies in the dairy industry proved the use of technologies and sensors for good growth of cows. This paper reviews a method used for detecting clinical mastitis in cows and proposes a system for the same using IoAHT. The KNN and SVM algorithms are used on the primary data set to obtain a result of the detection. In comparison to these algorithms, SVM provided better results in detecting mastitis in cows. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.

关键词： Internet of things

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Application and analysis of k-means algorithms on a decision support framework for municipal solid waste management 5th

Application and analysis of k-means algorithms on a decision...

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5th international conference on Advanced Machine Learning technologies and Applications, AMLTA 2020

作者： Sharma, Narendra Litoriya, Ratnesh Sharma, Akhilesh Department of Computer Science Mewar University Chittorgarh India Department of Computer Science & Engineering Jaypee University of Engineering & Technology Guna India Department of IT Manipal University Jaipur Jaipur India

ISBN: (纸本)9789811533822

Many countries are facing the problem of sustainable solid waste management for a long time. The quantity of generating solid waste increase varies quickly as the rate of the population grows. Now, presently the government has focused on all waste segmentation levels like waste generated evolves and the technologies available to collect and process it. Many new advanced analytical techniques have been proposed for dealing with sustainable solid waste management problems. These advanced tools help the organizations to analyze municipal solid waste raw data, which is stored by local municipal bodies. We have used many innovative analytical tools and technology, such as data mining technology. On these analytical technologies, the clustering data classification technique is one of them. This paper focuses on K-mean clustering algorithms and their working in context with solid waste management. The analysis algorithms have been played an important role in decision making process. This type of algorithm helps the main decision-makers to make the right decisions or helps to improve the efficiency of the decision-making process. An excellent solid waste management framework must include all disturbing factors, including pollution generation, land use, energy use, financial costs, labor needs, monitoring, recycling rates, etc. © Springer Nature Singapore Pte Ltd 2021.

关键词： Decision support systems

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Smart Authentication Process for Human and Vehicles Using AI Techniques : An Approach for making better life in 21st Century Smart Cities

Smart Authentication Process for Human and Vehicles Using AI...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Anshit Garg Aditya Singh Rohit Rastogi Abhay Singh Computer Science & Engineering ABES EC Ghaziabad Uttar Pradesh India CSE Department ABES EC Ghaziabad Uttar Pradesh India

The Entry detection system as the name suggests detects entry attempts. Till date entry to organizations like schools and colleges are provided by the guards that do not map entries and exits to the premises very efficiently which is a serious security concern and also it is not humanly possible to identify each student and stop outsiders who might be disguised from entering. Ours is a face detection and facial recognition system which detects faces from a live video feed recorded from a camera installed at the gates, recognizes the face using the data set of registered staff and students of the college and opens the gates to allow *** also maintains a record of entry and exits along with time in a database for security purposes. OpenCV is used for facial and vehicle no. plate detection which forwards to the dlib library that is used for deep metric learning and extracting facial embeddings and finally is face recognition library that displays the real time name of the person. This helps in authorized entry by correct identification along with a record of entry and exit times. The system meets the requirement as it forwards the result from the recognizer to the connected sensors and effectors for providing/restraining entry in required cases.

关键词： databases Face recognition Authentication Organizations Logic gates Cameras Libraries

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Accuracy Comparison of Various Movie Recommendation System Algorithms

Accuracy Comparison of Various Movie Recommendation System A...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Ankita Rai Kriti Yadav Manpreet Singh Shashi Kant Singh Computer Science and Engineering Galgotias College of Engineering and Technology Greater Noida India

A Movie Recommendation is significant in our social life since it enhances entertainment while also providing a tailored user experience. Using such recommendation algorithms, users may receive recommendations for a group of movies based on their interests or the popularity of the *** the fact that there are hundreds of movie recommendation systems, the majority of them either cannot recommend a movie to existing users or cannot propose a movie to a new user at all. In this paper, we compared the different algorithms to step up a movie recommendation system to improve the accuracy of user-recommended movies. This recommendation engine scours movie databases for all relevant variables, such as popularity and attractiveness, needed to make a recommendation. In many circumstances, large correlations between several categories of items can be used to produce more accurate suggestions. Experiments on real data have shown that the proposed solution is efficient and effective. This Movie Recommendation system will use a comparison among different algorithms that can be used to form a movie recommendation system, to search the movies that are similar to the customer’s taste or genre, and would also provide a combination of the recommendations that are highly rated by different customers’ experience so that the customer is not limited to a particular category or genre but is also able to explore new and different varieties as well. The system will get trained for the improvement of the recommendations through hybrid means.

关键词： Correlation Filtering databases Collaborative filtering Entertainment industry Motion pictures User experience

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A Deep Q-Learning-Based Adaptive Traffic Light Control System for Urban Safety

A Deep Q-Learning-Based Adaptive Traffic Light Control Syste...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Ashish Tigga Lopamudra Hota Sanjeev Patel Arun Kumar Department of Computer Science and Engineering National Institute of Technology Rourkela India

Traffic congestion is a significant and recurring issues in today’s urbanised world, caused by an increase in the number of vehicles. While vehicle density fluctuates on temporally short and geographically small scales, efficient traffic signaling system helps in avoiding traffic congestion. An inefficacious traffic system can lead to congestion and delays resulting in high pollution, and fuel wastage. The Deep Reinforcement Learning (DRL) method provides an excellent approach to solve the problem involving complex relations such as traffic flow and congestion. Recent development in Deep Neural Network (DNN) further enhances the learning capabilities of an agent with complex real-time data. The paper presents an intelligent Traffic Light Control System (TLCS) built on a Deep Q-Learning (DQL) model that accurately represents the problem’s components: agents, environment, and actions. The proposed model aims to minimize the traffic queue length and delay in terms of waiting time. The model is implemented using Simulation of Urban MObility (SUMO) for traffic generation in an urban scenario. The performance of the proposed model is compared with a traditional traffic light control system. The simulation results show that the proposed DQL-based model can significantly reduce the delay compared with the traditional model.

关键词： Deep learning Adaptation models Q-learning Computational modeling Simulation Control systems Real-time systems

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Cardiovascular Disease Prediction Using KNN Algorithm

Cardiovascular Disease Prediction Using KNN Algorithm

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Shyam Sagar Singh Choudhary Priyam Singh Dileep Kr. Yadav Computer Science and Engineering Galgotias University Greater Noida Uttar Pradesh India

Innovation has made considerable progress in the course of the most recent couple of years, individuals presently use web as their base vehicle for recovering and trading data. Abundance isn't the main thing of our life which individuals search for nowadays. Well being was, is and it will be the genuine abundance for individual. Wellbeing shouldn't be stayed away from anytime in our life. Coronary illness is one of the most well-known factor for passing’s in individual. Cardiovascular sickness is those which influences veins and causes heart infections. The past strategy of ascertaining the obscure measure of these illnesses ease in taking terms to bring down the higher gamble of *** well being office has colossal measure of information; the ML calculations helps in clearing the path for forecast and to control examination and medicine. In this examination, technique for eliminating missing information and properties characterization is utilized in per-handling for high precision and navigation. A portion of the calculations utilized for this thing like Decision Tree, KNN and Naive Bayes. Our point is to gather information mining techniques in Cardiovascular illness expectation, by utilizing basic boundary with the goal that it very well may be utilized in Machine 2 Machine remote observing.

关键词： Heart Technological innovation Navigation Veins Prediction algorithms Cardiovascular diseases Decision trees

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An Approach to Recommend Fishing Location and Forecast Fish Production by Using Big data Analysis and Distributed Deep Learning

An Approach to Recommend Fishing Location and Forecast Fish ...

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IEEE international conference on Research, Innovation and Vision for the Future

作者： Minh-Triet Thai Thao-Ngan Chu-Ha Tuan-Anh Vo Trong-Hop Do Faculty of Information Science and Engineering University of Information Technology Vietnam National University Ho Chi Minh City Vietnam

ISBN: (纸本)9781665461672

In fishing industry, fish populations can move over a vast sea and thus boats often search for days or weeks before making a catch. Given the excessive CO2 emissions from vessels and rising fuel cost, it is important to optimize commercial fishing activities by reducing un-necessary searching period. This study proposes a hybrid recommendation system for predicting the best locations for catching specific fish species and experimented various deep learning based multi-variate time series models to forecast the gross weight of two important species of Norwegian fishery: haddock and mackerel. To ensure the practicality, both recommendation system models and time series forecasting models are trained and deployed using Apache Spark and BigDL, which are frameworks for big data processing and distributed deep learning training. The proposed hybrid recommendation model achieve good performance with RMSE of 0.4933. Deep learning based models are also shown to achieve high performance on forecasting fish gross weight. Through this study, it is revealed that datetime and environmental features can play important roles for building sustainable commercial fishing plan.

关键词： Deep learning Costs Time series analysis Predictive models Big data Fish data models

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Multi-Layer Recurrent Neural Networks for the Classification of Compton Camera Based Imaging data for Proton Beam Cancer Treatment

Multi-Layer Recurrent Neural Networks for the Classification...

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IEEE/ACM international Symposium on Big data computing (BDC)

作者： Joseph Clark Anaise Gaillard Justin Koe Nithya Navarathna Daniel J. Kelly Matthias K. Gobbert Carlos A. Barajas Jerimy C. Polf Dept. of Mathematics and Computing Lander University Dept. of Computational and Data Sciences George Mason University Dept. of Electrical Engineering The Cooper Union Dept. of Biological Sciences U. of Maryland Dept. of Mathematics and Statistics U. of Maryland Dept. of Radiation Oncology U. of Maryland School of Medicine

Proton beam therapy is a unique form of radiotherapy that utilizes protons to treat cancer by irradiating cancerous tumors, while avoiding unnecessary radiation exposure to surrounding healthy tissues. Real-time imaging of the proton beam can make this form of therapy more precise and safer for the patient during delivery. The use of Compton cameras is one proposed method for the real-time imaging of prompt gamma rays that are emitted by the proton beams as they travel through a patient’s body. Unfortunately, some of the Compton camera data is flawed and the reconstruction algorithm yields noisy and insufficiently detailed images to evaluate the proton delivery for the patient. Previous work used a deep residual fully connected neural network. The use of recurrent neural networks (RNNs) has been proposed, since they use recurrence relationships to make potentially better predictions. In this work, RNN architectures using two different recurrent layers are tested, the LSTM and the GRU. Although the deep residual fully connected neural network achieves over 75% testing accuracy and our models achieve only over 73% testing accuracy, the simplicity of our RNN models containing only 6 hidden layers as opposed to 512 is a significant advantage. Importantly in a clinical setting, the time to load the model from disk is significantly faster, potentially enabling the use of Compton camera image reconstruction in real-time during patient treatment.

关键词： Protons Particle beams Recurrent neural networks Reconstruction algorithms Cameras Real-time systems Image reconstruction

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TCP Performance Enhancement Using Cross-Layer Method in IoT Networks

TCP Performance Enhancement Using Cross-Layer Method in IoT ...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Sultana Parween Syed Zeeshan Hussain Department of Computer Science Jamia Millia Islamia New Delhi India

Transmission Control Protocol also known as TCP, utilizes many paths for the simultaneous transmission of data in order to increase its overall performance. But, the previous TCP had a problem to transmit a larger number of subflows in the Internet of Things (IoT) networks. We used a cross-layer design to enhance TCP performance in the IoT networks by implementing effective packet scheduling & congestion control in order to address the existing issues. The IoT network uses grid topology and Manhattan distance to improve scalability and flexibility. After network configuration, packet scheduling is conducted using proportional fair scheduling with fitness mechanism (FBPF) and choosing best subflow to minimize transmission latency. Scheduled subflows are sent along optimal paths to increase goodput and throughput. After packet scheduling, the proposed algorithm is used to control congestion in TCP. This algorithm consists of three stages: congestion detection stage, fast retransmission, and fast recovery. To improve fast retransmission time, we utilize a cat and mouse based optimization algorithm (CMBO). The adoption of a metaheuristics algorithm improves the convergence speed thereby improving the user throughput. Simulation findings indicate that our proposed scheme outperforms high TCP performance with respect to goodput, throughput, packet loss, congestion window size, and transmission delay.

关键词： Cross layer design Protocols Scheduling algorithms Scalability Throughput Scheduling Windows

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