Identifying industry 5.0 contributions to sustainable development: A strategy roadmap for delivering sustainability values

Mobile phone and sensors have become very useful to understand and analyze human lifestyle because of the huge amount of data they can collect every second. This triggered the idea of combining benefits and advantages of reality mining, machine learning and big data predictive analytics tools, applied to smartphones/sensors real time. The main goal of our study is to build a system that interacts with mobile phones and wearable healthcare sensors to predict patterns. Wearable healthcare sensors (heart rate sensor, temperature sensor and activity sensor) and mobile phone are used for gathering real time data. All sensors are managed using IoT systems; we used Arduino for collecting data from health sensors and Raspberry Pi 3 for programming and processing. Kmeans clustering algorithm is used for patterns prediction and predicted clusters (partitions) are transmitted to the user in his front-end interface in the mobile application. Real world data and clustering validation statistics (Elbow method and Silhouette method) are used to validate the proposed system and assess its performance and effectiveness. All data management and processing tasks are conducted over Apache Spark Databricks. This system relies on real time gathered data and can be applied to any prediction case making use of sensors and mobile generated data. As a proof of concept, we worked on predicting miscarriages to help pregnant women make quick decisions in case of miscarriage or probable miscarriage by creating a real time system prediction of miscarriage using wearable healthcare sensors, mobile tools, data mining algorithms and big data technologies. 9 risk factors contribute vastly in prediction, the Elbow method asserts that the optimal number of cluster is 2 and we achieve a higher value (0, 95) of Silhouette width that validates the good matching between clusters and observations. K-means algorithm gives good results in clustering the data.

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Reality mining and predictive analytics for building smart applications

The real estate auction market has become increasingly important in the financial, economic and investment fields, but few artificial intelligence-based studies have attempted to forecast the auction prices of real estate. The purpose of this study is to develop forecasting models of real estate auction prices using artificial intelligence and statistical methodologies. The forecasting models are developed through a regression model, an artificial neural network and a genetic algorithm. For empirical analysis, we use Seoul apartment auction data from 2013 to 2017 to predict the auction prices and compare the forecasting accuracy of the models. The genetic algorithm model has the best performance, and effective regional segmentation based on the auction appraisal price improves the predictive accuracy.

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Developing a Forecasting Model for Real Estate Auction Prices Using Artificial Intelligence

Currently, the amount of Internet of Things (IoT) applications is enhanced for processing, analyzing, and managing the created big data from the smart city. Certain other applications of smart cities were location-based services, transportation management, and urban design, amongst others. There are several challenges under these applications containing privacy, data security, mining, and visualization. The blockchain-assisted IoT application (BIoT) is offering new urban computing to secure smart cities. The blockchain is a secure and transparent data-sharing decentralized platform, so BIoT is suggested as the optimum solution to the aforementioned challenges. In this view, this study develops an Optimal Machine Learning-based Intrusion Detection System for Privacy Preserving BIoT with Smart Cities Environment, called OMLIDS-PBIoT technique. The presented OMLIDS-PBIoT technique exploits BC and ML techniques to accomplish security in the smart city environment. For attaining this, the presented OMLIDS-PBIoT technique employs data pre-processing in the initial stage to transform the data into a compatible format. Moreover, a golden eagle optimization (GEO)-based feature selection (FS) model is designed to derive useful feature subsets. In addition, a heap-based optimizer (HBO) with random vector functional link network (RVFL) model was utilized for intrusion classification. Additionally, blockchain technology is exploited for secure data transmission in the IoT-enabled smart city environment. The performance validation of the OMLIDS-PBIoT technique is carried out using benchmark datasets, and the outcomes are inspected under numerous factors. The experimental results demonstrate the superiority of the OMLIDS-PBIoT technique over recent approaches.

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Optimal Machine Learning Based Privacy Preserving Blockchain Assisted Internet of Things with Smart Cities Environment

Pada paper ini, telah diterapkan metode Naive Bayes serta C.45 ke dalam 4 buah studi kasus, yaitu kasus penerimaan “Kartu Indonesia Sehat”, penentuan pengajuan kartu kredit di sebuah bank, penentuan usia kelahiran, serta penentuan kelayakan calon anggota kredit pada koperasi untuk mengetahui algoritma terbaik di setiap kasus . Setelah itu, dilakukan perbandingan dalam hal Precision , Recall serta Accuracy untuk setiap data training dan data testing yang telah diberikan. Dari hasil implementasi yang dilakukan, telah dibangun sebuah aplikasi yang dapat menerapkan algoritma Naive Bayes dan C.45 di 4 buah kasus tersebut. Aplikasi telah diuji dengan blackbox dan algoritma dengan hasil valid dan dapat mengimplementasikan kedua buah algoritma dengan benar. Berdasarkan hasil pengujian, semakin banyaknya data training yang digunakan, maka nilai precision, recall dan accuracy akan semakin meningkat. Selain itu, hasil klasifikasi pada algoritma Naive Bayes dan C.45 tidak dapat memberikan nilai yang absolut atau mutlak di setiap kasus. Pada kasus penentuan penerimaan Kartu Indonesia Sehat,  kedua buah algoritma tersebut sama-sama efektif untuk digunakan. Untuk kasus pengajuan kartu kredit di sebuah bank,  C.45 lebih baik daripada Naive Bayes. Pada kasus penentuan usia kelahiran,  Naive Bayes lebih baik daripada C.45. Sedangkan pada kasus penentuan kelayakan calon anggota kredit di koperasi, Naive Bayes memberikan nilai yang lebih baik pada precision, tapi untuk recall dan accuracy, C.45 memberikan hasil yang lebih baik. Sehingga untuk menentukan algoritma terbaik yang akan dipakai di sebuah kasus, harus melihat kriteria, variable maupun jumlah data di kasus tersebut. Abstract In this paper, applied Naive Bayes and C.45 into 4 case studies, namely the case of acceptance of “Kartu Indonesia Sehat”, determination of credit card application in a bank, determination of birth age, and determination of eligibility of prospective members of credit to Koperasi to find out the best algorithm in each case. After that, the comparison in Precision, Recall and Accuracy for each training data and data testing has been given. From the results of the implementation, has built an application that can apply the Naive Bayes and C.45 algorithm in 4 cases. Applications have been tested in blackbox and algorithms with valid results and can implement both algorithms correctly. Based on the test results, the more training data used, the value of precision, recall and accuracy will increase. The classification results of Naive Bayes and C.45 algorithms can not provide absolute value in each case. In the case of determining the acceptance of the Kartu Indonesia Indonesia, the two algorithms are equally effective to use. For credit card submission cases at a bank, C.45 is better than Naive Bayes. In the case of determining the age of birth, Naive Bayes is better than C.45. Whereas in the case of determining the eligibility of prospective credit members in the cooperative, Naive Bayes provides better value in precision, but for recall and accuracy, C.45 gives better results. So, to determine the best algorithm to be used in a case, it must look at the criteria, variables and amount of data in the case

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Perbandingan Algoritma Naive Bayes dan C.45 dalam Klasifikasi Data Mining

Clustering is technique which is used to analyze the data in efficient manner and generate required information. To cluster the dataset, there is a technique named k-mean, is applied which is based on central point selection and calculation of Euclidian Distance. Here in k-mean, dataset will be loaded and from the dataset. Central points are selected using the formulae Euclidian distance and on the basis of Euclidian distance points are assigned to the clusters. The main disadvantage of k-mean is of accuracy, as in k-mean clustering user needs to define number of clusters. Because of user defined number of clusters, some points of the dataset are remained un-clustered. In this work, improvement in the kmean clustering algorithm will be proposed which can define number of clusters automatically and assign required cluster to un-clustered points. The proposed improvement will leads to improvement in accuracy and reduce clustering time by the member assigned to the cluster to predict cancer.

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Improved K-mean Clustering Algorithm for Prediction Analysis using Classification Technique in Data Mining

Artificial intelligence enabled intrusion detection systems for cognitive cyber-physical systems in industry 4.0 environment

The current scenario of open source development and outsourcing industry heavily depends upon the reusability of software components for achieving consistency in quality and cost optimization. Hence, the software developer needs excellent support in the assessment of the reusability levels of the software that they are trying to develop. Estimating Software Reusability has now become a topic of discussion. Reusability is measured in terms of cost, portability, understandability, interface complexity, coupling, interoperability, documentation, clarity etc. The combination of some factors were tested on different soft computing techniques like Fuzzy Logic and Neural Network and the corresponding average RMSE (Reusability Mean Square Error) was calculated to find out the reusability of those factors. In this paper it takes into account three different factors or variables of reusability of software components and and then propose a model for usability assessment using the Adaptive Neuro Fuzzy Inference System Approach (ANFIS). General terms : Software Component, reusability

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Neuro Fuzzy based Approach to Predict Component's Reusability

Design of robust deep learning-based object detection and classification model for autonomous driving applications

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/cmu2.12498

Shalini Goel

Papers

Improved K-mean Clustering Algorithm for Prediction Analysis using Classification Technique in Data Mining

Artificial intelligence enabled intrusion detection systems for cognitive cyber-physical systems in industry 4.0 environment

Neuro Fuzzy based Approach to Predict Component's Reusability

Design of robust deep learning-based object detection and classification model for autonomous driving applications

Improved malicious node detection method for detecting a bait in an extensive network for getting the maximum throughput