Prototype Regularized Manifold Regularization Technique for Semi-Supervised Online Extreme Learning Machine

Data streaming applications such as the Internet of Things (IoT) require processing or predicting from sequential data from various sensors. However, most of the data are unlabeled, making applying fully supervised learning algorithms impossible. The online manifold regularization approach allows sequential learning from partially labeled data, which is useful for sequential learning in environments with scarcely labeled data. Unfortunately, the manifold regularization technique does not work out of the box as it requires determining the radial basis function (RBF) kernel width parameter. The RBF kernel width parameter directly impacts the performance as it is used to inform the model to which class each piece of data most likely belongs. The width parameter is often determined off-line via hyperparameter search, where a vast amount of labeled data is required. Therefore, it limits its utility in applications where it is difficult to collect a great deal of labeled data, such as data stream mining. To address this issue, we proposed eliminating the RBF kernel from the manifold regularization technique altogether by combining the manifold regularization technique with a prototype learning method, which uses a finite set of prototypes to approximate the entire data set. Compared to other manifold regularization approaches, this approach instead queries the prototype-based learner to find the most similar samples for each sample instead of relying on the RBF kernel. Thus, it no longer necessitates the RBF kernel, which improves its practicality. The proposed approach can learn faster and achieve a higher classification performance than other manifold regularization techniques based on experiments on benchmark data sets. Results showed that the proposed approach can perform well even without using the RBF kernel, which improves the practicality of manifold regularization techniques for semi-supervised learning

Nuhfatu al 'Abir al Sari Bi`ahaditsi Abi Ayyub al 'Anshari

280 hlm., 24 cm

Improving blockchain security for the internet of things: challenges and solutions

Due to its uniquely suited to the knowledge era, the blockchain technology has currently become highly appealing to the next generation. In addition, such technology has been recently extended to the internet of things (IoT). In essence, the blockchain concept necessitates the use of a decentralized data operation system to store as well as to distribute data and the transactions across the net. Therefore, this study examines the specific concept of the blockchain as a decentralized data management system in the face of probable protection threats. Furthermore, It discusses the present solutions that can be used to counteract those attacks. The blockchain security enhancement solutions are included in this study by summarizing the key points of these solutions. Several blockchain systems and safety devices that register security defenselessness can be developed using such key points. At last, this paper discusses the pending matters and the outlook research paths of blockchain-IoT systems

Adillatu al Wahdaniyah fi al Raddi 'Ala al Nashraniyah

116 hal.; 24 c

al-Furuq

Kitab ini erat hubungannya dengan ilmu ushu al-fiqh dan lebih dominan pembahasannya mengarah kepada hukum/syariat Islam yakni : Dalam bidang ilmu fiqih dan ushul

Syarh Tanqih al-Fushul: Fi Akhtishar al-Mahshul Fi al-Ushul, 1st.ed./ Syihab ad-Din Abu Abbas Ahmad Bin Idris al-Qarafi

460 hal.; 25 cm

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

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