An efficient algorithm for energy harvesting in IIoT based on machine learning and swarm intelligence.

The Internet of Things (IoT) is a network of smart gadgets that are connected through the Internet, including computers, cameras, smart sensors, and mobile phones. Recent developments in the industrial IoT (IIoT) have enabled a wide range of applications, from small businesses to smart cities, which have become indispensable to many facets of human existence. In a system with a few devices, the short lifespan of conventional batteries, which raises maintenance costs, necessitates more replacements and has a negative environmental impact, does not present a problem. However, in networks with millions or even billions of devices, it poses a serious problem. The rapid expansion of the IoT paradigm is threatened by these battery restrictions, thus academics and businesses are now interested in prolonging the lifespan of IoT devices while retaining optimal performance. Resource management is an important aspect of IIoT because it's scarce and limited. Therefore, this paper proposed an efficient algorithm based on federated learning. Firstly, the optimization problem is decomposed into various sub-problems. Then, the particle swarm optimization algorithm is deployed to solve the energy budget. Finally, a communication resource is optimized by an iterative matching algorithm. Simulation results show that the proposed algorithm has better performance as compared with existing algorithms.

Addressing Internet of Things security by enhanced sine cosine metaheuristics tuned hybrid machine learning model and results interpretation based on SHAP approach.

An ever increasing number of electronic devices integrated into the Internet of Things (IoT) generates vast amounts of data, which gets transported via network and stored for further analysis. However, besides the undisputed advantages of this technology, it also brings risks of unauthorized access and data compromise, situations where machine learning (ML) and artificial intelligence (AI) can help with detection of potential threats, intrusions and automation of the diagnostic process. The effectiveness of the applied algorithms largely depends on the previously performed optimization, i.e., predetermined values of hyperparameters and training conducted to achieve the desired result. Therefore, to address very important issue of IoT security, this article proposes an AI framework based on the simple convolutional neural network (CNN) and extreme machine learning machine (ELM) tuned by modified sine cosine algorithm (SCA). Not withstanding that many methods for addressing security issues have been developed, there is always a possibility for further improvements and proposed research tried to fill in this gap. The introduced framework was evaluated on two ToN IoT intrusion detection datasets, that consist of the network traffic data generated in Windows 7 and Windows 10 environments. The analysis of the results suggests that the proposed model achieved superior level of classification performance for the observed datasets. Additionally, besides conducting rigid statistical tests, best derived model is interpreted by SHapley Additive exPlanations (SHAP) analysis and results findings can be used by security experts to further enhance security of IoT systems.

Automatic Malignant and Benign Skin Cancer Classification Using a Hybrid Deep Learning Approach.

Skin cancer is one of the major types of cancer with an increasing incidence in recent decades. The source of skin cancer arises in various dermatologic disorders. Skin cancer is classified into various types based on texture, color, morphological features, and structure. The conventional approach for skin cancer identification needs time and money for the predicted results. Currently, medical science is utilizing various tools based on digital technology for the classification of skin cancer. The machine learning-based classification approach is the robust and dominant approach for automatic methods of classifying skin cancer. The various existing and proposed methods of deep neural network, support vector machine (SVM), neural network (NN), random forest (RF), and K-nearest neighbor are used for malignant and benign skin cancer identification. In this study, a method was proposed based on the stacking of classifiers with three folds towards the classification of melanoma and benign skin cancers. The system was trained with 1000 skin images with the categories of melanoma and benign. The training and testing were performed using 70 and 30 percent of the overall data set, respectively. The primary feature extraction was conducted using the Resnet50, Xception, and VGG16 methods. The accuracy, F1 scores, AUC, and sensitivity metrics were used for the overall performance evaluation. In the proposed Stacked CV method, the system was trained in three levels by deep learning, SVM, RF, NN, KNN, and logistic regression methods. The proposed method for Xception techniques of feature extraction achieved 90.9% accuracy and was stronger compared to ResNet50 and VGG 16 methods. The improvement and optimization of the proposed method with a large training dataset could provide a reliable and robust skin cancer classification system.

A clustering approach to identify multidimensional poverty indicators for the bottom 40 percent group.

The Multidimensional Poverty Index (MPI) is an income-based poverty index which measures multiple deprivations alongside other relevant factors to determine and classify poverty. The implementation of a reliable MPI is one of the significant efforts by the Malaysian government to improve measures in alleviating poverty, in line with the recent policy for Bottom 40 Percent (B40) group. However, using this measurement, only 0.86% of Malaysians are regarded as multidimensionally poor, and this measurement was claimed to be irrelevant for Malaysia as a country that has rapid economic development. Therefore, this study proposes a B40 clustering-based K-Means with cosine similarity architecture to identify the right indicators and dimensions that will provide data driven MPI measurement. In order to evaluate the approach, this study conducted extensive experiments on the Malaysian Census dataset. A series of data preprocessing steps were implemented, including data integration, attribute generation, data filtering, data cleaning, data transformation and attribute selection. The clustering model produced eight clusters of B40 group. The study included a comprehensive clustering analysis to meaningfully understand each of the clusters. The analysis discovered seven indicators of multidimensional poverty from three dimensions encompassing education, living standard and employment. Out of the seven indicators, this study proposed six indicators to be added to the current MPI to establish a more meaningful scenario of the current poverty trend in Malaysia. The outcomes from this study may help the government in properly identifying the B40 group who suffers from financial burden, which could have been currently misclassified.

The Deep Learning Solutions on Lossless Compression Methods for Alleviating Data Load on IoT Nodes in Smart Cities.

Networking is crucial for smart city projects nowadays, as it offers an environment where people and things are connected. This paper presents a chronology of factors on the development of smart cities, including IoT technologies as network infrastructure. Increasing IoT nodes leads to increasing data flow, which is a potential source of failure for IoT networks. The biggest challenge of IoT networks is that the IoT may have insufficient memory to handle all transaction data within the IoT network. We aim in this paper to propose a potential compression method for reducing IoT network data traffic. Therefore, we investigate various lossless compression algorithms, such as entropy or dictionary-based algorithms, and general compression methods to determine which algorithm or method adheres to the IoT specifications. Furthermore, this study conducts compression experiments using entropy (Huffman, Adaptive Huffman) and Dictionary (LZ77, LZ78) as well as five different types of datasets of the IoT data traffic. Though the above algorithms can alleviate the IoT data traffic, adaptive Huffman gave the best compression algorithm. Therefore, in this paper, we aim to propose a conceptual compression method for IoT data traffic by improving an adaptive Huffman based on deep learning concepts using weights, pruning, and pooling in the neural network. The proposed algorithm is believed to obtain a better compression ratio. Additionally, in this paper, we also discuss the challenges of applying the proposed algorithm to IoT data compression due to the limitations of IoT memory and IoT processor, which later it can be implemented in IoT networks.

Malay Version of the mHealth App Usability Questionnaire (M-MAUQ): Translation, Adaptation, and Validation Study.

BACKGROUND: Mobile health (mHealth) apps play an important role in delivering education, providing advice on treatment, and monitoring patients' health. Good usability of mHealth apps is essential to achieve the objectives of mHealth apps efficiently. To date, there are questionnaires available to assess the general system usability but not explicitly tailored to precisely assess the usability of mHealth apps. Hence, the mHealth App Usability Questionnaire (MAUQ) was developed with 4 versions according to the type of app (interactive or standalone) and according to the target user (patient or provider). Standalone MAUQ for patients comprises 3 subscales, which are ease of use, interface and satisfaction, and usefulness. OBJECTIVE: This study aimed to translate and validate the English version of MAUQ (standalone for patients) into a Malay version of MAUQ (M-MAUQ) for mHealth app research and usage in future in Malaysia. METHODS: Forward and backward translation and harmonization of M-MAUQ were conducted by Malay native speakers who also spoke English as their second language. The process began with a forward translation by 2 independent translators followed by harmonization to produce an initial translated version of M-MAUQ. Next, the forward translation was continued by another 2 translators who had never seen the original MAUQ. Lastly, harmonization was conducted among the committee members to resolve any ambiguity and inconsistency in the words and sentences of the items derived with the prefinal adapted questionnaire. Subsequently, content and face validations were performed with 10 experts and 10 target users, respectively. Modified kappa statistic was used to determine the interrater agreement among the raters. The reliability of the M-MAUQ was assessed by 51 healthy young adult mobile phone users. Participants needed to install the MyFitnessPal app and use it for 2 days for familiarization before completing the designated task and answer the M-MAUQ. The MyFitnessPal app was selected because it is one among the most popular installed mHealth apps globally available for iPhone and Android users and represents a standalone mHealth app. RESULTS: The content validity index for the relevancy and clarity of M-MAUQ were determined to be 0.983 and 0.944, respectively, which indicated good relevancy and clarity. The face validity index for understandability was 0.961, which indicated that users understood the M-MAUQ. The kappa statistic for every item in M-MAUQ indicated excellent agreement between the raters (κ ranging from 0.76 to 1.09). The Cronbach α for 18 items was .946, which also indicated good reliability in assessing the usability of the mHealth app. CONCLUSIONS: The M-MAUQ fulfilled the validation criteria as it revealed good reliability and validity similar to the original version. M-MAUQ can be used to assess the usability of mHealth apps in Malay in the future.

Enhancing web search result clustering model based on multiview multirepresentation consensus cluster ensemble (mmcc) approach.

Existing text clustering methods utilize only one representation at a time (single view), whereas multiple views can represent documents. The multiview multirepresentation method enhances clustering quality. Moreover, existing clustering methods that utilize more than one representation at a time (multiview) use representation with the same nature. Hence, using multiple views that represent data in a different representation with clustering methods is reasonable to create a diverse set of candidate clustering solutions. On this basis, an effective dynamic clustering method must consider combining multiple views of data including semantic view, lexical view (word weighting), and topic view as well as the number of clusters. The main goal of this study is to develop a new method that can improve the performance of web search result clustering (WSRC). An enhanced multiview multirepresentation consensus clustering ensemble (MMCC) method is proposed to create a set of diverse candidate solutions and select a high-quality overlapping cluster. The overlapping clusters are obtained from the candidate solutions created by different clustering methods. The framework to develop the proposed MMCC includes numerous stages: (1) acquiring the standard datasets (MORESQUE and Open Directory Project-239), which are used to validate search result clustering algorithms, (2) preprocessing the dataset, (3) applying multiview multirepresentation clustering models, (4) using the radius-based cluster number estimation algorithm, and (5) employing the consensus clustering ensemble method. Results show an improvement in clustering methods when multiview multirepresentation is used. More importantly, the proposed MMCC model improves the overall performance of WSRC compared with all single-view clustering models.