Smart security door system using SMS based energy harvest

Over the last decade, different studies have been conducted to increase security to identify sensor technology and provide alternative energy with other energy harvest techniques such as vibration energy harvester and sun energy harvester. There is no combinational approach to utilize the door to create energy and use it for security measures in the literature, making our system different and unique. This proposed system comprises the security and the energy harvest; the security section utilizes a motion detector sensor to detect intruders. For instance, the magnetic door lock type firmly locks the door, which can only open with a generated password. On the other side, the energy harvest section utilizes the door motion to generate electricity for the system, which solves power shortage and limited battery life issues. Moreover, this study includes a GSM module that allows authorized owners to receive a generated password as a security enhancement. This design mainly focuses on improving or optimizing the conventional security doors' overall performance as sliding door, panel door, or revolving door. The experimental results show the system efficiency in terms of power generation and the time needed to authenticate the property owner. Notably, the power generator can generate electricity more rapidly, while the needed time to receive the mobile device's security code is around 3.6 seconds

A smart fire detection system using iot technology with automatic water sprinkler

House combustion is one of the main concerns for builders, designers, and property residents. Singular sensors were used for a long time in the event of detection of a fire, but these sensors can not measure the amount of fire to alert the emergency response units. To address this problem, this study aims to implement a smart fire detection system that would not only detect the fire using integrated sensors but also alert property owners, emergency services, and local police stations to protect lives and valuable assets simultaneously. The proposed model in this paper employs different integrated detectors, such as heat, smoke, and flame. The signals from those detectors go through the system algorithm to check the fire's potentiality and then broadcast the predicted result to various parties using GSM modem associated with the system. To get real-life data without putting human lives in danger, an IoT technology has been implemented to provide the fire department with the necessary data. Finally, the main feature of the proposed system is to minimize false alarms, which, in turn, makes this system more reliable. The experimental results showed the superiority of our model in terms of affordability, effectiveness, and responsiveness as the system uses the Ubidots platform, which makes the data exchange faster and reliable

Reliable Bidirectional Data Transfer Approach for the Internet of Secured Medical Things Using ZigBee Wireless Network

[EN] Nowadays, the Internet of Things (IoT) performs robust services for real-time applications in monitoring communication systems and generating meaningful information. The ZigBee devices offer low latency and manageable costs for wireless communication and support the process of physical data collection. Some biosensing systems comprise IoT-based ZigBee devices to monitor patient healthcare attributes and alert healthcare professionals for needed action. However, most of them still face unstable and frequent data interruption issues due to transmission service intrusions. Moreover, the medical data is publicly available using cloud services, and communicated through the smart devices to specialists for evaluation and disease diagnosis. Therefore, the applicable security analysis is another key factor for any medical system. This work proposed an approach for reliable network supervision with the internet of secured medical things using ZigBee networks for a smart healthcare system (RNM-SC). It aims to improve data systems with manageable congestion through load-balanced devices. Moreover, it also increases security performance in the presence of anomalies and offers data routing using the bidirectional heuristics technique. In addition, it deals with more realistic algorithm to associate only authorized devices and avoid the chances of compromising data. In the end, the communication between cloud and network applications is also protected from hostile actions, and only certified end-users can access the data. The proposed approach was tested and analyzed in Network Simulator (NS-3), and, compared to existing solutions, demonstrated significant and reliable performance improvements in terms of network throughput by 12%, energy consumption by 17%, packet drop ratio by 37%, end-to-end delay by 18%, routing complexity by 37%, and tampered packets by 37%.This research is supported by Artificial Intelligence & Data Analytics Lab (AIDA) CCIS Prince Sultan University, Riyadh, Saudi Arabia. Authors are thankful for the support.Rehman, A.; Haseeb, K.; Fati, SM.; Lloret, J.; Peñalver Herrero, ML. (2021). Reliable Bidirectional Data Transfer Approach for the Internet of Secured Medical Things Using ZigBee Wireless Network. Applied Sciences. 11(21):1-16. https://doi.org/10.3390/app11219947S116112

Modelling contents status for IPTV delivery networks

Since IPTV has been invented, IPTV is considered a dominant technology to distribute high quality videos and live channels anytime anywhere over challenging environment to end users who are having different preferences and demands. Presently, IPTV service providers manage IPTV delivery networks, in terms of contents, channels, resources, based on contents popularity distribution and/or users’ preferences only. Although content popularity and users’ preferences play an important role to cope with the increasing demand of IPTV contents/channels, these two measures fail in producing efficient IPTV delivery networks For that, IPTV delivery network designing should integrate the IPTV content characteristics like size, interactivity, the rapid changing lifetime. Therefore, the idea of this paper is to build a mathematical model that integrates all these factors in one concept called IPTV content status. Modeling the contents status according to its characteristics is an important point to design Content-Aware IPTV delivery networks.The experimental results showed the superiority of modeling IPTV content status in balancing the load and reducing the resources waste

A coherent authentication framework for mobile computing based on homomorphic signature and implicit authentication

Mobile cloud computing is an extension of cloud computing that allow the users to access the cloud service via their mobile devices. Although mobile cloud computing is convenient and easy to use, the security challenges are increasing significantly.One of the major issues is unauthorized access.Identity Management enables to tackle this issue by protecting the identity of users and controlling access to resources. Although there are several IDM frameworks in place, they are vulnerable to attacks like timing attacks in OAuth, malicious code attack in OpenID and huge amount of information leakage when user’s identity is compromised in Single Sign-On. Our proposed framework implicitly authenticates a user based on user’s typing behavior.The authentication information is encrypted into homomorphic signature before being sent to IDM server and tokens are used to authorize users to access the cloud resources.Advantages of our proposed framework are: user’s identity protection and prevention from unauthorized access

Statistical Histogram Decision Based Contrast Categorization of Skin Lesion Datasets Dermoscopic Images

Most of the melanoma cases of skin cancer are the life-threatening form of cancer. It is prevalent among the Caucasian group of people due to their light skin tone. Melanoma is the second most common cancer that hits the age group of 15–29 years. The high number of cases has increased the importance of automated systems for diagnosing. The diagnosis should be fast and accurate for the early treatment of melanoma. It should remove the need for biopsies and provide stable diagnostic results. Automation requires large quantities of images. Skin lesion datasets contain various kinds of dermoscopic images for the detection of melanoma. Three publicly available benchmark skin lesion datasets, ISIC 2017, ISBI 2016, and PH2, are used for the experiments. Currently, the ISIC archive and PH2 are the most challenging and demanding dermoscopic datasets. These datasets’ pre-analysis is necessary to overcome contrast variations, under or over segmented images boundary extraction, and accurate skin lesion classification. In this paper, we proposed the statistical histogram-based method for the pre-categorization of skin lesion datasets. The image histogram properties are utilized to check the image contrast variations and categorized these images into high and low contrast images. The two performance measures, processing time and efficiency, are computed for evaluation of the proposed method. Our results showed that the proposed methodology improves the pre-processing efficiency of 77% of ISIC 2017, 67% of ISBI 2016, and 92.5% of PH2 datasets

A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter

The advent of social media, particularly Twitter, raises many issues due to a misunderstanding regarding the concept of freedom of speech. One of these issues is cyberbullying, which is a critical global issue that affects both individual victims and societies. Many attempts have been introduced in the literature to intervene in, prevent, or mitigate cyberbullying; however, because these attempts rely on the victims’ interactions, they are not practical. Therefore, detection of cyberbullying without the involvement of the victims is necessary. In this study, we attempted to explore this issue by compiling a global dataset of 37,373 unique tweets from Twitter. Moreover, seven machine learning classifiers were used, namely, Logistic Regression (LR), Light Gradient Boosting Machine (LGBM), Stochastic Gradient Descent (SGD), Random Forest (RF), AdaBoost (ADB), Naive Bayes (NB), and Support Vector Machine (SVM). Each of these algorithms was evaluated using accuracy, precision, recall, and F1 score as the performance metrics to determine the classifiers’ recognition rates applied to the global dataset. The experimental results show the superiority of LR, which achieved a median accuracy of around 90.57%. Among the classifiers, logistic regression achieved the best F1 score (0.928), SGD achieved the best precision (0.968), and SVM achieved the best recall (1.00)

Enhanced Credit Card Fraud Detection Model Using Machine Learning

The COVID-19 pandemic has limited people’s mobility to a certain extent, making it difficult to purchase goods and services offline, which has led the creation of a culture of increased dependence on online services. One of the crucial issues with using credit cards is fraud, which is a serious challenge in the realm of online transactions. Consequently, there is a huge need to develop the best approach possible to using machine learning in order to prevent almost all fraudulent credit card transactions. This paper studies a total of 66 machine learning models based on two stages of evaluation. A real-world credit card fraud detection dataset of European cardholders is used in each model along with stratified K-fold cross-validation. In the first stage, nine machine learning algorithms are tested to detect fraudulent transactions. The best three algorithms are nominated to be used again in the second stage, with 19 resampling techniques used with each one of the best three algorithms. Out of 330 evaluation metric values that took nearly one month to obtain, the All K-Nearest Neighbors (AllKNN) undersampling technique along with CatBoost (AllKNN-CatBoost) is considered to be the best proposed model. Accordingly, the AllKNN-CatBoost model is compared with related works. The results indicate that the proposed model outperforms previous models with an AUC value of 97.94%, a Recall value of 95.91%, and an F1-Score value of 87.40%

Early Diagnosis of Oral Squamous Cell Carcinoma Based on Histopathological Images Using Deep and Hybrid Learning Approaches

Oral squamous cell carcinoma (OSCC) is one of the most common head and neck cancer types, which is ranked the seventh most common cancer. As OSCC is a histological tumor, histopathological images are the gold diagnosis standard. However, such diagnosis takes a long time and high-efficiency human experience due to tumor heterogeneity. Thus, artificial intelligence techniques help doctors and experts to make an accurate diagnosis. This study aimed to achieve satisfactory results for the early diagnosis of OSCC by applying hybrid techniques based on fused features. The first proposed method is based on a hybrid method of CNN models (AlexNet and ResNet-18) and the support vector machine (SVM) algorithm. This method achieved superior results in diagnosing the OSCC data set. The second proposed method is based on the hybrid features extracted by CNN models (AlexNet and ResNet-18) combined with the color, texture, and shape features extracted using the fuzzy color histogram (FCH), discrete wavelet transform (DWT), local binary pattern (LBP), and gray-level co-occurrence matrix (GLCM) algorithms. Because of the high dimensionality of the data set features, the principal component analysis (PCA) algorithm was applied to reduce the dimensionality and send it to the artificial neural network (ANN) algorithm to diagnose it with promising accuracy. All the proposed systems achieved superior results in histological image diagnosis of OSCC, the ANN network based on the hybrid features using AlexNet, DWT, LBP, FCH, and GLCM achieved an accuracy of 99.1%, specificity of 99.61%, sensitivity of 99.5%, precision of 99.71%, and AUC of 99.52%

Deep and Hybrid Learning Technique for Early Detection of Tuberculosis Based on X-ray Images Using Feature Fusion

Tuberculosis (TB) is a fatal disease in developing countries, with the infection spreading through direct contact or the air. Despite its seriousness, the early detection of tuberculosis by means of reliable techniques can save the patients’ lives. A chest X-ray is a recommended screening technique for locating pulmonary abnormalities. However, analyzing the X-ray images to detect abnormalities requires highly experienced radiologists. Therefore, artificial intelligence techniques come into play to help radiologists to perform an accurate diagnosis at the early stages of TB disease. Hence, this study focuses on applying two AI techniques, CNN and ANN. Furthermore, this study proposes two different approaches with two systems each to diagnose tuberculosis from two datasets. The first approach hybridizes two CNN models, which are Res-Net-50 and GoogLeNet techniques. Prior to the classification stage, the approach applies the principal component analysis (PCA) algorithm to reduce the features’ dimensionality, aiming to extract the deep features. Then, the SVM algorithm is used for classifying features with high accuracy. This hybrid approach achieved superior results in diagnosing tuberculosis based on X-ray images from both datasets. In contrast, the second approach applies artificial neural networks (ANN) based on the fused features extracted by ResNet-50 and GoogleNet models and combines them with the features extracted by the gray level co-occurrence matrix (GLCM), discrete wavelet transform (DWT) and local binary pattern (LBP) algorithms. ANN achieved superior results for the two tuberculosis datasets. When using the first dataset, the ANN, with ResNet-50, GLCM, DWT and LBP features, achieved an accuracy of 99.2%, a sensitivity of 99.23%, a specificity of 99.41%, and an AUC of 99.78%. Meanwhile, with the second dataset, ANN, with the features of ResNet-50, GLCM, DWT and LBP, reached an accuracy of 99.8%, a sensitivity of 99.54%, a specificity of 99.68%, and an AUC of 99.82%. Thus, the proposed methods help doctors and radiologists to diagnose tuberculosis early and increase chances of survival