SEHIDS: Self Evolving Host-Based Intrusion Detection System for IoT Networks.

The Internet of Things (IoT) offers unprecedented opportunities to access anything from anywhere and at any time. It is, therefore, not surprising that the IoT acts as a paramount infrastructure for most modern and envisaged systems, including but not limited to smart homes, e-health, and intelligent transportation systems. However, the prevalence of IoT networks and the important role they play in various critical aspects of our lives make them a target for various types of advanced cyberattacks: Dyn attack, BrickerBot, Sonic, Smart Deadbolts, and Silex are just a few examples. Motivated by the need to protect IoT networks, this paper proposes SEHIDS: Self Evolving Host-based Intrusion Detection System. The underlying approach of SEHIDS is to equip each IoT node with a simple Artificial Neural Networks (ANN) architecture and a lightweight mechanism through which an IoT device can train this architecture online and evolves it whenever its performance prediction is degraded. By this means, SEHIDS enables each node to generate the ANN architecture required to detect the threats it faces, which makes SEHIDS suitable for the heterogeneity and turbulence of traffic amongst nodes. Moreover, the gradual evolution of the SEHIDS architecture facilitates retaining it to its near-minimal configurations, which saves the resources required to compute, store, and manipulate the model's parameters and speeds up the convergence of the model to the zero-classification regions. It is noteworthy that SEHIDS specifies the evolving criteria based on the outcomes of the built-in model's loss function, which is, in turn, facilitates using SEHIDS to develop the two common types of IDS: signature-based and anomaly-based. Where in the signature-based IDS version, a supervised architecture (i.e., multilayer perceptron architecture) is used to classify different types of attacks, while in the anomaly-based IDS version, an unsupervised architecture (i.e., replicator neuronal network) is used to distinguish benign from malicious traffic. Comprehensive assessments for SEHIDS from different perspectives were conducted with three recent datasets containing a variety of cyberattacks targeting IoT networks: BoT-IoT, TON-IOT, and IoTID20. These results of assessments demonstrate that SEHIDS is able to make accurate predictions of 1 True Positive and is suitable for IoT networks with the order of small fractions of the resources of typical IoT devices.

LLDNet: A Lightweight Lane Detection Approach for Autonomous Cars Using Deep Learning.

Lane detection plays a vital role in making the idea of the autonomous car a reality. Traditional lane detection methods need extensive hand-crafted features and post-processing techniques, which make the models specific feature-oriented, and susceptible to instability for the variations on road scenes. In recent years, Deep Learning (DL) models, especially Convolutional Neural Network (CNN) models have been proposed and utilized to perform pixel-level lane segmentation. However, most of the methods focus on achieving high accuracy while considering structured roads and good weather conditions and do not put emphasis on testing their models on defected roads, especially ones with blurry lane lines, no lane lines, and cracked pavements, which are predominant in the real world. Moreover, many of these CNN-based models have complex structures and require high-end systems to operate, which makes them quite unsuitable for being implemented in embedded devices. Considering these shortcomings, in this paper, we have introduced a novel CNN model named LLDNet based on an encoder-decoder architecture that is lightweight and has been tested in adverse weather as well as road conditions. A channel attention and spatial attention module are integrated into the designed architecture to refine the feature maps for achieving outstanding results with a lower number of parameters. We have used a hybrid dataset to train our model, which was created by combining two separate datasets, and have compared the model with a few state-of-the-art encoder-decoder architectures. Numerical results on the utilized dataset show that our model surpasses the compared methods in terms of dice coefficient, IoU, and the size of the models. Moreover, we carried out extensive experiments on the videos of different roads in Bangladesh. The visualization results exhibit that our model can detect the lanes accurately in both structured and defected roads and adverse weather conditions. Experimental results elicit that our designed method is capable of detecting lanes accurately and is ready for practical implementation.

Transfer Learning for Sentiment Analysis Using BERT Based Supervised Fine-Tuning.

The growth of the Internet has expanded the amount of data expressed by users across multiple platforms. The availability of these different worldviews and individuals' emotions empowers sentiment analysis. However, sentiment analysis becomes even more challenging due to a scarcity of standardized labeled data in the Bangla NLP domain. The majority of the existing Bangla research has relied on models of deep learning that significantly focus on context-independent word embeddings, such as Word2Vec, GloVe, and fastText, in which each word has a fixed representation irrespective of its context. Meanwhile, context-based pre-trained language models such as BERT have recently revolutionized the state of natural language processing. In this work, we utilized BERT's transfer learning ability to a deep integrated model CNN-BiLSTM for enhanced performance of decision-making in sentiment analysis. In addition, we also introduced the ability of transfer learning to classical machine learning algorithms for the performance comparison of CNN-BiLSTM. Additionally, we explore various word embedding techniques, such as Word2Vec, GloVe, and fastText, and compare their performance to the BERT transfer learning strategy. As a result, we have shown a state-of-the-art binary classification performance for Bangla sentiment analysis that significantly outperforms all embedding and algorithms.

Hybrid HCNN-KNN Model Enhances Age Estimation Accuracy in Orthopantomography.

Age estimation in dental radiographs Orthopantomography (OPG) is a medical imaging technique that physicians and pathologists utilize for disease identification and legal matters. For example, for estimating post-mortem interval, detecting child abuse, drug trafficking, and identifying an unknown body. Recent development in automated image processing models improved the age estimation's limited precision to an approximate range of +/- 1 year. While this estimation is often accepted as accurate measurement, age estimation should be as precise as possible in most serious matters, such as homicide. Current age estimation techniques are highly dependent on manual and time-consuming image processing. Age estimation is often a time-sensitive matter in which the image processing time is vital. Recent development in Machine learning-based data processing methods has decreased the imaging time processing; however, the accuracy of these techniques remains to be further improved. We proposed an ensemble method of image classifiers to enhance the accuracy of age estimation using OPGs from 1 year to a couple of months (1-3-6). This hybrid model is based on convolutional neural networks (CNN) and K nearest neighbors (KNN). The hybrid (HCNN-KNN) model was used to investigate 1,922 panoramic dental radiographs of patients aged 15 to 23. These OPGs were obtained from the various teaching institutes and private dental clinics in Malaysia. To minimize the chance of overfitting in our model, we used the principal component analysis (PCA) algorithm and eliminated the features with high correlation. To further enhance the performance of our hybrid model, we performed systematic image pre-processing. We applied a series of classifications to train our model. We have successfully demonstrated that combining these innovative approaches has improved the classification and segmentation and thus the age-estimation outcome of the model. Our findings suggest that our innovative model, for the first time, to the best of our knowledge, successfully estimated the age in classified studies of 1 year old, 6 months, 3 months and 1-month-old cases with accuracies of 99.98, 99.96, 99.87, and 98.78 respectively.

IoT-Based Water Quality Assessment System for Industrial Waste WaterHealthcare Perspective.

The environment, especially water, gets polluted due to industrialization and urbanization. Pollution due to industrialization and urbanization has harmful effects on both the environment and the lives on Earth. This polluted water can cause food poisoning, diarrhea, short-term gastrointestinal problems, respiratory diseases, skin problems, and other serious health complications. In a developing country like Bangladesh, where ready-made garments sector is one of the major sources of the total Gross Domestic Product (GDP), most of the wastes released from the garment factories are dumped into the nearest rivers or canals. Hence, the quality of the water of these bodies become very incompatible for the living beings, and so, it has become one of the major threats to the environment and human health. In addition, the amount of fish in the rivers and canals in Bangladesh is decreasing day by day as a result of water pollution. Therefore, to save fish and other water animals and the environment, we need to monitor the quality of the water and find out the reasons for the pollution. Real-time monitoring of the quality of water is vital for controlling water pollution. Most of the approaches for controlling water pollution are mainly biological and lab-based, which takes a lot of time and resources. To address this issue, we developed an Internet of Things (IoT)-based real-time water quality monitoring system, integrated with a mobile application. The proposed system in this research measures some of the most important indexes of water, including the potential of hydrogen (pH), total dissolved solids (TDS), and turbidity, and temperature of water. The proposed system results will be very helpful in saving the environment, and thus, improving the health of living creatures on Earth.

Spoken Language Identification Using Deep Learning.

The process of detecting language from an audio clip by an unknown speaker, regardless of gender, manner of speaking, and distinct age speaker, is defined as spoken language identification (SLID). The considerable task is to recognize the features that can distinguish between languages clearly and efficiently. The model uses audio files and converts those files into spectrogram images. It applies the convolutional neural network (CNN) to bring out main attributes or features to detect output easily. The main objective is to detect languages out of English, French, Spanish, and German, Estonian, Tamil, Mandarin, Turkish, Chinese, Arabic, Hindi, Indonesian, Portuguese, Japanese, Latin, Dutch, Portuguese, Pushto, Romanian, Korean, Russian, Swedish, Tamil, Thai, and Urdu. An experiment was conducted on different audio files using the Kaggle dataset named spoken language identification. These audio files are comprised of utterances, each of them spanning over a fixed duration of 10 seconds. The whole dataset is split into training and test sets. Preparatory results give an overall accuracy of 98%. Extensive and accurate testing show an overall accuracy of 88%.

An Efficient Dynamic-Decision Based Task Scheduler for Task Offloading Optimization and Energy Management in Mobile Cloud Computing.

Restricted abilities of mobile devices in terms of storage, computation, time, energy supply, and transmission causes issues related to energy optimization and time management while processing tasks on mobile phones. This issue pertains to multifarious mobile device-related dimensions, including mobile cloud computing, fog computing, and edge computing. On the contrary, mobile devices' dearth of storage and processing power originates several issues for optimal energy and time management. These problems intensify the process of task retaining and offloading on mobile devices. This paper presents a novel task scheduling algorithm that addresses energy consumption and time execution by proposing an energy-efficient dynamic decision-based method. The proposed model quickly adapts to the cloud computing tasks and energy and time computation of mobile devices. Furthermore, we present a novel task scheduling server that performs the offloading computation process on the cloud, enhancing the mobile device's decision-making ability and computational performance during task offloading. The process of task scheduling harnesses the proposed empirical algorithm. The outcomes of this study enable effective task scheduling wherein energy consumption and task scheduling reduces significantly.

Aortic Root Geometric and Dynamic Changes After Device Closure of Interatrial Shunts.

BACKGROUND: The spatial relationship between atrial septal occluders and the aorta and the subsequent impact on the geometry and mechanics of the aortic root have been underinvestigated. The aim of this study was to evaluate occluder-aorta interaction after device closure of an atrial septal defect (ASD) or a patent foramen ovale (PFO) using three-dimensional transesophageal echocardiography and two-dimensional speckle-tracking echocardiography. METHODS: In 65 adult patients (mean age, 47 ± 14 years; 71% women) who underwent ASD (n = 35) or PFO (n = 30) closure with the Amplatzer Septal Occluder or Amplatzer PFO Occluder, occluder-aorta contact was evaluated on three-dimensional transesophageal echocardiography and defined as continuous, intermittent, or absent. Sinus of Valsalva diameter, height, eccentricity, and strain were measured before and immediately after occluder implantation. RESULTS: The occluder/total septal length and occluder/body surface area ratios were significantly larger after PFO than after ASD closure. The occluder was in contact with the aorta in 93.8% of cases (ASD, 91.4%; PFO, 96.7%). After ASD closure, occluder-aorta contact was very common, in patients with an aortic rim < 5 mm (100%) and those with an aortic rim ≥ 5 mm (79%). However, continuous occluder-aorta contact was more frequent in those with an aortic rim < 5 mm (95% vs 50%). Factors influencing aortic root strain after occluder implantation included the pattern of occluder-aorta relationship and the occluder/body surface area ratio. CONCLUSIONS: Most interatrial septal occluders are in contact with the aortic root, even in patients with ASDs with a sufficient aortic rim and in patients with PFOs. However, continuous occluder-aorta contact is more likely in patients with ASDs with a deficient aortic rim. The pattern of occluder-aorta relationship and the occluder/body surface area ratio affect aortic root strain.

Complement components (C3, C4) as inflammatory markers in asthma.

OBJECTIVE: To assess the serum levels of complement factors C3 and C4 in Egyptian asthmatic children. METHODS: This case-controlled study comprised of 60 Egyptian children with the diagnosis of bronchial asthma (not in acute attack) and 60 age-and sex-matched healthy controls. All candidates were subjected to a thorough clinical study, complete blood counts, absolute eosinophil count and serum complements (C3, C4). RESULTS: Serum C3 was significantly higher in asthmatics when compared to controls (140.60 +/- 38.80 mg/dl vs 107.70 +/- 45.00 mg/dl respectively, (p = 0.01). However, differences in serum C4 levels were not significant (41.30+/-48.80 mg/dl vs 44.60 +/- 39.70 mg/dl respectively, p = 0.69). There was a significant positive correlation between severity of asthma and serum C3 (p=0.02) but not with serum C4. CONCLUSIONS: Serum levels of C3 - but not C4 - are elevated in children with stable asthma, with a positive correlation between serum C3 and severity of asthma.