An Adaptive Real-Time Malicious Node Detection Framework Using Machine Learning in Vehicular Ad-Hoc Networks (VANETs).

Modern vehicle communication development is a continuous process in which cutting-edge security systems are required. Security is a main problem in the Vehicular Ad Hoc Network (VANET). Malicious node detection is one of the critical issues found in the VANET environment, with the ability to communicate and enhance the mechanism to enlarge the field. The vehicles are attacked by malicious nodes, especially DDoS attack detection. Several solutions are presented to overcome the issue, but none are solved in a real-time scenario using machine learning. During DDoS attacks, multiple vehicles are used in the attack as a flood on the targeted vehicle, so communication packets are not received, and replies to requests do not correspond in this regard. In this research, we selected the problem of malicious node detection and proposed a real-time malicious node detection system using machine learning. We proposed a distributed multi-layer classifier and evaluated the results using OMNET++ and SUMO with machine learning classification using GBT, LR, MLPC, RF, and SVM models. The group of normal vehicles and attacking vehicles dataset is considered to apply the proposed model. The simulation results effectively enhance the attack classification with an accuracy of 99%. Under LR and SVM, the system achieved 94 and 97%, respectively. The RF and GBT achieved better performance with 98% and 97% accuracy values, respectively. Since we have adopted Amazon Web Services, the network's performance has improved because training and testing time do not increase when we include more nodes in the network.

COVID-19 Detection Mechanism in Vehicles Using a Deep Extreme Machine Learning Approach.

COVID-19 is a rapidly spreading pandemic, and early detection is important to halting the spread of infection. Recently, the outbreak of this virus has severely affected people around the world with increasing death rates. The increased death rates are because of its spreading nature among people, mainly through physical interactions. Therefore, it is very important to control the spreading of the virus and detect people's symptoms during the initial stages so proper preventive measures can be taken in good time. In response to COVID-19, revolutionary automation such as deep learning, machine learning, image processing, and medical images such as chest radiography (CXR) and computed tomography (CT) have been developed in this environment. Currently, the coronavirus is identified via an RT-PCR test. Alternative solutions are required due to the lengthy moratorium period and the large number of false-negative estimations. To prevent the spreading of the virus, we propose the Vehicle-based COVID-19 Detection System to reveal the related symptoms of a person in the vehicles. Moreover, deep extreme machine learning is applied. The proposed system uses headaches, flu, fever, cough, chest pain, shortness of breath, tiredness, nasal congestion, diarrhea, breathing difficulty, and pneumonia. The symptoms are considered parameters to reveal the presence of COVID-19 in a person. Our proposed approach in Vehicles will make it easier for governments to perform COVID-19 tests timely in cities. Due to the ambiguous nature of symptoms in humans, we utilize fuzzy modeling for simulation. The suggested COVID-19 detection model achieved an accuracy of more than 90%.

Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control.

In vehicular ad hoc networks (VANETs), content pre-caching is a significant technology that improves network performance and lowers network response delay. VANET faces network congestion when multiple requests for the same content are generated. Location-based dependency requirements make the system more congested. Content pre-caching is an existing challenge in VANET; pre-caching involves the content's early delivery to the requested vehicles to avoid network delays and control network congestion. Early content prediction saves vehicles from accidents and road disasters in urban environments. Periodic data dissemination without considering the state of the road and surrounding vehicles are considered in this research. The content available at a specified time poses considerable challenges in VANET for content delivery. To address these challenges, we propose a machine learning-based, zonal/context-aware-equipped content pre-caching strategy in this research. The proposed model improves content placement and content management in the pre-caching mode for VANET. Content caching is achieved through machine learning, which significantly improves content prediction by pre-caching the content early to the desired vehicles that are part of the zone. In this paper, three algorithms are presented, the first is zone selection using the customized algorithm, the second is the content dissemination algorithm, and the third is the content pre-caching decision algorithm using supervised machine learning that improves the early content prediction accuracy by 99.6%. The cache hit ratio for the proposed technique improves by 13% from the previous techniques. The prediction accuracy of the proposed technique is compared with CCMP, MLCP, and PCZS+PCNS on the number of vehicles from 10 to 150, with an improved average of 16%. Finally, the average delay reduces over time compared with the state-of-the-art techniques of RPSS, MLCP, CCMP, and PCZS+PCNS. Finally, the average delay shows that the proposed method effectively reduces the delay when the number of nodes increases. The proposed solution improves the content delivery request while comparing it with existing techniques. The results show improved pre-caching in VANET to avoid network congestion.

Edge AI-Based Automated Detection and Classification of Road Anomalies in VANET Using Deep Learning.

Road surface defects are crucial problems for safe and smooth traffic flow. Due to climate changes, low quality of construction material, large flow of traffic, and heavy vehicles, road surface anomalies are increasing rapidly. Detection and repairing of these defects are necessary for the safety of drivers, passengers, and vehicles from mechanical faults. In this modern era, autonomous vehicles are an active research area that controls itself with the help of in-vehicle sensors without human commands, especially after the emergence of deep learning (DNN) techniques. A combination of sensors and DNN techniques can be useful for unmanned vehicles for the perception of their surroundings for the detection of tracks and obstacles for smooth traveling based on the deployment of artificial intelligence in vehicles. One of the biggest challenges for autonomous vehicles is to avoid the critical road defects that may lead to dangerous situations. To solve the accident issues and share emergency information, the Intelligent Transportation System (ITS) introduced the concept of vehicular network termed as vehicular ad hoc network (VANET) for achieving security and safety in a traffic flow. A novel mechanism is proposed for the automatic detection of road anomalies by autonomous vehicles and providing road information to upcoming vehicles based on Edge AI and VANET. Road images captured via camera and deployment of the trained model for road anomaly detection in a vehicle could help to reduce the accident rate and risk of hazards on poor road conditions. The techniques Residual Convolutional Neural Network (ResNet-18) and Visual Geometry Group (VGG-11) are applied for the automatic detection and classification of the road with anomalies such as a pothole, bump, crack, and plain roads without anomalies using the dataset from different online sources. The results show that the applied models performed well than other techniques used for road anomalies identification.

Architectural Distortion-Based Digital Mammograms Classification Using Depth Wise Convolutional Neural Network.

Architectural distortion is the third most suspicious appearance on a mammogram representing abnormal regions. Architectural distortion (AD) detection from mammograms is challenging due to its subtle and varying asymmetry on breast mass and small size. Automatic detection of abnormal ADs regions in mammograms using computer algorithms at initial stages could help radiologists and doctors. The architectural distortion star shapes ROIs detection, noise removal, and object location, affecting the classification performance, reducing accuracy. The computer vision-based technique automatically removes the noise and detects the location of objects from varying patterns. The current study investigated the gap to detect architectural distortion ROIs (region of interest) from mammograms using computer vision techniques. Proposed an automated computer-aided diagnostic system based on architectural distortion using computer vision and deep learning to predict breast cancer from digital mammograms. The proposed mammogram classification framework pertains to four steps such as image preprocessing, augmentation and image pixel-wise segmentation. Architectural distortion ROI's detection, training deep learning, and machine learning networks to classify AD's ROIs into malignant and benign classes. The proposed method has been evaluated on three databases, the PINUM, the CBIS-DDSM, and the DDSM mammogram images, using computer vision and depth-wise 2D V-net 64 convolutional neural networks and achieved 0.95, 0.97, and 0.98 accuracies, respectively. Experimental results reveal that our proposed method outperforms as compared with the ShuffelNet, MobileNet, SVM, K-NN, RF, and previous studies.

Different Types of Clinical Presentations and Stages of Retinoblastoma Among Children.

Introduction Retinoblastoma (Rb) is the most common intraocular malignant tumor of childhood. The different modes of Rb presentation comprise proptosis, anterior chamber inflammatory signs, spontaneous hyphema, secondary glaucoma, and strabismus. The primary aim of this study was to investigate the different clinical presentations and stages of Rb that may help in early detection and timely diagnosis to prevent the advancement of the disease and increase rates of survival in children.  Materials and methods This was a descriptive cross-sectional study conducted between December 2019 and May 2020 over a period of six months at a tertiary care hospital in Karachi, Pakistan. The sample size included 68 eyes of children with lesions of Rb at the time of presentation to the hospital. Brightness scans (B-scans), computed tomography (CT) scans, and magnetic resonance imaging (MRI) were performed. The International Intraocular Retinoblastoma Classification (IIRC) was used to stage each eye. In case of enucleation (if necessary) of the eye, the biopsy was performed to evaluate the histological features of cancer. All statistical analysis was performed using Statistical Package for Social Sciences version 17.0 (IBM Corp., Armonk, New York). Results The mean age of the children was 3.21 ± 1.75 years. Leukocoria was the most common clinical presentation observed in more than half (n = 35, 51.47%) of the sample population followed by proptosis reported in nearly two-fifths (n = 25, 36.76%), strabismus and phthisis bulbi observed in equal proportions (n = 3, 4.41%), and hypopyon documented in a minor proportion (n = 2, 2.94%) of patients. Regarding stages of the Rb disease, the most common stages were observed to be stage C and stage E.  Conclusions This study concludes that the early detection of Rb is possible through a better understanding of presenting features of the disease. It can prevent the progression of the disease to the advanced stages and decrease morbidity and mortality. The early detection of Rb can be made possible through the examination of red reflex on the regular check-ups of children as leukocoria is the most common clinical presentation.

Movie Review Summarization Using Supervised Learning and Graph-Based Ranking Algorithm.

With the growing information on web, online movie review is becoming a significant information resource for Internet users. However, online users post thousands of movie reviews on daily basis and it is hard for them to manually summarize the reviews. Movie review mining and summarization is one of the challenging tasks in natural language processing. Therefore, an automatic approach is desirable to summarize the lengthy movie reviews, and it will allow users to quickly recognize the positive and negative aspects of a movie. This study employs a feature extraction technique called bag of words (BoW) to extract features from movie reviews and represent the reviews as a vector space model or feature vector. The next phase uses Naïve Bayes machine learning algorithm to classify the movie reviews (represented as feature vector) into positive and negative. Next, an undirected weighted graph is constructed from the pairwise semantic similarities between classified review sentences in such a way that the graph nodes represent review sentences, while the edges of graph indicate semantic similarity weight. The weighted graph-based ranking algorithm (WGRA) is applied to compute the rank score for each review sentence in the graph. Finally, the top ranked sentences (graph nodes) are chosen based on highest rank scores to produce the extractive summary. Experimental results reveal that the proposed approach is superior to other state-of-the-art approaches.

Identification of phytotoxins in different plant parts of Brassica napus and their influence on mung bean.

Plants in Brassica genus have been found to possess strong allelopathic potential. They may inhibit seed germination and emergence of subsequent crops following them in a rotation system. Series of laboratory and greenhouse experiments were conducted to determine the allelopathic impacts of Brassica napus L. against mung bean. We studied (1) the effects of aqueous extract (5%) of different plant parts (root, stem, leaf, flower, and whole plant) of B. napus, (2) the effects of leaf and flower extracts of B. napus at 0, 1, 2, 3, and 4% concentrations, and (3) the effect of residues of different B. napus plant parts and decomposition periods (0, 7, 14, and 21 days) on germination and seedling growth of mung bean. Various types of phenolics including quercitin, chlorogenic acid, p-coumeric acid, m-coumaric acid, benzoic acid, caffeic acid, syringic acid, vanillic acid, ferulic acid, cinamic acid, and gallic acid were identified in plant parts of B. napus. Among aqueous extracts of various plant parts, leaf and flower were found to have stronger inhibitory effects on germination and seedling growth traits of mung bean, higher concentrations were more toxic. The decomposition period changed the phtotoxic effect of residues, more inhibitory effect was shown at 14 days decomposition while decomposition for 21 days reduced inhibitory effect. The more total water-soluble phenolic was found in 5% (w/v) aqueous extract and 5% (w/w) residues of B. napus flowers at 14 days of decomposition (89.80 and 10.47 mg L-1), respectively. The strong inhibitory effects of B. napus should be managed when followed in rotation.