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  1. Khan A, Gul MA, Zareei M, Biswal RR, Zeb A, Naeem M, et al.
    Comput Intell Neurosci, 2020;2020:7526580.
    PMID: 32565772 DOI: 10.1155/2020/7526580
    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.
  2. Bibi R, Saeed Y, Zeb A, Ghazal TM, Rahman T, Said RA, et al.
    Comput Intell Neurosci, 2021;2021:6262194.
    PMID: 34630550 DOI: 10.1155/2021/6262194
    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.
  3. Ahmad P, Khandaker MU, Muhammad N, Rehman F, Ullah Z, Khan G, et al.
    Appl Radiat Isot, 2020 Dec;166:109404.
    PMID: 32956924 DOI: 10.1016/j.apradiso.2020.109404
    The shortcomings in Boron neutron capture therapy (BNCT) and Hyperthermia for killing the tumor cell desired for the synthesis of a new kind of material suitable to be first used in BNCT and later on enable the conditions for Hyperthermia to destroy the tumor cell. The desire led to the synthesis of large band gap semiconductor nano-size Boron-10 enriched crystals of hexagonal boron nitride (10BNNCs). The contents of 10BNNCs are analyzed with the help of x-ray photoelectron spectroscopy (XPS) and counter checked with Raman and XRD. The 10B-contents in 10BNNCs produce 7Li and 4He nuclei. A Part of the 7Li and 4He particles released in the cell is allowed to kill the tumor (via BNCT) whereas the rest produce electron-hole pairs in the semiconductor layer of 10BNNCs suggested to work in Hyperthermia with an externally applied field.
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