WITHDRAWN: Car parking location tracking, routing and occupancy monitoring system using cloud infrastructure

Arun, C.; Karthick, S.; Selvakumarasamy, S.; James, S. Joseph

doi:10.1016/j.matpr.2021.01.449

Cited by 6 publications

(8 citation statements)

References 12 publications

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“…There are various word embedding techniques in the literature. A few of them: Bag of Words, 35 Word2Vec, 36 Glove, 37 TF‐IDF, 38 and LDA. 39 Fu et al 40 used Word2Vec to represent datasets as input to the recursive autoencoders.…”

Section: Related Workmentioning

confidence: 99%

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A novel TCNN–Bi‐LSTM deep learning model for predicting sentiments of tweets about COVID‐19 vaccines

Aslan

2022

Concurrency and Computation

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Many researchers in various disciplines have focused on extracting meaningful information from social media platforms in recent years. Identification of behaviors and emotions from user posts is examined under the heading of sentiment analysis (SA) studies using the natural language processing (NLP) techniques. In this study, a novel TCNN-Bi-LSTM model using the two-stage convolutional neural network (TCNN) and bidirectional long short-term memory (Bi-LSTM) architectures was proposed. While TCNN layers enable the extraction of strong local features, the output of these layers feeds the Bi-LSTM model that remembers forward-looking information and capture long-term dependencies. In this study, first, preprocessing steps were applied to the raw dataset. Thus, strong features were extracted from the obtained quality dataset using the FastText word embedding technique that pre-trained with location-based and sub-word information features. The experimental results of the proposed method are promising compared to the baseline deep learning and machine learning models. Also, experimental results show that while the FastText data embedding technique achieves the best performance compared to other word embedding techniques in all deep learning classification models, it has not had the same outstanding success in machine learning models. This study aims to investigate the sentiments of tweets about the COVID-19 vaccines and comments on these tweets among Twitter users by using the power of Twitter data. A new dataset collected from Twitter was constructed to be used in experimental results. This study will facilitate detecting inappropriate, incomplete, and erroneous information about vaccination. The results of this study will enable society to broaden its perspective on the administered vaccines. It can also assist the government and healthcare agencies in planning and implementing the vaccination's promotion on time to achieve the herd immunity provided by the vaccination.

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Section: Related Workmentioning

confidence: 99%

“…There are various word embedding techniques in the literature. A few of them: Bag of Words,35 Word2Vec,…”

mentioning

confidence: 99%

A novel TCNN–Bi‐LSTM deep learning model for predicting sentiments of tweets about COVID‐19 vaccines

Aslan

2022

Concurrency and Computation

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“…However, it has become the target of malicious apps, and this demand has evolved with more security difficulties [11]. MalDozer, an automated Android malware analysis and family attribution framework that depends on orders of classification using deep learning classifiers, was suggested in [12][13][14][15][16]. An Android malware detection system (APK Auditor) is based on permissions that employ static analysis to analyze and classify Android applications as benign or malicious.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Study on Detection of Malware and Benign on the Internet Using Machine Learning Classifiers

Pavithra

Samy

2022

Mathematical Problems in Engineering

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The exponential growth in network usage has opened the way for people who use the Internet to be exploited. A phishing attack is the most effective way to obtain sensitive information about a target individual without their knowledge over the Internet. Phishing detection has an increased false-positive rate and is inaccurate. The motivation behind the research is to analyze and classify the applications among malware or benign with less time complexity. The main purpose is to find the algorithm which provides better accuracy for detecting the adware. The comparative analysis was made with three machine learning classifiers to find a better one. Random forest, SVM, and naïve Bayes were selected because of the better results obtained in previous research papers. Using a confusion matrix, the classifier methods were evaluated for accuracy, precision, recall, and F-measure with positive rates of both true and false. This research indicates that there are a number of classifiers that, if accurately detected, offer better reliable phishing detection outcomes. Random forest has proven to be an effective classifier with 0.9947 accuracy and a 0.017 false-positive rate. In this study, the comparative analysis reveals that the best ML classifiers have a lesser prediction accuracy for spoofing threat identification, implying that nonphishing programmers can use the best ML classifiers to evaluate the attributes of spoofing threat recognition and classification.

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“…In one of them, [48] signalled the necessity of developing intelligent systems for guiding parking spaces based on the knowledge of the actual parking lot occupancy. The possibility of calculating this parameter exists owing to the existing systems and further work is observed in this area [10], [12] and [50]. Based on the research by Fleger et al [38], it was found that in public parking spaces, drivers always or often find a free space in only 11% of cases.…”

Section: Parkingsmentioning

confidence: 99%

“…These issues, in turn, are analysed in terms of developing algorithms to reduce congestion and the negative impact on the environment when searching for parking spaces [9], [10]. The need to optimize parking or navigation systems is also indicated in [11], [12]. Within the scope of ISRB, researchers have also investigated the impact of vacancy search on network congestion or travel times [13].…”

Section: Introductionmentioning

confidence: 99%

Parking Lots Assignment Algorithm for Vehicles Requiring Specific Parking Conditions in Vehicle Routing Problem

Betkier¹,

Żak

Mitkow

2021

IEEE Access

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Based on the analysis of the current literature, mathematical modelling of the studied phenomenon was carried out. It was conducted according to the graph theory for vehicles transporting dangerous, oversized, and valuable cargo, moving within a transport network. The mapping of the parking areas included organisational, technical, and security aspects. The main algorithm determines the driving routes based on the parkings that meet the requirements specified by the carrier. These routes can ultimately be analysed and evaluated based on the parameters resulting from the formulated criterion functions. To verify the performance of the algorithm, its implementation was carried out in a computer environment using the Neo4j graph database. The transport network, consisting of 462 transport nodes and 602 transport links, was mapped based on the real national road network of the province in Poland. Data from 113 parking locations in the study area were included in the analysis. The research covered a total of three case studies, one for each vehicle type requiring specific parking conditions, for various input data. The results obtained allowed us to assess the validity of selecting particular routes and evaluating the scalability of the solution. The result of this work is a method, which, on the one hand, fits the current transportation requirements. On the other hand, it lends itself to scaling, extension by additional logical constraints, and is compatible with modern parking systems.INDEX TERMS vehicle routing problem, parking assignment, parking requirements, transport planning, transportation software, truck driver scheduling

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WITHDRAWN: Car parking location tracking, routing and occupancy monitoring system using cloud infrastructure

Cited by 6 publications

References 12 publications

A novel TCNN–Bi‐LSTM deep learning model for predicting sentiments of tweets about COVID‐19 vaccines

A novel TCNN–Bi‐LSTM deep learning model for predicting sentiments of tweets about COVID‐19 vaccines

A Comparative Study on Detection of Malware and Benign on the Internet Using Machine Learning Classifiers

Parking Lots Assignment Algorithm for Vehicles Requiring Specific Parking Conditions in Vehicle Routing Problem

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