Rabia Riaz

Word embeddings are essential to natural language processing tasks because they contain a single word’s syntactic and semantic information. Word embeddings have been developed widely for numerous spoken languages across the globe like English. The research community needs to pay more attention to the Urdu language despite its significant number of speakers, which amounts to approximately 231.3 million individuals. Urdu is a complex language because word boundaries in Urdu are unspecified, as it does not employ delimiters between words. The compound word, a multiword expression, is a more complex word consisting of many strings or independent base words. Traditionally, compound words are identified during the word segmentation using bigram or trigram approaches. The challenge with these techniques is that they do not produce meaningful words. This study uses morphological rule-based compound words in Urdu text documents. For text representation, a self-trained morphological rule-based compound word embedding (Cword2vec) based on the word2vec model is proposed for Urdu text sentiment analysis. The performance of self-trained morphological rule-based compound word embedding was then evaluated using four well-known deep learning models, i.e., long short-term memory (LSTM), bidirectional LSTM (BiLSTM), convolutional neural networks (CNN), and convolutional LSTM (C-LSTM) for sentiment analysis. We also compare the performance of morphological rule-based compound words with traditional compound word identification techniques such as bigrams and trigrams. Regardless of the classification model, word embedding using our proposed morphological rule-based compound words outperformed in terms of precision, recall, F1 score, and accuracy than bigrams and trigrams.

Advanced technologies, driven by extensive data analysis, support the concept of intelligent cities, which aim to enhance the quality of people’s lives, minimize the consumption of energy, reduce pollution, and promote economic growth. The transportation network is a crucial component of this vision in urbanized cities. However, a massive increase in road traffic poses a significant challenge to achieving this vision. Developing an intelligent transportation system requires accurately predicting the traffic speed. This paper proposes a novel deep stacking-based Ensemble model with a two-layer structure to address the problem of forecasting traffic speed in urbanized transportation networks to solve traffic congestion problems. Firstly, advanced machine learning such as eXtreme Gradient Boosting(XGB), Random Forest(RF), and Extra Tree(ET) as base learners are used to predict short-term traffic speed. In the next phase, the Multi-Layer Perceptron (MLP) as a meta-learner technique, employing various combinations of the aforementioned approaches is used to enhance the accuracy of traffic speed prediction. The proposed stacking-based approach has the capability to analyze, extract, and aggregate various features from primary traffic speed data in order to generate more refined and accurate forecasts. This study used a publicly available dataset of Floating Cars Data collected from real transportation networks for evaluation. Mutual information regression is used as a feature selection technique to obtain the features from the dataset for the training of these models. The performance results are compared with state-of-the-art traffic prediction models. Results show that the proposed stacking-based ensemble strategy outperforms conventional approaches by a large margin such as HA, KNN, SVR, DT, T-GCN, and A3TGCN models. The results demonstrate a notable reduction of 9.71% in RMSE and 15.4% in MAE, indicating enhanced accuracy. Furthermore, our approach achieved a substantial improvement of 13.80% in $\mathbb {R}^{2}$ and 11.64% in EV for the 15-minute prediction horizon.

The current research has aimed to investigate and develop machine-learning approaches by using the data in the dataset to be applied to classify location-based social network data and predict user activities based on the nature of various locations (such as entertainment). The analysis of user activities and behavior from location-based social network data is often based on venue types, which require the input of data into various categories. This has previously been done through a tedious and time-consuming manual method. Therefore, we proposed a novel approach of using machine-learning models to extract these venue categories. In this study, we used a Weibo dataset as the main source of research and analyzed machine-learning methods for more efficient implementation. We proposed four models based on well-known machine-learning techniques, including the generalized linear model, logistic regression, deep learning, and gradient-boosted trees. We designed, tested, and evaluated these models. We then used various assessment metrics, such as the Receiver Operating Characteristic or Area Under the Curve, Accuracy, Recall, Precision, F-score, and Sensitivity, to show how well these methods performed. We discovered that the proposed machine-learning models are capable of accurately classifying the data, with deep learning outperforming the other models with 99% accuracy, followed by gradient-boosted tree with 98% and 93%, generalized linear model with 90% and 85%, and logistic regression with 86% and 91%, for multiclass distributions and single class predictions, respectively. We classified the data using our machine-learning models into the 10 classes we used in our previous study and predicted tourist destinations among the data to demonstrate the effectiveness of using machine learning for location-based social network data analysis, which is vital for the development of smart city environments in the current technological era.

The Internet of Things (IoT) strongly influences the world economy; this emphasizes the importance of securing all four aspects of the IoT model: sensors, networks, cloud, and applications. Considering the significant value of public-key cryptography threats on IoT system confidentiality, it is vital to secure it. One of the potential candidates to assist in securing public key cryptography in IoT is quantum computing. Although the notion of IoT and quantum computing convergence is not new, it has been referenced in various works of literature and covered by many scholars. Quantum computing eliminates most of the challenges in IoT. This research provides a comprehensive introduction to the Internet of Things and quantum computing before moving on to public-key cryptography difficulties that may be encountered across the convergence of quantum computing and IoT. An enhanced architecture is then proposed for resolving these public-key cryptography challenges using SimuloQron to implement the BB84 protocol for quantum key distribution (QKD) and one-time pad (OTP). The proposed model prevents eavesdroppers from performing destructive operations in the communication channel and cyber side by preserving its state and protecting the public key using quantum cryptography and the BB84 protocol. A modified version is introduced for this IoT situation. A traditional cryptographic mechanism called “one-time pad” (OTP) is employed in hybrid management.

Vehicular Ad-hoc network (VANET) is an imminent technology having both exciting prospects and substantial challenges, especially in terms of security. Due to its distributed network and frequently changing topology, it is extremely prone to security attacks. The researchers have proposed different strategies for detecting various forms of network attacks. However, VANET is still exposed to several attacks, specifically Sybil attack. Sybil Attack is one of the most challenging attacks in VANETS, which forge false identities in the network to undermine communication between network nodes. This attack highly impacts transportation safety services and may create traffic congestion. In this regard, a novel collaborative framework based on majority voting is proposed to detect the Sybil attack in the network. The framework works by ensembling individual classifiers, i.e., K-Nearest Neighbor, Naïve Bayes, Decision Tree, SVM, and Logistic Regression in a parallel manner. The Majority Voting (Hard and Soft) mechanism is adopted for a final prediction. A comparison is made between Majority Voting Hard and soft to choose the best approach. With the proposed approach, 95% accuracy is achieved. The proposed framework is also evaluated using the Receiver operating characteristics curve (ROC-curve).

Braille is used as a mode of communication all over the world. Technological advancements are transforming the way Braille is read and written. This study developed an English Braille pattern identification system using robust machine learning techniques using the English Braille Grade-1 dataset. English Braille Grade-1 dataset was collected using a touchscreen device from visually impaired students of the National Special Education School Muzaffarabad. For better visualization, the dataset was divided into two classes as class 1 (1–13) (a–m) and class 2 (14–26) (n–z) using 26 Braille English characters. A position-free braille text entry method was used to generate synthetic data. N = 2512 cases were included in the final dataset. Support Vector Machine (SVM), Decision Trees (DT) and K-Nearest Neighbor (KNN) with Reconstruction Independent Component Analysis (RICA) and PCA-based feature extraction methods were used for Braille to English character recognition. Compared to PCA, Random Forest (RF) algorithm and Sequential methods, better results were achieved using the RICA-based feature extraction method. The evaluation metrics used were the True Positive Rate (TPR), True Negative Rate (TNR), Positive Predictive Value (PPV), Negative Predictive Value (NPV), False Positive Rate (FPR), Total Accuracy, Area Under the Receiver Operating Curve (AUC) and F1-Score. A statistical test was also performed to justify the significance of the results.

The transfer of information is a demanding issue, particularly due to the presence of a large number of eavesdroppers on communication channels. Sharing medical service records between different clinical jobs is a basic and testing research topic. The particular characteristics of blockchains have attracted a large amount of attention and resulted in revolutionary changes to various business applications, including medical care. A blockchain is based on a distributed ledger, which tends to improve cyber security. A number of proposals have been made with respect to the sharing of basic medical records using a blockchain without needing earlier information or the trust of patients. Specialist service providers and insurance agencies are not secure against data breaches. The safe sharing of clinical records between different countries, to ensure an incorporated and universal medical service, is also a significant issue for patients who travel. The medical data of patients normally reside on different healthcare units around the world, thus raising many concerns. Firstly, a patient’s history of treatment by different physicians is not accessible to the doctor in a single location. Secondly, it is very difficult to secure widespread data residing in different locations. This study proposed record sharing in a chain-like structure, in which every record is globally connected to the others, based on a blockchain under the suggestions and recommendations of the HL7 standards. This study focused on making medical data available, especially of patients who travel in different countries, for a specific period of time after validating the required authentication. Authorization and authentication are performed on the Shibboleth identity management system with the involvement of patient in the sanction process, thereby revealing the patient data for the specific period of time. The proposed approach improves the performance with respect to other record sharing systems, e.g., it reduces the time to read, write, delete, and revoke a record by a noticeable margin. The proposed system takes around three seconds to upload and 7.5 s to download 250 Mb of data, which can contain up to sixteen documents, over a stable network connection. The system has a latency of 413.76 ms when retrieving 100 records, compared to 447.9 and 459.3 ms in previous systems. Thus, the proposed system improved the performance and ensured seclusion by using a blockchain.

Revolution in technology is changing the way visually impaired people read and write Braille easily. Learning Braille in its native language can be more convenient for its users. This study proposes an improved backend processing algorithm for an earlier developed touchscreen-based Braille text entry application. This application is used to collect Urdu Braille data, which is then converted to Urdu text. Braille to text conversion has been done on Hindi, Arabic, Bangla, Chinese, English, and other languages. For this study, Urdu Braille Grade 1 data were collected with multiclass (39 characters of Urdu represented by class 1, Alif (), to class 39, Bri Yay (ے). Total (N=144) cases for each class were collected. The dataset was collected from visually impaired students from The National Special Education School. Visually impaired users entered the Urdu Braille alphabets using touchscreen devices. The final dataset contained (N=5638) cases. Reconstruction Independent Component Analysis (RICA)-based feature extraction model is created for Braille to Urdu text classification. The multiclass was categorized into three groups (13 each), i.e., category-1 (113), Alif-Zaal ( - ), category-2 (1426), Ray-Fay ( - ), and category-3 (2739), Kaaf-Bri Yay (ے - ), to give better vision and understanding. The performance was evaluated in terms of true positive rate, true negative rate, positive predictive value, negative predictive value, false positive rate, total accuracy, and area under the receiver operating curve. Among all the classifiers, support vector machine has achieved the highest performance with a 99.73 accuracy. For comparisons, robust machine learning techniques, such as support vector machine, decision tree, and K-nearest neighbors were used. Currently, this work has been done on only Grade 1 Urdu Braille. In the future, we plan to enhance this work using Grade 2 Urdu Braille with text and speech feedback on touchscreen-based android phones.

The Internet of Things (IoT) is shaping the world of every economy and client. By 2023, clients, companies, and governments will be able to install a total of 40 billion IoT devices worldwide. The 2021's challenges simply confirmed the necessity to secure all four elements of the IoT Model: Sensors, Networks, Cloud, and Applications. Therefore, it is important to be pragmatic regarding the extreme threats and high worth of attacks on secrecy in IoT systems. Although the concept of convergence of IoT and Quantum Computing is not a standby topic, it had been mentioned in several works of literature and coated by many researchers, however, nothing is getting ready for sensible applications thus far. Quantum Computing is not prepared nevertheless, it is so far from preparation on a billboard scale. Quantum computing resolves most of the issues in IoT however it occurs yet below the analysis phase and can take over a couple of years to induce in public obtainable use. During this research, the author will start through a broad introduction to the internet of things and Quantum Computing and carry on with issues that can be featured throughout the convergence of Quantum Computing and IoT. To solve these integration issues in terms of cryptography this research will also provide the answer. At long last, this research will call attention to the hybrid of both fields to existing IoT network design.