The idea of Smart Cities (SCs) was developed to digitize conventional urban living areas and redevelop using digital equipment to enhance lifestyle and resident security. Smart cities cover a wide range of applications such as smart government, smart energy, smart transportation, smart health, and smart education. In this paper, we have examined the mobility-related application e.g., smart transportation. We have focused on related to smart transportation like traffic safety, passenger and driver safety, obstacle detection, accident prevention, crime prevention, and detection, and more importantly smart vehicles like UAVs which played a key role in making these gadgets easy to use in urban environments. For a brief overview, 63 academic publications are examined in the field of ITS and SC technologies. Furthermore, we have provided possible solutions in terms of security in the smart transportation system.

Emotion detection (ED) and sentiment analysis (SA) play a vital role in identifying an individual’s level of interest in any given field. Humans use facial expressions, voice pitch, gestures, and words to convey their emotions. Emotion detection and sentiment analysis in English and Chinese have received much attention in the last decade. Still, poor-resource languages such as Urdu have been mostly disregarded, which is the primary focus of this research. Roman Urdu should also be investigated like other languages because social media platforms are frequently used for communication. Roman Urdu faces a significant challenge in the absence of corpus for emotion detection and sentiment analysis because linguistic resources are vital for natural language processing. In this study, we create a corpus of 1021 sentences for emotion detection and 20,251 sentences for sentiment analysis, both obtained from various areas, and annotate it with the aid of human annotators from six and three classes, respectively. In order to train large-scale unlabeled data, the bag-of-word, term frequency-inverse document frequency, and Skip-gram models are employed, and the learned word vector is then fed into the CNN-LSTM model. In addition to our proposed approach, we also use other fundamental algorithms, including a convolutional neural network, long short-term memory, artificial neural networks, and recurrent neural networks for comparison. The result indicates that the CNN-LSTM proposed method paired with Word2Vec is more effective than other approaches regarding emotion detection and evaluating sentiment analysis in Roman Urdu. Furthermore, we compare our based model with some previous work. Both emotion detection and sentiment analysis have seen significant improvements, jumping from an accuracy of 85% to 95% and from 89% to 93.3%, respectively.

Flying Ad-hoc networks are emergent area in Ad-hoc networks evolved from MANETs and VANETs. Small unmanned aerial vehicles (UAVs) are used in FANETs applications and these small UAVs have limited resources while efficiently utilization of these resources is most critical task in real time monitoring of FANETs application. Network consumes its resources in path selection process and data routing from source to destination. Selecting of efficient routing protocol to utilize all available resources played vital role in extending network life time. In this article fisheye state routing (FSR) protocol is implemented in FANET and compare networks performance in term of channel utilization, link utilization vs throughput and packet delivery ratio (PDR) with distance sequence distance vector (DSDV), optimized link state routing (OLSR), adhoc on demand distance vector (AODV), dynamic source routing (DSR) and temperary ordered routing protocol (TORA). Experimental analysis slows that FSR is good in term of PDR (16438 packets delivered), channel utilization (89%) and link vs throughput from the rest of routing protocols after addressing of these problems UAVs resources are efficiently utilized (energy).

Coronary heart disease has an intense impact on human life. Medical history-based diagnosis of heart disease has been practiced but deemed unreliable. Machine learning algorithms are more reliable and efficient in classifying, e.g., with or without cardiac disease. Heart disease detection must be precise and accurate to prevent human loss. However, previous research studies have several shortcomings, for example,take enough time to compute while other techniques are quick but not accurate. This research study is conducted to address the existing problem and to construct an accurate machine learning model for predicting heart disease. Our model is evaluated based on five feature selection algorithms and performance assessment matrix such as accuracy, precision, recall, F1-score, MCC, and time complexity parameters. The proposed work has been tested on all of the dataset'sfeatures as well as a subset of them. The reduction of features has an impact on theperformance of classifiers in terms of the evaluation matrix and execution time. Experimental results of the support vector machine, K-nearest neighbor, and logistic regression are 97.5,95 , and 93 (accuracy) with reduced computation timesof 4.4, 7.3, and 8seconds respectively.

Malaria is an infectious disease spread by mosquitoes that effect humans and other animals. It is a massive threat to humanity, with instances growing each year. It is essential to prevent and diagnose malaria immediately and efficiently. For the time being, conventional methods are used for diagnosing malaria in which the patient's blood sample is examined by microscope or by using malaria RTD kits. This approach has several limitations because it requires medical expertise, is expensive, takes a long time, and the results are unsatisfactory. Artificial intelligence-based systems can prevent and help in diagnose of this infectious disease. Because of these limitations, the proposed work has proposed an AI-based diagnosis system that can detect malaria parasites immediately and efficiently. In the proposed experiment, we have applied four different pre-trained deep learning models on the image dataset with some preprocessing and optimization techniques for malaria parasite detection. After investigations, evaluation matrices such as precision, Recall, F1-score, sensitivity, and specificity are used to measure the performance of the proposed models. The Inception-Resnet outperformed by achieving 95% accuracy, VGG16 achieved 92% accuracy, inception achieved 93% accuracy, and VGG19 achieved 91% accuracy. The positive outcomes of this study show that this approach performs much better than the approaches currently used. Furthermore, the proposed method is relevant to health experts for screening purposes.

Lung cancer illness seriously impacts people's health. Medical history-based detection of lung cancers has been utilized but it has unsatisfactory results. Artificial intelligence algorithms are more precise and efficient in classifying lung cancer patients and healthy persons. Additionally, the medical history-based diagnosis of lung cancer disease is costly and time consuming. The life of lung cancer disease is very short after detection. Artificial intelligence-based diagnosis systems can detect the lung cancer disease early and efficiently. However, previous research work as several limitations, for example, some techniques computation time is very high but their accuracy is good while some techniques have less computation time but accuracy is not good. The proposed work suggests a deep convolutional neural network-based diagnosis system for lung cancer disease early and accurate detection. We made use of publically available dataset downloaded from Kaggle online repository and applied deep convolutional neural network for accurate lung cancer detection. Furthermore, we have applied some preprocessing and features selection techniques such as max, min, standard deviation and variance threshold. The proposed CNN model achieved 99.2% validation accuracy, 99.8% training accuracy, 99% precision, and 99% recall in minimum computation time of 6 sec which is acceptable.