Internet of Vehicles (IoV) is turning out to be one of the first impressive examples of Internet of Things (IoT). In IoV, the factors of connectivity and interaction/information dispersion are equally important as sensing/actuating, context-awareness, services provisioning, etc. However, most of the researches related to connectivity and interaction are constrained to physics of signaling and data science (semantics/contents), respectively. Very rapidly, the meanings of these factors are changing due to evolution of technologies from physical to social domain. For example, Social IoV (SIoV) is a term used to represent when vehicles build and manage their own social network. Hence, in addition to physical aspects, the social aspects of connectivity and information dispersion towards these systems of future should also be researched, a domain so far ignored in this particular context. In this paper, an agent-based model of information sharing (for context-based recommendations) of a hypothetical population of smart vehicles is presented. Some important hypotheses are tested under reasonable connectivity and data constraints. The simulation results reveal that closure of social ties and its timing impacts the dispersion of novel information (necessary for a recommender system) substantially. It is also observed that as the network evolves due to incremental interactions, the recommendations guaranteeing a fair distribution of vehicles across equally good competitors is not possible.

Impulsive noise is one of the major challenges for reliable transmission over power lines. Interleavers provide higher protection against the impulsive noise by dispersing information across the channel and spreading the burst of errors over multiple codewords. Multi-fold turbo (MFT) coding is a technique that improves the communication reliability using multiple interleavers. In the MFT codes, each data subsequence is equally protected. For applications in which data constitute information with various levels of importance, it is intuitive to offer the more important subsequence, a stronger protection. A modified form of the MFT codes capable of providing unequal error protection over a two-user power-line binary adder channel is proposed here. As a benchmark, two test images are transmitted across the channel. The trellis-based iterative algorithm is modified for the twouser scenario to decode the received signal. The simulation results show a gain of 1.5 dB for the modified MFT code over the conventional turbo codes for each of the transmitted images. A gain of 2 dB is also recorded for the most protected component of each image over the least protected components.

In Natural Language Processing (NLP), topic modeling is the technique to extract abstract information from documents with huge amount of text. This abstract information leads towards the identification of the topics in the document. One way to retrieve topics from documents is keyphrase extraction. Keyphrases are a set of terms which represent high level description of a document. Different techniques of keyphrase extraction for topic prediction have been proposed for multiple languages i.e. English, Arabic, etc. However, this area needs to be explored for other languages e.g. Urdu. Therefore, in this paper, a novel unsupervised approach for topic prediction for Urdu language has been introduced which is able to extract more significant information from the documents. For this purpose, the proposed TOP-Rank system extracts keywords from the document and ranks them according to their position in a sentence. These keywords along with their ranking scores are utilized to generate keyphrases by applying syntactic rules to extracts more meaningful topics. These keyphrases are ranked according to the keywords scores and re-ranked with respect to their positions in the document. Finally, our proposed model identifies topranked keyphrases as topical significance and keyphrase with the highest score is selected as the topic of the document. Experiments are performed on two different datasets and performance of the proposed system is compared with existing state-of-the-art techniques. Results have shown that our proposed system outperforms existing techniques and holds the ability to produce more meaningful topics.

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Cotton is one of the world’s most economically significant agricultural products; however, it is susceptible to numerous pest and virus attacks during the growing season. Pests (whitefly) can significantly affect a cotton crop, but timely disease detection can help pest control. Deep learning models are best suited for plant disease classification. However, data scarcity remains a critical bottleneck for rapidly growing computer vision applications. Several deep learning models have demonstrated remarkable results in disease classification. However, these models have been trained on small datasets that are not reliable due to model generalization issues. In this study, we first developed a dataset on whitefly attacked leaves containing 5135 images that are divided into two main classes, namely, (i) healthy and (ii) unhealthy. Subsequently, we proposed a Compact Convolutional Transformer (CCT)-based approach to classify the image dataset. Experimental results demonstrate the proposed CCT-based approach’s effectiveness compared to the state-of-the-art approaches. Our proposed model achieved an accuracy of 97.2%, whereas Mobile Net, ResNet152v2, and VGG-16 achieved accuracies of 95%, 92%, and 90%, respectively.