This paper introduces a new local descriptor called radial transition local binary pattern (RTLBP) that is designed to extract more discriminative information from images. Unlike existing descriptors that use a 3 × 3 window, RTLBP uses a 5 × 5 pixel window and separates pixels into two scales. At the first scale, pixels are compared to the central pixel to generate binary values, similar to local binary pattern (LBP). At the second scale, pixels at major and non-major directions are compared differently to create two 8-bit patterns. This information is then processed using four primary and secondary directional radial pixels to create the feature descriptor. Finally, histograms are generated for each of the transformed images. The proposed technique outperforms state-of-the-art descriptors on two publicly available databases, demonstrating its effectiveness.

Summary The standard information retrieval systems mainly extract documents based on the relative keywords and this method is not effective since the related information can be only identified by extracting the semantics present in the text. Hence to overcome this problem, a semantic‐based query retrieval model is formulated in this article for query processing, identification, extraction, expansion, sorting, and filtering. A keyword expansion approach using the hybrid spatial bound whale optimization algorithm‐binary moth flame optimization algorithm is proposed in this work. In this way, the complexity associated with insufficient query information is overcome. The efficiency of the proposed model in extracting the top k relevant information is evaluated using the experiments conducted in the TREC data using different performance metrics such as precision@k, recall@k, mean reciprocal rank@k, mean average precision, and normalized discounted cumulative gain (NDCG@k). The proposed model achieves improved outcomes when compared to different state‐of‐art techniques such as i‐Dataquest, fuzzy logic, ontological framework for information extraction, and personal knowledge management in terms of precision@k, recall@k, mean reciprocal rank@k, exact match, F1‐score, and NDCG@k. The proposed model gives a mean reciprocal rank@k score of 0.8901, 0.8947, 0.8958, and 0.9014 for the k‐values 1, 3, 5, and 10, respectively. The MAP@k score for the top‐10 result suggestion retrieved is 0.39056. The exact match score of the proposed model for the newsgroup, SQuAD 1.0, and SQUAD 2.0 is 93.25, 94.65, and 95.25.

Advances in technology and high Internet penetration are leading to a large number of businesses going online. As a result, there is a substantial increase in the number of customers making online purchases and the number of items available online. However, with so many options available to choose from, users have to face the information overload problem. Several techniques have been developed to handle this, but the performance of the recommendation system (RS) has been recorded unprecedentedly. The collaborative filtering (CF) of RS is the most prevalent technique, which suggests personalized items to users based on their past preferences. The efficacy of this technique mainly depends on the similarity calculation, which the traditional or cognitive approach can ascertain. In the traditional approach, a similarity measure utilizes the user’s ratings on an item to compute the similarity. Most similarity measures in this approach suffer from either data sparsity and/or cold-start problems. To address both of them, a new similarity measure based on the Jaccard and Gower coefficients, the efficient Gowers–Jaccard–Sigmoid Measure (EGJSM), is proposed in this article. It also includes a nonlinear sigmoid function to penalize the bad ratings. The performance of EGJSM is evaluated by conducting experiments on benchmark datasets, and the results depict that the proposed technique outperforms several existing methods. Along with this, a cognitive similarity (CgS) measure has been proposed, which considers cognitive features such as genre and year of release along with rating information, to calculate similarity. The CgS method also outperforms the proposed EGJSM method and produces almost 4% and 1% lower mean absolute error (MAE) and root-mean-squared error (RMSE) values than that.

Timely delivery of critical information and data may reduce the chances of road accidents using vehicular adhoc networks. The vehicular ad-hoc network uses the multihop data dissemination technique due to the limited transmission capabilities of the vehicles. The dynamic topology of the network due to the presence of highly mobile vehicles experiences frequent path disconnections in the communication network. Due to these challenges, the timely delivery of messages to the intended recipient is still a challenging issue. The core attention of this work is to report the timely message delivery problem in the vehicular communication network. In this work, we proposed a secure and optimized data routing technique using cross-layer optimization for the heterogeneous ad-hoc network. Firstly we calculate the trust value of nodes based on the entity-centric trust model. Further, we estimate the link durability and probability of collision in the network of vehicles. Our prime aim is to increase the link reliability and duration to deliver the message on time. We also minimize the probability of data collision and improve the delivery ratio of the network. The proposed optimized data dissemination model has been tested with several network parameters. The experimental results show significant improvements in terms of minimized delay, packet loss ratio.

Summary The Internet of Things (IoT)–enabled healthcare environment irregularly requires the resources from the Cloud to handle massive amounts of data, which impacts the response times of the Cloud. A typical healthcare application scenario could be to monitor the heart‐patients who require the highest response times, immediate attention of all the stakeholders, and very quick decisions. This paper replaces the default First‐Come‐First‐Serve (FCFS) Virtual Machine (VM) allocation scheme of the Cloud computing in CloudSim with the implementation of Ant Colony Optimization (ACO) by the efficient tuning of parameters. An IoT‐enabled healthcare environment is designed, which irregularly requires the resources from the Cloud to handle massive amounts of data to assess the impact of the response times of the Cloud. Several experiments have been carried out to assess the optimal VM allocation using ACO by varying different parameters like the variation of the number of ants, the strength of pheromone, the number of VMs, the number of hosts, the strength of computing power of processing elements, the number of users, and the size of the workloads. Experimental results indicate that the ACO optimally utilizes the resources in a Cloud when compared to the default FCFS strategy. The results indicate that on an average, the ACO gives 25% to 30% improved response times than FCFS in the given healthcare and Cloud scenarios.

Internet-of-Things (IoT) is revolutionizing the healthcare by providing high-quality services, lowering the costs, and increasing the efficiency of management by allowing the physical objects to integrate with computer systems to collect the sensed data and process as per the need. Healthcare industries will also get benefited due to reduced investments and management, automated services, better disease diagnosis/analysis, and minimum operations and maintenance. Wireless Body Area Network (WBAN), an element of IoT, enables several sensor nodes attached to a body that generates enormous volumes of healthcare data over the period of a patient. In life-critical pervasive healthcare applications, the data rates from the WBAN is unpredictable, requiring uneven resources in short time intervals to provide qualitative services. Cloud is abundant with pooled resources ready to meet such unpredictable workloads and rapidly deployable to handle massive amounts of data. This paper presents a mechanism that assesses the default resource allocation strategy regarding its response time in simulated IoT healthcare workloads which will raise irregular requirements of the resources from the cloud to handle massive amounts of data. Several experiments are conducted to study the optimal virtual machine allocation to meet the irregular resource requirements from cloud to suit to WBAN, the IoT scenario. Finally, the paper concludes with the reported results.