Protein subcellular localization prediction is of great significance in bioinformatics and biological research. Most of the proteins do not have experimentally determined localization information, computational prediction methods and tools have been acting as an active research area for more than two decades now. Knowledge of the subcellular location of a protein provides valuable information about its functionalities, the functioning of the cell, and other possible interactions with proteins. Fast, reliable, and accurate predictors provides platforms to harness the abundance of sequence data to predict subcellular locations accordingly. During the last decade, there has been a considerable amount of research effort aimed at developing subcellular localization predictors. This paper reviews recent subcellular localization prediction tools in the Eukaryotic, Prokaryotic, and Virus-based categories followed by a detailed analysis. Each predictor is discussed based on its main features, strengths, weaknesses, algorithms used, prediction techniques, and analysis. This review is supported by prediction tools taxonomies that highlight their rele- vant area and examples for uncomplicated categorization and ease of understandability. These taxonomies help users find suitable tools according to their needs. Furthermore, recent research gaps and challenges are discussed to cover areas that need the utmost attention. This survey provides an in-depth analysis of the most recent prediction tools to facilitate readers and can be considered a quick guide for researchers to identify and explore the recent literature advancements.

We live in the era of Intelligent Transport Systems (ITS), which is an extension of Vehicular AdHoc Networks (VANETs). In VANETs, vehicles act as nodes connected with each other and sometimes with a public station. Vehicles continuously exchange and collect information to provide innovative transportation services; for example, traffic management, navigation, autonomous driving, and the generation of alerts. However, VANETs are extremely challenging for data collection, due to their high mobility and dynamic network topologies that cause frequent link disruptions and make path discovery difficult. In this survey, various state-of-the-art data collection protocols for VANETs are discussed, based on three broad categories, i.e., delay-tolerant, best-effort, and real-time protocols. A taxonomy is designed for data collection protocols for VANETs that is essential to add precision and ease of understandability. A detailed comparative analysis among various data collection protocols is provided to highlight their functionalities and features. Protocols are evaluated based on three parametric phases. First, protocols investigation based on six necessary parameters, including delivery and drop ratio, efficiency, and recovery strategy. Second, a 4-D functional framework is designed to fit most data collection protocols for quick classification and mobility model identification, thus eradicating the need to read extensive literature. In the last, in-depth categorical mapping is performed to deep dive for better and targeted interpretation. In addition, some open research challenges for ITS and VANETs are discussed to highlight research gaps. Our work can thus be employed as a quick guide for researchers to identify the technical relevance of data collection protocols of VANETs.

With the dynamic growth of smart devices and connectivity with vehicles, researchers are getting growing interest due to variety of enormous application scenarios. Vehicular ad hoc networks (VANETs) comprises of huge number of vehicles and roadside units that communicate with each other in several realtime scenarios and internet application services. Security in VANET is more crucial, because of different network layers and trusting on unknown intermediate vehicles. There is critical demand of efficient trust management for VANETs, because it prevents fabricated information directed by suspicious nodes connected to the network. A number of factors including lack of infrastructure, autonomous modules, auto-pilot support and dynamic topology due to mobility can result in hardening the reliable trust management solutions. In this paper, we present a comprehensive overview of trust management schemes for routing protocols. This survey specifically elaborates Trust management approaches applicable for secure release of information for real time application services. Moreover, these schemes are analyzed and presented in a tabular form to highlight the concept, methodology, algorithm, QoS and performance capabilities. It can help the researcher in this field to quickly identify the weak and strong features of schemes. We have also provided categorical discussion for comparative overview. As a finals task, we have identified open research challenges.

Safety Critical Systems (SCS) are critical in organizing requirements and that's why needs appropriate requirement engineering. Requirements that are not completely understood and defined are the primary cause for safety related problems. In order to accomplish the task of eliminating chances of mismanaged requirement engineering. We have sorted out approaches of improvised communication between Requirement Engineering (RE) and SCS in Safety Engineering. We have analyzed activities and techniques that should be performed by RE during safety analysis. Moreover, specified tools are explored that are used for analysis. We have focuses on systematic literature review for SCS where main focus is on mandatory safety concerns. Schemes are analyzed as per research questions to identify that which schemes consider these questions in three conditions including satisfactory, partial or not handled. We have selected a large number of articles from different publishers and then identified the articles with duplicate features. Finally we have selected a set of articles after eliminating duplicate ones and then evaluated these schemes as per research questions. We have concluded that requirement completeness, strong need for standard safety analysis tool and compliance with safety standards are mandatory to meet safety requirements during RE process.

The continuous global urbanization with rapid and dynamic transitioning in traffic situations among highly populated cities results in difficulty for data collection and communication. Data collection for millions of vehicles hinders by various problems, i.e., higher cost of energy, time, space, and storage resources. Moreover, higher data traffic results in higher delays, larger throughput, excessive bottlenecks, and frequent repetition of data. To better facilitate the aforementioned challenges and to provide a solution, we have proposed a lightweight Machine Learning based data collection protocol named ML-TDG to effectively deal with higher data volumes in a real-time traffic environment capable of bringing the least burden on the network while utilizing less space, time, and energy. ML-TDG is functional based on Apache Spark, an effective data processing engine that indexes the data based on two logs, i.e., old commuters or frequent/daily commuters and second new/occasional commuters. The proposed protocol’s main idea is to utilize real-time traffic, distinguish the indexes in parallel based on two assigned logs criteria to train the network, and collect data with the least sources. For energy and time optimization, dynamic segmentation switching is introduced which is an intelligent road segments division and switching for reducing bottlenecks and replication. ML-TDG is tested and verified on Dublin, Ireland’s busiest motorway M50. ML-TDG performs the data collection, data sorting, and network training to decide the next execution altogether for better optimization every time. The experimental results verify that our proposed protocol is attaining higher performance with lower resource requirements along with rich and time-efficient sustainable data collection clusters in comparison with baseline protocols.

The subcellular location of a protein provides valuable insights to bioinformaticians in terms of drug designs and discovery, genomics, and various other aspects of medical research. Experimental methods for protein subcellular localization determination are time-consuming and expensive, whereas computational methods, if accurate, would represent a much more efficient alternative. This article introduces an ab initio protein subcellular localization predictor based on an ensemble of Deep N-to-1 Convolutional Neural Networks. Our predictor is trained and tested on strict redundancy-reduced datasets and achieves 63% accuracy for the diverse number of classes. This predictor is a step towards bridging the gap between a protein sequence and the protein’s function. It can potentially provide information about protein–protein interaction to facilitate drug design and processes like vaccine production that are essential to disease prevention.