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Microorganisms or microbes comprise majority of the diversity on earth and are extremely important to human life. They are also integral to processes in the ecosystem. The process of their recognition is highly tedious, but very much essential in microbiology to carry out different experimentation. To overcome certain challenges, machine learning techniques assist microbiologists in automating the entire process. This paper presents a systematic review of research done using machine learning (ML) and deep leaning techniques in image recognition of different microorganisms. This review investigates certain research questions to analyze the studies concerning image pre-processing, feature extraction, classification techniques, evaluation measures, methodological limitations and technical development over a period of time. In addition to this, this paper also addresses the certain challenges faced by researchers in this field. Total of 100 research publications in the chronological order of their appearance have been considered for the time period 1995–2021. This review will be extremely beneficial to the researchers due to the detailed analysis of different methodologies and comprehensive overview of effectiveness of different ML techniques being applied in microorganism image recognition field.
Priya Rani; Shallu Kotwal; Jatinder Manhas; Vinod Sharma; Sparsh Sharma. Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments. Archives of Computational Methods in Engineering 2021, 1 -37.
AMA StylePriya Rani, Shallu Kotwal, Jatinder Manhas, Vinod Sharma, Sparsh Sharma. Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments. Archives of Computational Methods in Engineering. 2021; ():1-37.
Chicago/Turabian StylePriya Rani; Shallu Kotwal; Jatinder Manhas; Vinod Sharma; Sparsh Sharma. 2021. "Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments." Archives of Computational Methods in Engineering , no. : 1-37.
Ensuring soil strength, as well as preliminary construction cost and duration prediction, is a very crucial and preliminary aspect of any construction project. Similarly, building strong structures is very important in geotechnical engineering to ensure the bearing capability of structures against external forces. Hence, in this first-of-its-kind state-of-the-art review, the capability of various artificial intelligence (AI)-based models toward accurate prediction and estimation of preliminary construction cost, duration, and shear strength is explored. Initially, background regarding the revolutionary AI technology along with its different models suited for geotechnical and construction engineering is presented. Various existing works in the literature on the usage of AI-based models for the abovementioned applications of construction and maintenance are presented along with their advantages, limitations, and future work. Through analysis, various crucial input parameters with great impact on the estimation of preliminary construction cost, duration, and soil shear strength are enumerated and presented. Lastly, various challenges in using AI-based models for accurate predictions in these applications, as well as factors contributing to the cost-overrun issues, are presented. This study can, thus, greatly assist civil engineers in efficiently using the capabilities of AI for solving complex and risk-sensitive tasks, and it can also be used in Internet of things (IoT) environments for automated applications such as smart structural health-monitoring systems.
Sparsh Sharma; Suhaib Ahmed; Mohd Naseem; Waleed S. Alnumay; Saurabh Singh; Gi Hwan Cho. A Survey on Applications of Artificial Intelligence for Pre-Parametric Project Cost and Soil Shear-Strength Estimation in Construction and Geotechnical Engineering. Sensors 2021, 21, 463 .
AMA StyleSparsh Sharma, Suhaib Ahmed, Mohd Naseem, Waleed S. Alnumay, Saurabh Singh, Gi Hwan Cho. A Survey on Applications of Artificial Intelligence for Pre-Parametric Project Cost and Soil Shear-Strength Estimation in Construction and Geotechnical Engineering. Sensors. 2021; 21 (2):463.
Chicago/Turabian StyleSparsh Sharma; Suhaib Ahmed; Mohd Naseem; Waleed S. Alnumay; Saurabh Singh; Gi Hwan Cho. 2021. "A Survey on Applications of Artificial Intelligence for Pre-Parametric Project Cost and Soil Shear-Strength Estimation in Construction and Geotechnical Engineering." Sensors 21, no. 2: 463.
It is a well‐known research outcome that clustering helps in increasing the network lifetime and the routing performance. This research thus aims to optimize the energy consumption of wide scale wireless sensor networks (WSNs) by proposing a novel and an adaptive energy efficient fuzzy (AEEF) clustering for a WSN. It is an improvement and modification on the traditional clustering of the cells of the network for Landslide Detection systems. It incorporates the concept of fuzziness and state machine in selecting the cluster heads, unlike previously clustering algorithms such as low‐energy adaptive clustering hierarchy and so on. The proposed AEEF approach is validated by carrying out simulations and the results show that the average energy consumption per node under no‐clustering is 0.5144892 mJ, whereas it reduces drastically to 0.084482 mJ using the proposed AEEF clustering algorithm. Hence, the proposed algorithm is approximately 83.5% more energy efficient and thus increases the lifetimes of the nodes deployed for sensing a landslide along with being adaptive to any changes in the ambient conditions.
Suhaib Ahmed; Swastik Gupta; Ashish Suri; Sparsh Sharma. Adaptive energy efficient fuzzy: An adaptive and energy efficient fuzzy clustering algorithm for wireless sensor network‐based landslide detection system. IET Networks 2020, 10, 1 -12.
AMA StyleSuhaib Ahmed, Swastik Gupta, Ashish Suri, Sparsh Sharma. Adaptive energy efficient fuzzy: An adaptive and energy efficient fuzzy clustering algorithm for wireless sensor network‐based landslide detection system. IET Networks. 2020; 10 (1):1-12.
Chicago/Turabian StyleSuhaib Ahmed; Swastik Gupta; Ashish Suri; Sparsh Sharma. 2020. "Adaptive energy efficient fuzzy: An adaptive and energy efficient fuzzy clustering algorithm for wireless sensor network‐based landslide detection system." IET Networks 10, no. 1: 1-12.
Vehicles which are the nodes in VANETs are equipped with powerful resources to enable the safe and quick dissemination of information for reliable and trusty intelligent transportation system. These resources of vehicles can be utilized for providing support to various other new VANETs applications for enhancing the driving experience, reducing the road congestions, accidents and to promote pollution-free ITS. In order to support and promote the abovementioned tasks, a concept of vehicular cloud is getting a lot of attention nowadays. Introduction of VANETs cloud has led to the expansion in the perimeter of the capabilities of VANETs and ITS by enabling newer types of collaborations between the vehicles and also given rise to a plethora of emerging applications and services. In this study, an in-depth review covering all the possible aspects associated with VANETs cloud has been explored. To begin with, concepts like architectures, communication types supported in VANETs cloud has been analyzed. A motivation explaining the need of VANETs cloud in today’s scenario has been briefly overviewed. Then, a detailed comparison illustrating the similarities and differences between the VANETs cloud and its parent’s network i.e. VANETs is presented. In this article, our main focus is on finding the various applications that will be supported by VANETs cloud. Various challenges in terms of security, deployment and in the use of VANETs cloud has been explored. Finally, applicability of VANETs cloud with various emerging technologies like IoT, software defined networks, and cognitive radio in terms of their applications, challenges, issues, and benefits has been presented in this article. This study on the collaboration of VANETs and cloud is believed to assist the researchers working on this field to find out the current status of research in VANETs clouds along with the challenges that one can face during its deployment.
Sparsh Sharma; Ajay Kaul. VANETs Cloud: Architecture, Applications, Challenges, and Issues. Archives of Computational Methods in Engineering 2020, 28, 2081 -2102.
AMA StyleSparsh Sharma, Ajay Kaul. VANETs Cloud: Architecture, Applications, Challenges, and Issues. Archives of Computational Methods in Engineering. 2020; 28 (4):2081-2102.
Chicago/Turabian StyleSparsh Sharma; Ajay Kaul. 2020. "VANETs Cloud: Architecture, Applications, Challenges, and Issues." Archives of Computational Methods in Engineering 28, no. 4: 2081-2102.
Vehicular ad-hoc Network (VANET) is an emerging type of Mobile ad-hoc Networks (MANETs) with excellent applications in the intelligent traffic system. Applications in VANETs are life critical since human lives are at stake and therefore, interaction among nodes (vehicles) must be established in the most secure manner. To provide security for VANETs, various security measures are designed, the most popular of which is Intrusion Detection Systems (IDSs). IDS has already proved its worth in detection of malicious nodes in traditional networks but applying the IDS in VANET like networks is somehow different and difficult due to its peculiar characteristics such as resource-constrained nodes, high mobility of nodes, specific protocols stacks, and standards. This paper presents a brief introduction about the various IDSs, in general, to get the readers well acquainted with the concept of IDS after which an in-depth survey of various IDSs that are propounded for VANETs is put forward followed by analyzing and comparing each technique along with merits and demerits. Some basic instructions have also been presented for developing IDSs that have a potential application in VANET and VANET Cloud. Our aim is to identify leading trends, open challenges, and future research directions in the deployment of IDS in VANET. In order to bridge the research gaps in terms of performance, detection rate and overhead, and also to overcome the challenges of existing IDS in literature, a proactive bait based Honeypot optimized IDS system is also proposed with the aim to detect existing and zero-day attacks with minimal overhead. Finally, some open research works being carried out in the field is also proposed.
Sparsh Sharma; Ajay Kaul. A survey on Intrusion Detection Systems and Honeypot based proactive security mechanisms in VANETs and VANET Cloud. Vehicular Communications 2018, 12, 138 -164.
AMA StyleSparsh Sharma, Ajay Kaul. A survey on Intrusion Detection Systems and Honeypot based proactive security mechanisms in VANETs and VANET Cloud. Vehicular Communications. 2018; 12 ():138-164.
Chicago/Turabian StyleSparsh Sharma; Ajay Kaul. 2018. "A survey on Intrusion Detection Systems and Honeypot based proactive security mechanisms in VANETs and VANET Cloud." Vehicular Communications 12, no. : 138-164.