•An innovative bio-inspired optimization based filtering system is considered.•Dragonfly and Modified Firefly algorithm is used to select the optimal parameters.•The Convolutional based Neural ...Network (CNN) is used to classify the denoised image as normal or abnormal.
Image denoising has been foremost concern in the field of medical imaging (MI). For image denoising, the most challenging is to protect the data bearing structures such as edges and surfaces to get good visual quality while enhancing Peak Signal to Noise Ratio (PSNR). In this work, an inventive bio-inspired optimization based filtering system is considered for the MI denoising process, the filter named as Bilateral Filter (BF). The execution of the denoising process influences the decision of selecting the optimal parameters, i.e., Gaussian and spatial weights. Here, these parameters are chosen by utilizing swarm based optimization that is Dragonfly (DF) and Modified Firefly (MFF) algorithm. For this parameter selection, multi-objective fitness function (PSNR and vector root mean square error (VRMSE)) is utilized. Moreover, Convolutional based Neural Network (CNN) classifier is used to classify the denoised image as normal or abnormal, with better classification rate. From the experimental results the proposed model, PSNR of 47.52 dB and error rate of 1.23 is attained contrasting to the existing filters and some classifiers.
In recent years, the need for high security with reliability in the wireless network has tremendously been increased. To provide high security in reliable networks, mobile ad hoc networks (MANETs) ...play a top role, like open network boundary, distributed network, and fast and quick implementation. By expanding the technology, the MANET faces a number of security challenges due to self-configuration and maintenance capabilities. Besides, traditional security solutions for wired networks are ineffective and inefficient because of the nature of highly dynamic and resource-constrained MANETs. In this paper, the researchers focus on improving reliable data transmission with high security in the MANET using an optimization technique. In the proposed MANET system, the nodes are clustered by utilizing an energy-efficient routing protocol. Then, the modified discrete particle swarm optimization is used to select the optimal cluster head. A secured routing protocol and a signcryption model can be used to improve the transmission security of the reliable MANET. The signcryption algorithm encrypts the digital signature, which can enhance the overall efficiency and confidentiality. The security-based analysis is performed on the basis of packet delivery ratio, energy consumption, network lifetime, and throughput. Finally, the result demonstrates that the MANET with optimization techniques achieves a high transmission rate and improves the reliable data security.
Various sustainable development goals (SDG's) that have been achieved with current economic business models initiated the new socioeconomic system known as the “sharing economy.” This collaborative ...economy system shares resources among communities and further assists to reduce both negative environmental and societal impacts and to reduce costs. The importance of this endeavor urges researchers to investigate the sharing economy more fully through different applications, primarily including transportation and customer service. However, these studies fail to attempt to explore the perspectives of the sharing economy through industrial applications. Industrial sectors are more prone towards negative sustainability; hence, it is necessary to address strategies to promote and to implement the sharing economy with an industrial perspective. Hence, this study seeks to identify the barriers of sharing economy implementation in industries, particularly with small and medium scale enterprises (SME) located in India. Common barriers are identified through the combined assistance of literature review and experts' opinions. These common barriers are shortlisted through Best Worst Method (BWM) and further analyzed through proposed solution methodology, DEMATEL, with the support of case industrial managers. Among 15 shortlisted barriers of sharing economy, “Lack of trust” was identified as the most influential and “Capital cost” was identified as the least influential barriers. This study contributes to both academic and practitioner levels by identifying the most common barriers of the industrial sharing economy. Further, we present strategies so practitioners can evade such barriers through customized practices instead of providing a generic treatment. In addition, this study provides a new approach to the literature; insight into the ‘industrial sharing economy’ could be a scientific breakthrough for upcoming studies. Finally, the study concludes with various useful recommendations and suggestions on the implementation of sharing economy in industries, particularly with SMEs. These recommendations shed light into various key research areas, which can be examined more fully in future studies.
Urbanization and globalization has led to a rapid development in the construction industry. Many strategies have been proposed to improve cost effectiveness in this sector. Over the last decade, cost ...concerns have been balanced with a growing debate on the necessity for sustainable construction practices. Because of depleting resources and environmental concerns, researchers and practitioners have begun to explore sustainable construction strategies. Among these strategies is the selection of sustainable materials which play a vital role in a building׳s environmental footprint. In the UAE in particular, there is an ongoing demand to select the best sustainable construction materials because the industry is growing so rapidly in this nation. Thus, the main intent of this paper is to propose a model to evaluate the best sustainable construction material based on sustainable indicators through a hybrid multi criteria decision making (MCDM) methodology with a specific examination of the UAE. The indicators collected from existing literatures were used in evaluation of sustainable construction materials with the assistance of construction sector-based respondents. The proposed framework was validated with a case study company, and the results were compared to the existing literatures and to feedback from the respondents. Finally, the study concludes with an assessment of its own limitations along with the identification of some useful managerial implications; it sheds some light on future possible enhancements in the theme of sustainable construction in the context of UAE.
Increasing numbers of end-of-life vehicles and a burgeoning concern for conservation of resources pressures the implementation of auto parts remanufacturing globally. But unlike in developed nations, ...developing countries encounter struggle with the implementation of auto parts remanufacturing due to hidden facts. Many researchers have explored such hidden facts in auto parts remanufacturing in developing nations and their emerging economies. However, there is a huge gap in the state-of-the-art remanufacturing practices in India, despite India's strong advancements in the automobile industry. In fact, India's automobile industry is rapidly approaching that of China's in recent years. Although some studies are available regarding remanufacturing implementation, current research has generally failed to offer a deep analysis of the factors and barriers that exist as hurdles to such implementation. Consequently, this paper attempts to investigate the essential barrier to auto parts remanufacturing in an Indian scenario, and we also address vital interrelationships and interdependencies. To achieve the aim of the study, a model framework is proposed and applied to an Indian case industry. The data obtained from the case industry is processed with two solution methodologies: namely, interpretive structural modeling (ISM) and analytic network process (ANP). To avoid inaccuracies and vagueness in results, the data are manipulated under a fuzzy environment. This study reveals results which show that higher cost and lack of customer acceptance are the essential and most influential barriers in auto parts remanufacturing in an Indian scenario. This study contributes to society by assisting closed loop supply chain players to identify and eliminate the barriers to remanufacturing within the scope of the Indian sector. Finally, this paper provides a brief road map and insights into future research for remanufacturing specifically in an Indian context.
Green issues have gained more importance in contemporary globalization. Recent years have seen manufacturing processes understand the green issues due to the social and environmental concerns ...involved. The drivers of green manufacturing, however, have not been thoroughly investigated. Thus, this paper investigates the responsibility of identifying twelve common drivers of green manufacturing from the combined assistance of existing literature, industrial managers, and expert opinion in the relevant field. A questionnaire on these common drivers was circulated among 120 leading firms in south India, and aided by their replies; a pair-wise comparison was made among the drivers. The pair-wise comparison is used as an input data and the drivers were analyzed on its basis. The analysis resorted to the use of a fuzzy Multi Criteria Decision Making (MCDM) approach. The obtained results are validated by a two-stage sensitivity analysis, namely: (1) using different de-fuzzification methods that are further evaluated through the Spearman coefficient and (2) assigning varying weight to the essential top priority drivers of green manufacturing among all common drivers. This study helps firms to stimulate an essential driver for quick and better adoption of green manufacturing. Finally, the paper concludes with some insight into the future path of green manufacturing in developing countries and an acknowledgment of the study's own limitations.
•Common drivers of green manufacturing are identified.•A two stage framework is proposed with fuzzy approach to rank the drivers.•Results are validated through different defuzzification methods and sensitivity analysis was done.
Lung cancer is one of the dangerous diseases that cause huge cancer death worldwide. Early detection of lung cancer is the only possible way to improve a patient’s chance for survival. A Computed ...Tomography (CT) scan used to find the position of tumor and identify the level of cancer in the body. The current study presents an innovative automated diagnosis classification method for Computed Tomography (CT) images of lungs. In this paper, the CT scan of lung images was analyzed with the assistance of Optimal Deep Neural Network (ODNN) and Linear Discriminate Analysis (LDA). The deep features extracted from a CT lung images and then dimensionality of feature is reduced using LDR to classify lung nodules as either malignant or benign. The ODNN is applied to CT images and then, optimized using Modified Gravitational Search Algorithm (MGSA) for identify the lung cancer classification The comparative results show that the proposed classifier gives the sensitivity of 96.2%, specificity of 94.2% and accuracy of 94.56%.
•An innovative approach is proposed for the automated diagnosis of lung cancer in CT images.•Modified Gravitational Search Algorithm is applied to train the Optimal Deep Neural Network.•The proposed classifier provides the sensitivity of 95.26%, specificity of 96.2% and accuracy of 96.2%.
Recently, the advancements in the field of wireless technologies and micro-electro-mechanical systems lead to the development of potential applications in wireless sensor networks (WSNs). The visual ...sensors in WSN create a significant impact on computer vision based applications such as pattern recognition and image restoration. generate a massive quantity of multimedia data. Since transmission of images consumes more computational resources, various image compression techniques have been proposed. But, most of the existing image compression techniques are not applicable for sensor nodes due to its limitations on energy, bandwidth, memory, and processing capabilities. In this article, we introduce a highly reliable and low-complexity image compression scheme using neighborhood correlation sequence (NCS) algorithm. The NCS algorithm performs the bit reduction operation and then encoded by a codec (such as PPM, Deflate, and Lempel Ziv Markov chain algorithm.) to further compress the image. The proposed NCS algorithm increases the compression performance and decreases the energy utilization of the sensor nodes with high fidelity. Moreover, it achieved a minimum end to end delay of 1074.46 ms at the average bit rate of 4.40 bpp and peak signal to noise ratio of 48.06 on the applied test images. On comparing with state-of-art methods, the proposed method maintains a better tradeoff between compression efficiency and reconstructed image quality.
At present times, healthcare systems are updated with advanced capabilities like machine learning (ML), data mining and artificial intelligence to offer human with more intelligent and expert ...healthcare services. This paper introduces an intelligent prediction and classification system for healthcare, namely Density based Feature Selection (DFS) with Ant Colony based Optimization (D-ACO) algorithm for chronic kidney disease (CKD). The proposed intelligent system eliminates irrelevant or redundant features by DFS in prior to the ACO based classifier construction. The proposed D-ACO framework three phases namely preprocessing, Feature Selection (FS) and classification. Furthermore, the D-ACO algorithm is tested using benchmark CKD dataset and the performance are investigated based on different evaluation factors. Comparing the D-ACO algorithm with existing methods, the presented intelligent system outperformed the other methodologies with a significant improvisation in classification accuracy using fewer features.
In wireless communication, Mobile Ad Hoc Network (MANET) consists of a number of mobile nodes which are communicated with each other without any base station. One of the security attacks in MANETs is ...Packet forwarding misbehaviour attack; this makes MANETs weak by showing message loss behavior. For securing message transmission in MANET, the work proposes Energy Efficient Clustering Protocol (EECP) with Radial Basis Function (RBF) based CH is elected for formed Clusters. Moreover, here some Network measures are considered to detect the malicious nodes and CH model that is speed, mobility, trust and so on. The trust value of the node is computed from the neighbor node which helps in further location to find a malicious node in the network to avail message drop and energy consumption (EC). After detecting malicious nodes, Multi secure Protocols that is Secure Efficient Distance Vector Routing (SEDV) and Secure Link State Routing Protocol (SLSP) with encryption technique used for message security. If the" HELLO" message sending by the sender, its encrypted and decrypted triples in receiver end to get the plain message, this technique is Triple Data Encryption Standard (TDES). Finally, the implementation results are evaluated to analyze the message security level of the proposed system in MANET in terms, of Packet to Delivery Ratio (PDR, Network Life Time (NLT) and some other important Measures.
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