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Co-Saliency object detection is the process of identifying common and repetitive objects from the group of images. Earlier studies have looked over several state-of-art deep neural network methodologies for co-saliency detection approach. The Deep CNN approaches rely heavily on co-saliency detection due to their potent feature extraction capabilities both deep and wide. This article assess the performance of several state-of-art deep learning model (VGG19, Inceptionv3, modifiedResNet, MobileNetV2 and PoolNet) for the purpose of co-saliency detection among images from benchmark datasets. All the models were trained on   70% part of the dataset and remaining were used for testing purpose. Experimental results show that modified ResNetmodel outperforms getting 96.53% accuracy as compared to other state-of-the-art deep neural network models.

Finding a brain tumor yourself by a human in this day and age by looking through a large quantity of magnetic-resonance-imaging (MRI) images is a procedure that is both exceedingly time consuming and prone to error. It may prevent the patient from receiving the appropriate medical therapy. Again, due to the large number of image datasets involved, completing this work may take a significant amount of time. Because of the striking visual similarity that exists between normal tissue and the cells that comprise brain tumors, the process of segmenting tumour regions can be a challenging endeavor. Therefore, it is absolutely necessary to have a system of automatic tumor detection that is extremely accurate. In this paper, we implement a system for automatically detecting and segmenting brain tumors in 2D MRI scans using a convolutional-neural-network (CNN), classical classifiers, and deep-learning (DL). In order to adequately train the algorithm, we have gathered a broad range of MRI pictures featuring a variety of tumour sizes, locations, forms, and image intensities. This research has been double-checked using the support-vector-machine (SVM) classifier and several different activation approaches (softmax, RMSProp, sigmoid). Since "Python" is a quick and efficient programming language, we use "TensorFlow" and "Keras" to develop our proposed solution. In the course of our work, CNN was able to achieve an accuracy of 99.83%, which is superior to the result that has been attained up until this point. Our CNN-based model will assist medical professionals in accurately detecting brain tumors in MRI scans, which will result in a significant rise in the rate at which patients are treated.

The Internet of things has become an interesting area of research in the last few years due to its ability to make human life simple and easier. Quality of Services (QoS) has gained a lot of importance due to the increasing popularity of the technology. QoS metrics help the IoT users to understand and express their requirements for the selection of services provided by IoT. Researchers in this field have come up with different types of architectures to provide a better view and define all the functions of the technology. In this paper, we have defined a few of the architectures and QoS metrics related to these architectures.

The rapid growth and advancement of electronic devices and technologies in the FinTech industry empower new innovative products and services. The covid-19 pandemic could have a devastating effect on Malaysia’s economy, but it has offered additional opportunities for the E-wallet segment of the Fintech business to thrive. The E-wallet segment of FinTech is one of the latest innovations that is currently growing as there is a need for contactless payments during the pandemic situation. The main objective of the study is to examine the factors affecting e-wallet adoption among young adults in Malaysia. A sample of 200 responses was analyzed using Smart PLS 3.0. The findings revealed that the factors of “performance expectancy”, “effort expectancy”, “compatibility”, and “social influence” have a positive and significant impact during the pandemic; however, the factor of “facilitating conditions” has no significant impact on the adoption of the E-wallets. The study substantiates the key and important variables of adoption in order to develop and evolve E-wallet providers' existing services. Particularly, due to the increasing importance of e-commerce, E-wallet service providers are urged to focus on the system's interoperability, which encourages individuals or customers to use the strategy.  They should include unique features that allow customers to accept the service, trust its benefits and feel comfortable using the technology. The study is useful to the E-wallet providers to improve the existing services. The findings also guide the companies offering E-wallets to enhance the usage and adoption of their services.

In the Industry 4.0 era, many pioneering industries are leveraging emerging technologies such as the Internet of Things (IoT) as solutions in the digital age. One of the largest and most active industries in Iran is the food industry, which stands to benefit significantly from these advancements. Achieving a sustainable competitive advantage is often possible at the level of the supply chain, where companies use information and communication technologies, such as IoT, to coordinate information, finances, and materials among supply chain actors. This research aimed to identify the key success factors (KSFs) for implementing IoT in the food supply chain. Firstly, through a systematic literature review, the KSFs for IoT implementation in the food supply chain were identified. To develop a measurement model, confirmatory factor analysis using structural equation modeling was employed, making the research applied-descriptive. A questionnaire was designed and completed by 142 members of the "Amadeh Laziz" supply chain (a case study), who were selected using a stratified random sampling method. Confirmatory factor analysis and LISREL 8.83 were then used to validate the proposed model. Finally, the cause-and-effect relationship between KSFs in IoT implementation in the food supply chain was analyzed using Grey DEMATEL. Based on the confirmatory factor analysis findings, the KSFs in implementing IoT in the food supply chain were identified as technical, economic, legal, cultural and social, security, applicability of IoT throughout the supply chain, and implementation of IoT applications. Thus, the measurement model included eight factors and 27 measures. According to the cause-and-effect relationship findings, "Implementation of IoT applications" and "Economic" factors were found to be mostly influenced, while "Applicability of IoT throughout the supply chain" and "Technical" factors were recognized as the most influential. The results of this research can guide food producers and technology policymakers in their supply chains and help avoid trial and error in IoT implementation by leveraging global and national experiences.

In recent privacy has emerged as one of the major concerns of deep learning, since it requires huge amount of personal data. Medical Image Analysis is one of the prominent areas where sensitive data are shared to a third party service provider. In this paper, a secure deep learning scheme called Metamorphosed Learning (MpLe) is proposed to protect the privacy of images in medical image analysis. An augmented convolutional layer and image morphing are two main components of MpLe scheme. Data providers morph the images without privacy information using image morphing component. The human unrecognizable image is then delivered to the service providers who then apply deep learning algorithms on morphed data using augmented convolution layer without any performance penalty. MpLe provides sturdy security and privacy with optimal computational overhead. The proposed scheme is experimented using VGG-16 network on CIFAR dataset. The performance of MpLe is compared with similar works such as GAZELLE and MiniONN and found that the MpLe attracts very less computational and data transmission overhead. MpLe is also analyzed for various adversarial attack and realized that the success rate is as low as . The efficiency of the proposed scheme is proved through experimental and performance analysis.

Purpose: This research aimed at presenting the consumers’ compulsive buying pattern through internet advertisements of digital content in Iran's TV shopping industry.Method: Research Methodology was practical in terms of purpose and conducted using mixed method (qualitative-quantitative). The research community was based on the purposeful sampling method, and consisted of ten marketing experts. The research tool was interview. MAXQDA software was used to analyze data through database theory. The statistical population in the quantitative section included TV buyers in Mashhad. Based on Morgan table and random sampling, 384 samples were selected. The research tool was a researcher-made questionnaire, and the Structural Equation Method (SEM) in SmartPLS software was used for data analysis. The validity of the questionnaire was confirmed by using face, content, divergent and convergent validities, and its reliability was also confirmed using Cronbach's alpha. Both of Composite and homogeneous reliability were evaluated.Findings: "appropriate digital marketing mix design for TV sales, digital marketing capabilities, individual demographic characteristics, lifestyle, family " constitute the causal conditions in the consumer’s compulsive buying pattern in the TV shopping. According to the findings, “quick and transient purchase and irrational and emotional purchase” were identified as a central phenomenon. “TV's attractiveness from the audience's point of view, broadcasting policies, sales companies' policies, national TV belief and trust, individual awareness and knowledge about buying products and society's culture” acted as intervening conditions. In the field of buying, “intellectual structures of society and executive structures of society” identified as background conditions. Human strategies and structural and organizational strategies” acted as strategies and “Consumers outcomes; families and society outcomes” were identified as outcomes. According to the results of structural modeling, the relationships of the identified pattern were significant.Conclusion: The issue of compulsive buying is one of the most important and common issues, and buying from TV has fueled this issue, and has become the basis for its expansion and, following that, its negative consequences. In this scientific research, efforts were made to reduce the consequences of this phenomenon. The results of this study showed that although the phenomenon of compulsive purchase from TV is negative, but with proper management, useful results can be obtained from it.

Agriculture is one of the essential sources of occupation and revenue in India. Conferring to existing statistics, most agriculturalists are facing severe losses due to poor farming yield. Farming activities are challenged by various environmental factors that affect agricultural productivity to a greater extent. The present farming situation is above the average of the process involves more biochemical bases for managing the diseases and other destructing facts. The foremost problems they are facing in day-to-day farming tasks are crop or plant diseases affecting productivity. Also, the growth of weeds along with field crops has been another challenge.  The technology has developed to rectify the problems using some machine learning algorithms like Random Forest algorithms, Decision trees, Naïve Bayes, KNN, K-Means clustering, Support vector machines. The result has been evaluated and observed through the performance evaluation metrics using confusion matrix, accuracy, precision, Sensitivity, specificity with the observations, research, and studies. The statistics have expressed the overall accuracy of 98% by achieving the detection of diseases in plants and by removing the weeds that ruin the growth of plants.

The performance of SMEs will keep the wheel of the country’s economy turning. Upgrading the technologies currently used in SMEs will make the manufacturing process of food and beverage (F&B) industries more efficient. These firms are still using traditional equipment and machinery in their F&B manufacturing process; however, it is required to adopt the next level of technologies for maximum productivity, which they cannot afford. Thus, the study focuses on the intention to adopt upgraded technologies in F&B SMEs in developing countries. A total of 230 F&B SME owners and top management were surveyed in Bangladesh. The collected data were analyzed using PLS-SEM with SmartPLS software. The results showed that disaster preparedness and business continuity had significant effects on the intention to adopt; similarly, performance expectancy, effort expectancy, and social influence were also significant in the intention to adopt the next level of IR technologies. The moderating role of government support and policy had mixed effects on intention. The study is significant because disaster preparedness and business continuity plans are utilized in technology adoption and intention scenarios. To investigate this intention, the study adopted two perspectives: the influence and role of disasters and hazards on SMEs through disaster preparedness and business continuity plans, and secondly, the Unified Theory of Acceptance and Use of Technology (UTAUT) for measuring the intention. The study contributed to these two theories in a new context with constructs. The study will also contribute to policymakers in developing constructive policies and providing effective financial and non-financial support for F&B manufacturing SMEs.