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Diseases like diabetes are chronic and require long-term management. Inadequate production of insulin results in high blood sugar levels. Such diseases lead to serious health issues such as heart ailments, blood vessel complaints, eye ailments, kidney function disorders, and nerve ailments. Hence, accurate assessment and management of risk factors are crucial for the onset of diabetes. Our proposed approach combines fuzzy logic & machine learning algorithms for diabetes risk prediction. Three machine learning models were trained to classify patients into two categories of diabetes (Type-I and Type-II) based on their clinical dataset collected from Katihar Medical College & Hospital and Suvadhan Lab. The polynomial regression algorithm achieved a score of 0.947, while the support vector regression algorithm with the rbf kernel achieved a score of 0.954, with a linear kernel achieved a score of 0.73. Our proposed approach performed well with respect to the conventional approaches with improved accuracy by identifying the patients at diabetes risk. In future work, we further analyze the relationship between other ignored factors which contribute to diabetes risk.

Numerous face recognition systems employ deep learning techniques to identify individuals in public areas such as shopping malls, airports, and other high-security zones. However, adversarial attacks are susceptible to deep learning-based systems. The adversarial attacks are intentionally generated by the attacker to mislead the systems. These attacks are imperceptible to the human eye. In this paper, we proposed a feature-based masking iterative method (F-MIM) to generate the adversarial images. In this method, we utilize the features of the face to misclassify the models. The proposed approach is based on a black-box attack technique where the attacker does not have the information related to target models. In this black box attack strategy, the face landmark points are modified using the binary masking technique. In the proposed method, we have used the momentum iterative method to increase the transferability of existing attacks. The proposed method is generated using the ArcFace face recognition model that is trained on the Labeled Face in the Wild (LFW) dataset and evaluated the performance of different face recognition models namely ArcFace, MobileFace, MobileNet, CosFace and SphereFace under the dodging and impersonate attack. The F-MIM attack is outperformed in comparison to the existing attacks based on Attack Success Rate evaluation metrics and further improves the transferability.

Due to obstruction in photosynthesis, the leaves of the plants get affected by the disease. Powdery mildew is the main disease in cucumber plants which generally occurs in the middle and late stages. Cucumber plant leaves are affected by various diseases, such as powdery mildew, downy mildew and Alternaria leaf spot, which ultimately affect the photosynthesis process; that’s why it is necessary to detect diseases at the right time to prevent the loss of plants. This paper aims to identify and classify diseases of cucumber leaves at the right time using a deep convolutional neural network (DCNN). In this work, the Deep-CNN model based on disease classification is used to enhance the performance of the ResNet50 model. The proposed model generates the most accurate results for cucumber disease detection using data enhancement based on a different data set. The data augmentation method plays an important role in enhancing the characteristics of cucumber leaves. Due to the requirements of the large number of parameters and the expensive computations required to modify standard CNNs, the pytorch library was used in this work which provides a wide range of deep learning algorithms. To assess the model accuracy large quantity of four types of healthy and diseased leaves and specific parameters such as batch size and epochs were compared with various machine learning algorithms such as support vector machine method, self-organizing map, convolutional neural network and proposed method in which the proposed DCNN model gave better results.

Net Asset Value (NAV) has long been a key performance metric for mutual fund investors. Due to the considerable fluctuation in the NAV value, it is risky for investors to make investment decisions. As a result, accurate and reliable NAV forecasts can help investors make better decisions and profit. In this research, we have analysed and compared the NAV prediction performance of our proposed deep learning models, such as N-BEATS and NBSL, with the FLANN model in both univariate and multivariate settings for five Indian mutual funds for forecast periods of 15, 20, 45, 63, 126, and 252 days using RMSE, MAPE, and R2 as evaluation metrics. A large forecast horizon was chosen to assess the model's consistency, reliability, and accuracy. The result reveals that the N-BEATS model outperforms the FLANN and NBSL models in the univariate setting for all datasets and all prediction horizons. In a multivariate setting, the outcome demonstrates that the N-BEATS model outperforms the FLANN model across all datasets and prediction horizons. The result also shows that, as the number of forecast days grew, our suggested models, notably N-BEATS, maintained consistency and attained the highest R2 value throughout the longest forecast duration.

purpose: The purpose of this research is to provide a model of transparency in the Islamic Council of the Islamic Republic of Iran (with the approach of digital governance in the context of information technology). Method: The type of research is based on the fundamental objective, which is a mixed approach (qualitative and quantitative) with an exploratory design. In this article, only the qualitative part is mentioned. in the qualitative part with a phenomenological approach, the seven-step theoretical method of Kleizeri and the Delphi technique have been used. The statistical population of this research in the qualitative part includes experts in the field of transparency, whose number is 24 people. The data collection tool of this research was conducted in the qualitative part through individual, in-depth, semi-structured interviews, and then the data was processed. Findings: The model of transparency in the Majlis of the Islamic Council of Iran consists of five main categories, which are: informational, financial, supervisory and accountable, ethical and professional, which not only achieved the dimensions of transparency in the Islamic Council, but also the factors affecting the establishment The success of transparency, the consequences of establishing transparency, the obstacles to establishing transparency, as well as possible damages in the Islamic Council in case transparency is not established will also be discussed.

Purpose: The Purpose of this paper is to explain the nature and process of digital transformation.Methodology: Its methodology is based on a structured literature review (SLR) and searching scientific sites and specialized resources. One roadmap model, six maturity assessment models, three digital business model transformation approaches and eight digital transformation frameworks are explained interpretively.Findings: Digital transformation is  a strategy and an operating model change, in which technological advancements are leveraged to improve human experiences and operating efficiencies and to evolve the products and services to which customers will remain loyal. A review of research in this field shows that the nature of digital transformation depends on knowledge and knowledge is formed as a valuable asset in people's minds. Therefore, the role of human resources in creating and achieving the transformation is important and fundamental. Findings show that no digital transformation is only technological, it is organizational and requires its own planning, resources and rules. It is clear that we need a model in this kind of transformation journey. Staff capital and its empowerment by knowledge and fundamental requirements are important for success in the digital transformation program. Transformation in culture, in structure, in processes and methods, along with the benefits of the frontier technologies, will lead to a dramatic transformation in the field of business. The environment of organizations is changing faster and has become more volatile, uncertain and complex than in the past. Rapid changes in competition, demand, technology and regulations make it more important than ever for organizations to be able to respond and adapt to their environment. In this context, the pressure on firms to align their business strategy with the technological changes in the environment has significantly increased with the emergence and growing importance of new digital technologies, such as Social Media, Cloud Computing, Big Data and Analytics, Embedded Devices, 3D-Printing, the Internet of Things and Artificial Intelligence. They are profoundly transforming the strategic context of organizations: changing the structure of competition, the behavior and expectations of customers, the way business is conducted, the way products are manufactured and services are delivered, the way of working and, ultimately, entire industries. The growth of technology, in addition to transforming organizational boundaries, has also improved knowledge sharing. As the results indicate, the digital age refers to a time in history when the use of digital technology was generalized throughout the world. We are on the verge of a technological revolution that will fundamentally change the way we live, work and communicate with each other. In terms of scale, scope, and complexity, this transformation will be different from what mankind has already experienced.Conclusion: Digital transformation has affected the work processes of individuals in organizational environments. New methods in combining digital innovations and intensifying inter-organizational collaboration are among the key features of success. Because the goal of digital transformation is a business transformation, it requires different organizational elements, which are explained in the frameworks, to work together like a piece of music in a concert with different instruments played harmoniously and simultaneously

Purpose: Dynamic features of the market, increased competition, and globalization are primary requirements of active participation in new markets. These days, only companies which benefit from digital agility and function fast, as well as flexibly, can grow and expand their market. The present study aimed to design and illustrate a model of agile digital marketing capabilities in the insurance industry in Iran.Methodology: The present study is aimed at presenting a model of agile digital marketing capabilities in insurance industry by using semi-structured interviews. By nature the study is exploratory and qualitative; concerning research objective it is an applied study; it is, also, a descriptive survey study in terms of data collection. The population includes all senior managers of insurance firms and decision-makers in digital marketing of insurance industry. Accordingly, the sample includes 42 participants with required subject-matter experience and skill and expertise. By means of qualitative research methods in marketing, and the real-world data-based grounded theory (GT) systematic paradigm, the study tried to present a theoretical model.Findings: Based on the interview data and following the grounded theory approach, 246 open codes and 41 themes were identified regarding the agile digital marketing capabilities in the insurance industry. In this research, the main categories are Flexibility, Prediction and accountability-prevention, Quality, Reconfiguration, Functional mutual cooperation, Convenience and reaction speed, Perception, Trust and safety, Integrity of skills and performance, the ability of technical integration, the ability of organizational integration. In  addition to the central dimension, five other components (Casual factors, Contextual factors, Intervening factors, Strategies, and Consequences) were identified.Discussion and Conclusion: The final result of the study is a relatively comprehensive and complete paradigm based on the requirements and characteristics of the insurance industry. The present study aims to expand theoretical foundations and the existing literature on digital agile marketing through an applied and appropriate model, whereas previous researches are focused on speed and accuracy, or on organizational structure, culture, manpower, information equipment, technology, and process, or on reaction speed, flexibility, and responsiveness.  Also, the importance of channels, social media, digital communication, digital technology in digital marketing, and social networks, as well as the special value of the brand , are common knowledge. Studies, also, allude to team-making and management support, and culture . Also, in some cases, adaptability to changeable conditions, cooperative approach, integrative workplace, innovation speed, concentration on prediction, and monitoring market needs were attested in previous literature