Purpose: The digital transformation of family-owned small businesses (F-OSBs) has become a critical area of research to maintain their economic contribution in today’s rapidly evolving digital landscape. This study examines the effect of internet entrepreneurial self-efficacy on the digital transformation of F-OSBs by mediating strategic agility and moderating artificial intelligence usage. Design/methodology/approach: This study employed a cross-sectional survey design to collect primary data from 378 descendent entrepreneurs of F-OSBs in Pakistan’s five major cities. Findings: The study revealed that leadership ability, internet marketing, technology utilization, and artificial intelligence used by the F-OSBs can contribute to their digital transformation, but e-commerce ability does not. The strategic agility of the descendant entrepreneur enhances the abilities of e-commerce, leadership, and technology utilization, leading to the digital transformation of F-OSB. However, strategic agility reduces the role of Internet marketing in digital transformation. Artificial intelligence usage moderates leadership’s ability to improve strategic agility but increases technology utilization for strategic agility and digital transformation of F-OSB. Practical implications: The digital transformation through a combination of strategic agility and artificial intelligence can increase the F-OSBs' proactive approach to respond to changing market conditions even during economic recessions like COVID-19. Originality/value: This study broadens the existing literature by examining the effect of descendent entrepreneur’s internet entrepreneurial self-efficacy, strategic agility, artificial intelligence usage, and their interplay on the digital transformation of F-OSB through the unified theory of acceptance and the use of technology.

Compared to centralized and decentralized models, distributed models have the potential to dramatically expand the scalability of existing IoT and Industry 4.0 solutions while maintaining participant organizations’ security and privacy. This is partly because participating firms are not required to rely on or trust other services or third parties to manage the data they gather and transfer, out of concern that these parties could misuse the data or, in the worst-case scenario, share it with mass surveillance programs. However, until blockchain technology (BCT) demonstrates its viability as a means of developing security solutions in decentralized, collaborative, and trustless environments, the vast majority of these use cases will struggle to meet the requirements for integrity, immutability, traceability, and notarization. By utilizing BCT, it is possible to eliminate intermediaries, enabling individuals and devices to manage their data independently of third parties and most significantly to achieve a high level of traceability with information flow harmony. This technology enables transaction, transparency, and traceability by enabling for the interchange of historical data. The fundamentals of blockchain are examined in this research paper, along with an investigation of its operation and a discussion of some of its most fundamental aspects and concepts. A concise overview of smart contracts enables us to completely reimagine how network members create and automate transactions. Finally, several IoT and Industry 4.0 application possibilities that leverage blockchain are investigated, as is the blockchain’s future trajectory.

The minimal-degree distributed denial-of-service attack takes advantage of flaws in the adaptive mechanisms of network protocols, which could have a big impact on network service quality. It is very hard to find, has a low attack rate, and comes at a set time. Detection methods that have been used before have problems because they only use one type of detection and are not very good at identifying the object. In the end, a way to detect many sorts of minimal DDoS assaults that use deep hybrid learning is suggested. To construct multi-type limited DDoS threat data sets and mimic diverse sorts DDoS assaults and legitimate traffic in varying situations in the 5G setting, collect congestion at the networking entry and extract flow feature info are considered. From a statistical threshold and feature engineering point of view, these data sets show how many sorts of minimal DDoS assaults are there. This study aims to develop a deep hybrid learning-based multi-type low-rate DDoS attack detection solution for 5G networks which is the novel model that is recently deployed, and a hybrid deep learning algorithm was used to train the algorithm offline, and the algorithm’s performance was compared to that of the LSTM-Light GBM and LSTM-RF algorithms. The CNN-RF revealing model was then used to detect minimal DDoS assaults at the gateway, so that multiple attacks could be detected at the same time. It can identify 4 sorts of low-rate DDoS assaults like Slow-Headers, Slow-Body, Slow-Read, and Shrew assaults, in a 120-second window. The false intercept rate is 11.03 percent. This means that 96.22 percent of traffic could be found. Using the strategy suggested can help cut down on the traffic concentration of minimal DDoS attacks at the net ingress. It can also be used in real-world situations.

When it comes to agricultural sciences, one of the most difficult challenges to solve is the detection of diseases. Agricultural specialists study a variety of sources to detect plant issues on a regular basis. Rarely can misinterpretations of diseased plants cause improper pesticide selection and subsequent agricultural disaster, although this does happen from time to time. In order to diagnose illnesses at an early stage, it is necessary to deploy automated disease detection systems. This is critical for farmers since it is both time-consuming and expensive. A sick leaf must be carefully segmented in order to be properly separate it from the rest of the leaves. Despite digital noise, a different background, a different shape, and a different brightness, it is tough to distinguish a sick photo. In order to increase the quality of apple leaf images for disease detection and classification, a new approach known as brightness preserving dynamic fuzzy histogram equalisation (BPDFHE) has been created. To determine the sweetness of an apple, examine the leaf and the texture of the fruit. In the next section, the performance of the proposed enhancement algorithm is compared to the performance of existing enhancement approaches. Existing segmentation algorithms are outperformed by our approach for segmenting the area of interest from ill leaves against a live background. It is during this phase that we analyse the Jaccard index, the Dice coefficient, and correctness. Comparing the proposed segmentation algorithm to current approaches, it proves to be a highly effective strategy that can more efficiently identify apple ill leaves from a live background with a 99.8 percent accuracy rate.

Deep learning (DL) based camouflage target localization, classification and detection using UAV data is currently an evolving and promising hotspot in the field of digital image processing and computer vision. Weed, an undesirable plant that hinders crop growth, acts as a camouflage target w.r.t sugarcane crop (Saccharum Officinarum) . Weed exhibits green-on-green color-based merging capability (background matching) and similar spectral behavior with the sugarcane crop, resulting in a visually complex camouflage habitat, making it difficult to distinguish and detect weed from crop. The concealed nature of weed in the sugarcane field, qualifies it as a camouflage target. In this scenario, weed is distributed in the form of multiple small patches across the UAV imagery, making the detection even more complicated, leading to a requirement of a pixel-based classification and subsequent target detection technique. The research problem is to successfully detect weed (camouflage target) at the smallest resolvable pixel size (2-4 cm/px), keeping in mind the similar behavior (merging color) and its difficulty to differentiate it from the sugarcane crop (background). To achieve this challenge, color and texture are exploited as important feature representations. They are extracted via UAV image(s), which can aid in small and multiple weed patch detection, by implementing a rich feature-based Deep Neural Network (DNN). In this revolutionary era of AI and modern drones, DNNs provide feature based elastic transformations, expedited processing, improved accuracy, and optimized mapping and detection. These representation-based networks traverse deeper into color and texture feature representations as a unified component, pixel by pixel, eventually detecting the camouflage targets by their self-learning capabilities in comparison to traditional classification approaches. DNNs exhibit good capabilities to solve camouflage target detection problems, therefore we have proposed a methodology based on deep learning feature-based modelling which generates classified and localized image maps that highlight multiple and small weed patches at the minimal pixel level possible from a merged green-on green background. We have explored and proposed a methodology that self learns exhaustive features from the UAV imagery via feature transformations which in turn reduces the feature bias and exhibits enhanced prospects w.r.t sensitivity of imagery background. This eventually results in improved localization and classification of camouflage targets with respect to traditional target detection and localization modelling techniques in natural background. It is observed that the proposed methodology is capable of detecting small patches of weed (similar color behavior) from its background with an accuracy of 90.5%.