International Journal of Communication Networks and Information Security (IJCNIS)
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DISEASE DETECTION IN CROPS USING DEEP LEARNING
No full textThe horticultural area assumes a key part in providing quality food and makes the best commitment to developing economies and populaces. Plant illness might cause huge misfortunes in food creation and destroy variety in species. Early determination of plant illnesses utilizing exact or programmed recognition procedures can upgrade the nature of food creation and limit financial misfortunes. As of late, profound learning has acquired enormous enhancements the acknowledgment precision of picture arrangement and article location frameworks. Subsequently, in this paper, we used convolutional brain organization (CNN)- based pre-prepared models for proficient plant illness ID. We zeroed in on adjusting the hyper boundaries of famous pre-prepared models, for example, DenseNet-121, ResNet-50, VGG-16, and Commencement V4. The analyses were completed utilizing the famous Plant Town dataset, which has 54,305 picture tests of various plant illness species in 38 classes. The exhibition of the model was assessed through characterization exactness, awareness, explicitness, and F1 score. A relative investigation was likewise performed with comparable best in class review. The investigations demonstrated that DenseNet-121 accomplished 99.81% higher arrangement precision, which was better than cutting edge models
A Hybrid Deep Learning Approach for ECG Arrhythmia Detection: GPT, GANs, and Triplet Loss Integration
No full textThis paper proposes a novel deep-learning method to detect arrhythmias from the ECG data by adopting pre-trained GPT models and other powerful state-of-the-art DL algorithms. Most traditional ECG classification models face challenges in capturing complex temporal dependencies and handling class imbalances. To meet these challenges, our system leverages GPT to capture complex temporal patterns and contextual relationships within ECG signals, enabling us to better understand the more intricate dependencies in the data. Finally, the proposed system leverages data augmentation with Generative Adversarial Networks (GANs) to generate a wide variety ofcomplex samples, which help improve model capability and robustness. It also uses Triplet Loss, which shows it can work better on imbalanced classes and tiny differencesin different cardiac arrhythmias. Compared with other methods, our results exhibit great improvements in classification performance, particularly for rare arrhythmias. Model Interpretability is based on SHapley Additive exPlanations(SHAP) and Gradient weighted Class Activation Map (Grad-CAM), which interpret the model decisions
State of the Art in the Use of Wireless Sensor Networks (WSN) and IoT Devices for Water Source Monitoring in Urban Environments
No full textObjective: The study aimed to analyze the use of wireless sensor networks (WSN) and IoT devices in the monitoring of water sources in urban environments, focusing on key applied technologies, advancements, and technological challenges. Methodology: A systematic review of 37 scientific articles indexed in Scopus between 2014 and 2023 was conducted, focusing on the analysis of IoT devices, SCADA systems, sensor networks, drones, and unmanned vehicles, as well as the integration of machine learning algorithms for resource use prediction and optimization. Results: The findings show that most research concentrates on the use of IoT and sensor networks for water quality monitoring and resource management. The implementation of drones and unmanned vehicles has enhanced monitoring capabilities in remote areas. Predictive models based on machine learning have improved the efficiency of detecting water-related events such as floods, in addition to enhancing decision-making regarding resource use. Discussion: Despite advancements in the development of water monitoring technologies, challenges remain in system standardization and real-time data integration, underscoring the need for further development of more robust and scalable technological solutions. Conclusions: This study highlights the importance of emerging technologies, such as IoT and sensor networks, in the management and monitoring of water resources, emphasizing their positive impact on the sustainability and efficiency of water systems in urban environments
REVIEW OF MOBILE APPLICATION PERFORMANCE EVALUATION TO ENHANCE SELECTION AND PREDICTION IN MOBILE APP DEVELOPMENT
No full textIn the rapidly evolving mobile application landscape, understanding user preferences and optimizing application performance are critical factors for developers and consumers alike. This study aims to provide a structured framework for analyzing mobile application research conducted between 2019 and 2023, categorizing efforts into predictive analytics, user sentiment analysis, and feature prioritization to streamline the processes of application selection and development. The proliferation of mobile apps and diverse user needs have created a complex environment, with users struggling to identify suitable applications and developers facing challenges in ensuring functionality and profitability. This comprehensive review synthesizes findings from the past four years, proposing an integrated structure to leverage predictive analytics for anticipating user needs, user sentiment analysis for understanding customer preferences, and feature prioritization for optimizing application development. By adopting this holistic approach, the research aims to enhance the selection process for users and improve the overall performance and profitability of mobile applications
A Predictable performance Multi-controller-based Monitoring Framework for SDN
No full textSoftware defined networks (SDNs) have been released for dynamic operations of the networks and for controlling scalable networks. In SDN, the operating processes are controlled by a centralized controller. The performance of the centralized controller is decreased by increasing the scale of the network. Monitoring functionality is an essential element of any network system. The monitoring performance under centralized controller is decreased with large scale networks. In this paper, a proposed monitoring framework for multi-controller software defined networks is introduced. The introduced framework enhances the reliability and performance of large-scale software defined networks. Many copies of the proposed monitoring framework can run with SDN multi-controller for monitoring large-scale networks, detecting failure in any controller, and failover of the network failure. All copies of the introduced monitoring framework run in parallel, each on a slice to enhance the performance of the SDN network. All copies receive the requests from network applications, collect considerable amounts of measurements data, process them, and return the results to the network applications.
The contribution of this paper is introducing a reliable and high-performance multi-controller-based SDN monitoring framework. The introduced monitoring framework monitors large scale SDN networks with good performance and enhances network reliability. It has the capabilities to monitor in parallel many network applications where each application runs on a network slice. Each slice is controlled by an SDN controller and monitored by a copy of the introduced monitoring framework. The copies of the introduced monitoring framework are communicated in a synchronization scheme. 
Review of Using Technologies of Artificial Intelligence in Companies
Get PDFArtificial intelligence (AI) has become increasingly prevalent in business as companies adopt machine algorithms that can learn and improve over time. These AI-powered solutions are used in various business areas, including operations, analytics, product personalisation, marketing, sales, customer service, and human resource (HR). AI can help companies automate mundane tasks, make smarter decisions based on data and insights, and provide capabilities for smoother customer experiences, better customer service, and increased efficiency. AI has also allowed companies to enhance the quality of their digital services, optimise supply chain processes, and gain access to real-time insights and analytics. Companies can use AI to reduce lead times, generate new customer insights, improve customer service, and create meaningful customer experiences. This paper aims to address the gap in knowledge on incorporating AI into business strategy by conducting a critical literature review, synthesising current approaches and frameworks, highlighting potential benefits, challenges, and opportunities, and discussing future research directions
Preserving the Integrity of Li Yu's Craft Aesthetics using the Digital Information Technology
Get PDFPreserving the integrity of ancient aesthetic works has become a greater matter of concern in the modern digital age. As the internet is streaming with data that are collected from multiple sources, it imposes a severe threat to the integrity and security of the original aesthetic of many craft works. This paper proposes a novel framework that attempts to preserve Li Yu's craft aesthetics, which can be explored from his wide range of literary collections. As the author is popular for his excellence in many art forms like gardening, theatre arts, etc, recreation of his work is a major problem of concern. The framework initially pre-processed the corpus to extract keywords, which are then subjected to a semantic vector to find the closely related as well as differing words. Then, these words are subjected to geometric perturbations to impart versatility to the training phase. The text under screening is fed to the classification phase to find whether it is an altered or modified work of Li Yu, thus isolating the author's work from being reproduced, which greatly affects the aesthetics. As a future extension, the work can be trained with different classifiers to develop a defrauding system
A Survey of Biometric Recognition Systems in E-Business Transactions
Get PDFThe global expansion of e-business applications has introduced novel challenges, with an escalating number of security issues linked to online transactions, such as phishing attacks and identity theft. E-business involves conducting buying and selling activities online, facilitated by the Internet. The application of biometrics has been proposed as a solution to mitigate security concerns in e- business transactions. Biometric recognition involves the use of automated techniques to validate an individual's identity based on both physiological and behavioural characteristics. This research focuses specifically on implementing a multimodal biometric recognition system that incorporates face and fingerprint data to enhance the security of e-business transactions. In contrast to unimodal systems relying on a single biometric modality, this approach addresses limitations such as noise, universality, and variations in both interclass and intraclass scenarios. The study emphasizes the advantages of multimodal biometric systems while shedding light on vulnerabilities in biometrics within the e- business context. This in-depth analysis serves as a valuable resource for those exploring the intersection of e-business and biometrics, providing insights into the strengths, challenges, and best practices for stakeholders in this domain. Finally, the paper concludes with a summary and outlines potential avenues for future research
Deep Belief Neural Network Framework for an Effective Scalp Detection System Through Optimization
Get PDFIn an era where technology rapidly enhances various sectors, medical services have greatly benefited, particularly in tackling the prevalent issue of hair loss, which affects individuals' self-esteem and social interactions. Acknowledging the need for advanced hair and scalp care, this paper introduces a cost-effective, tech-driven solution for diagnosing scalp conditions. Utilizing the power of deep learning, we present the Grey Wolf-based Enhanced Deep Belief Neural (GW-EDBN) method, a novel approach trained on a vast array of internet-derived scalp images. This technique focuses on accurately identifying key symptoms like dandruff, oily scalp, folliculitis, and hair loss. Through initial data cleansing with Adaptive Gradient Filtering (AGF) and subsequent feature extraction methods, the GW-EDBN isolates critical indicators of scalp health. By incorporating these features into its Enhanced Deep Belief Network (EDBN) and applying Grey Wolf Optimization (GWO), the system achieves unprecedented precision in diagnosing scalp ailments. This model not only surpasses existing alternatives in accuracy but also offers a more affordable option for individuals seeking hair and scalp analysis, backed by experimental validation across several performance metrics including precision, recall, and execution time. This advancement signifies a leap forward in accessible, high-accuracy medical diagnostics for hair and scalp health, potentially revolutionizing personal care practices
Graphic Style Transfer Technology in Multimedia Communication: An Application of Deep Residual Adaptive Networks in Graphic Design
Get PDFWith the rapid development of wireless network technology and the rapid popularity of portable smart terminals, multimedia communication based on images and videos has become the favorite way of communication in the new era. Image style transfer technology is one of the research directions that has attracted much attention in the field of multimedia communication. To achieve the diversification of images and ease of use in the multimedia communication process, this paper researches the multimedia network communication technology and image style transfer technology. By combining visual style transfer technology and depth residual adaptive network technology in multimedia communication technology, the redesign and creation of graphics can be carried out effectively. The resulting graphics can meet the needs of the art creators and the technique provides higher creative efficiency, excellent peak model signal-to-noise ratio and structural similarity performance, and output levels that meet the basic needs compared to traditional manual design. The method can be effectively used in urban building appearance design and art creation and has good theoretical and practical research value