International Journal of Communication Networks and Information Security (IJCNIS)
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Privacy-Preserving Techniques in Big Data Communication Networks: A Comprehensive Review
No full textBig data communication networks have revolutionized the way we handle vast amounts of information. However, they also pose significant privacy concerns, necessitating the development of robust privacy-preserving techniques. This review explores various privacy-preserving techniques in big data communication networks, examining their effectiveness, limitations, and potential areas for future research. A comprehensive evaluation of current methods, including cryptographic techniques, differential privacy, homomorphic encryption, and secure multiparty computation, is conducted. Furthermore, we provide a comparative analysis through tables, graphs, and diagrams for better understanding
Multi-stage Fine-tuning Approach for AI-based Chest X-ray Abnormality Detection
No full textCorrect detection and localization of thoracic diseases on chest X-ray images supports or even secures an early diagnosis and treatment-oriented planning. This paper proposes a version of the YOLOv5 deep learning model augmented with more sophisticated components, namely Spatial Pyramid Pooling (SPP), Path Aggregation Network (PANet), and Convolutional Block Attention Module (CBAM) to enhance overall stability and generalization for clinical use. The key components of the proposed model are first trained on the VinBigData Chest X-ray Abnormalities Detection dataset to improve feature extraction and adaptively stretch multiple image resolutions across different strides per pixel levels while focusing more attention on the region. Next, we make a multi-stage fine-tuning approach for real-world clinical data, which usually shifts domains in practical settings. Finally, the model is forced to be more resistant and less overfitting by performing real-world data augmentation instead of focusing on clinical variability. We further qualitatively assess the performance of our model on both the VinBigData test set and CheXDet dataset with only publicly available bounding box annotations on matching classes between the two datasets. Moreover, the model was integrated into a web application that could easily be employed in clinical environments for real-time chest X-ray analysis and may assist with more accurate diagnosis at an early stage
Emergency Remote Education
No full text The Coronavirus (COVID-19) pandemic compelled several countries to follow severe protocols and led to school closures. With schools under the pandemic, distance learning and Emergency Remote Education (ERE) have come to be the only method of schooling accessible. Learners, parents, and teachers in Iran like other countries have faced many difficulties in remote teaching and learning as a result of the existing restrictions related to procedural, educational, and social problems. This mixed-method investigation explored the opportunities and challenges of primary to high school students, parents, and teachers during ERE. Thus, an explanatory design was developed to expansively explore the perceptions of teachers, parents, and students on distance learning and homeschooling throughout the lockdown. Overall, this study revealed that a significant number of students, parents, and teachers preferred classroom-based schooling to homeschooling and distance learning. The minority considered the ERE style an acceptable method and likewise more solutions should be provided for boosting the effectiveness of teaching and educational conditions
Landscape Planning and Public Space Optimization of Grand Canal Cultural Park based on Computer-aided Design
Get PDFCollaborative As the rate of urbanisation is increasing at a very fast pace, there is a huge demand for Landscape planning with proper public space optimisation. China, a country which has witnessed rapid growth in population and economics, has become a target of urbanisation. The country is known for its aesthetic appeal in the Grand Canal cultural park, which has been a primary factor in the country's development since ancient times. However, landscape planning with efficient utilization of available public space in the region using contemporary computing technologies is the need of the hour. This work focuses on deploying Computer-Aided Design in landscape planning using the Artisan plugin, specifically meant for environment planning. The special tools available in this plugin help landscape planning architects to accurately study the characteristics of the landscape, like terrain, water bodies, planar regions, etc. Also, this work proposed a four-phased model that aids the development process of landscape planning activity by including micro-level factors that directly interact with the environment. In future, this model could be extended to include AR, VR, AI and ML technologies
Steiner Whisper Clustering and Gated Recurrent Trust-Based Secure Routing for Underwater Sensor Networks
Get PDFUnderwater Wireless Sensor Networks (UWSNs) have prompted the growing curiosity of several researchers in industrial establishments, surveillance, trading, and academic purposes over the past few years. In recent days, the application of UWSNs in different areas of application has seen a monumental advancement. In UWSN, several techniques are developed by clustering as well as deep learning for optimizing the problem of secure data routing. In this work, an energy-efficient method called Steiner Chinese Whisper Clustering and Memory Gated Recurrent Trust-based (SCWC-MGRT) secured routing in UWSN is proposed. The energy-efficient SCWC-MGRT method for secured routing in UWSN is split into two sections: clustering and secured routing. Initially, based on the energy level, underwater sensor nodes are grouped by employing the Steiner Chinese Whisper Node Clustering model. Here, the energy consumption model is designed separately for node initialization and data forwarding using the Steiner Triangulation function. Finally, maximum residual energy and distance were utilized to choose the cluster head. Then, secured data routing with underwater sensors is carried out by means of a memory-centered gated recurrent trust-based secure routing model. By the memory-centred nature, specified underwater sensor node for current time stamp and hidden state of previous time stamp, validation is through and therefore secured routing is secured. The NS2 platform was utilized to simulate SCWC-MGRT and compare the two other routing methods. SCWC-MGRT method of outcomes appreciably enhances energy efficiency, data confidentiality rate, and delivery ratio without forfeiting too much end-to-end delay
Role-Based Access Control (RBAC) Enabled Secure and Efficient Data Processing Framework for IoT Networks
No full textInternet of Things (IoT) has the potential to significantly impact various domains e.g. health, transportation, automation, and emergency response to both man-made and natural disasters, particularly in scenarios where human decision is challenging. In this research a Role-Based Access Control (RBAC) Enabled Secure and Efficient Data Processing Framework for IoT Networks has been proposed. This framework ensures robust security and optimized data handling through granular access control mechanisms based on predefined roles. By leveraging RBAC, it mitigates unauthorized access risks, thereby safeguarding sensitive IoT data during transmission and storage. Our approach emphasizes efficiency by streamlining data processing workflows, reducing latency, and optimizing resource utilization. The framework is designed to scale with IoT network expansions and adapt to evolving security needs, promising enhanced reliability and trustworthiness in data operations for contemporary IoT environments. According to this research work current security effectiveness is 99 percent.Home area network (HAN) can be used for smart connectivity of different home appliances using IoT and automatic start and stop feature may be possible.Access control server (ACS) is used to control access and provide permission for different operations
Mechanical Strength Behavior of Recycled Aggregate Concrete
No full textConcrete is the most consumed material on Earth next to water. The demand of concrete as a material of construction is increasing with increasing population explosion and urbanization. The huge requirement of concrete requires a huge requirement of natural resources to satisfy the need of demand of infrastructure development. To minimize the use of natural resources and reduce the effect of environment impact is to reduce the usage of natural resources and also reduce dumping of waste into landfill areas. Hence a pilot study is carried out to partially replace natural coarse aggregates by construction and demolition waste (C & D). Studies are carried out for various replacement ratio ranging from 0% to 100% with 10% increment. Initially characteristic study of C & D waste material was carried out to know the suitability of recycled aggregates. Mix design is carried out using ACI method to arrive at high strength concrete of M60 grade. Specimens are tested for mechanical properties like compressive strength, split and flexural strength. The experiment is also carried out to know the young’s modulus of recycled aggregate concrete and understand the variation with respect to varies codes of practice. The results indicate that concrete with aggregate partially replaced with RCA exhibits considerably good performance for 45% replacement ratio. Hence C & D waste can be used as partial replacement to arrive concrete of strength to be suitably used as structural concrete
Optimizing Geometric Dimensions for Effective Field Assessment in Sugarcane Crop Monitoring using GIS-based CNN Model and Alerting identified Areas of infection in Diagnosed Farms
No full textUtilizing a Geoinformatics Information System (GIS)-based deep learning model for sugarcane crops can effectively address challenges faced by farmers, particularly in disease detection and classification within specific regions. To overcome overfitting issues, the study employs data augmentation techniques, enlarging the dataset and introducing a self-created database of sugarcane leaf diseases to the research community. The database comprises 2539 images across five classes/categories, encompassing both healthy and diseased leaves. Given the significant threat sugarcane leaf diseases pose to smallholder farmers, an automated diagnosis model is crucial for early detection. The proposed methodology generates predictions for test samples, incorporating scores from conventional metrics such as recall, precision, accuracy, and f1-score obtained from base learners. Customized GIS-based CNN models are employed for training, leading to improved results. The approach is evaluated using a real-world sugarcane leaf dataset, with GIS providing information on coverage, mapping, and disease classification on land cover. Satellite imagery is utilized for mapping areas with different sugarcane diseases, identifying specific characteristics and disease infestation using the GIS model. The GIS-based CNN model exhibits a training accuracy of 0.9591 and validation accuracy of 0.9948 on 30th epochs and an acceptable number of parameters for disease recognition. Validation is conducted using pictures collected from villages in Maharashtra, India. Comparisons with another other models highlights the effectiveness of the GIS-based CNN model as a valuable tool for recognizing sugarcane diseases. Overall, the study underscores the potential of integrating GIS and deep learning in agricultural monitoring, offering a promising solution for early disease detection and classification in sugarcane crops
Artificial Intelligence and Innovation Management: A Catalyst for Organizational Growth
No full textArtificial intelligence (AI) has the enormous potential of completely revolutionizing innovation perseverance, speeding up research and development processes, improving decision making as well build open innovations among business partners. AI can help centralize information and expedite the innovation process, delivering direct added value to internal stakeholders or external partners as they compete in marketplaces. The article also dwells on the limitations of AI inclusions — ethical questions, threats to data privacy and a complimentary synergy between human-AI interaction. This paper provides real-world examples that show how AI can be a powerful source for continuous innovation and organizational growth. Results indicate that, despite its high potential, the proper deployment of AI is by no means a foregone conclusion and must be carefully considered
Future-Proofing Careers in an Ai World: Strategies for Workforce Adaptation and Resilience
No full textArtificial intelligence (AI) has become a prominent subject of discussion, attracting much interest owing to its possible impact on employment, the labor market, and future work. Thus, has sparked significant intrigue and concern among individuals. AI has the extraordinary capacity to revolutionize a variety of industries and improve operational efficiency. Nevertheless, this progress also raises concerns regarding the possible displacement of humans and the subsequent need to acquire new skills and training. Furthermore, AI has the potential to enhance inventory management, reduce waste, and optimize supply chain management, resulting in additional cost reductions. Additionally, by leveraging advanced algorithms and machine learning techniques AI can contribute to the mitigation of workplace accidents and injuries. By harnessing the power of AI, organizations can enhance various aspects of workplace operations such as efficacy, productivity, cost-effectiveness, and safety. Although there may be some initial apprehensions regarding employment displacement, it is imperative to understand the undeniable advantages that AI brings to the organization. To remain competitive and capitalize on the advantages of this swiftly evolving technology, it is crucial for businesses to strategically integrate AI into their operations. The current study seeks to examine a case study about the impact of artificial intelligence (AI) on the workforce and the potential landscape of employment. Subsequently, discuss the strategies for the workforce to stay relevant and competitive in an AI-driven world