International Journal of Communication Networks and Information Security (IJCNIS)
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Research on Optimization of Communication Network Security and Intelligent Supervision Model in Urban Community Aging Adaptation Renovation
No full textIn the process of increasing the number of elderly people, their care and attention have received greater attention, which has made traditional community environments face insufficient data encryption performance and data mining services in the communication network security of aging communities. This has made communication security optimization and intelligent monitoring models in urban community aging adaptation and transformation a focus of research in the field of data communication security. This study delves into the improvement and enhancement of intelligent monitoring models for communication network security in urban communities. Its goal is to improve the security and intelligent monitoring mode of data communication networks during the transformation process through technological innovation and progress in management practices, and further improve the security performance of data communication. This research has solved the communication network security problems existing in intelligent devices and Internet services in the community aging transformation. Through the analysis of improvement strategies, this article compares the basic performance indicators of data encryption, firewall performance, data analysis and mining, and intelligent recommendation in communication network security and intelligent recommendation between different models. The optimization of communication network security in urban community aging adaptation can ensure the data security of terminal devices and further improve and enhance intelligent recommendation services for aging, providing more references and suggestions for communication network security and intelligent monitoring
Impact Articles in Academic Writing Sustainability
No full textA research article is an academic work that presents the results of a systematic and in-depth study of a specific topic. Here are some characteristics of a quality research article; Clear Research: A quality research article begins with a clear and well-defined research, which provides direction for the research being conducted; 2. Accurate Methodology: The research uses a research methodology that is accurate and suitable for the purpose of the research, such as a qualitative, quantitative, or mixed approach; 3. Reliable and Transparent Data: Articles are supported by valid and transparent data, collected through a careful research process and appropriate methodology; 4. In-depth Analysis: The author conducts an in-depth analysis of the collected data, explores the findings thoroughly and provides an accurate interpretation; 5. Relationship with Relevant Literature: A quality research article reflects a good understanding of the relevant literature in the relevant field of study, and relates the findings of the study to previous studies; 6. Conclusions that can be held accountable: The conclusions drawn from the study are based on systematic analysis and available data, and can be held accountable by the findings of the study; 7. Significant Practical or Theoretical Implications: The article describes the practical or theoretical implications of the study findings, showing the relevance and involvement of the study to the relevant field
Bibliotherapy: Its Implementation in Achieving Organisational Goals
No full textSo many strategies have been used in organisations to inspire motivation in the achievement of organisational goals but some of them were without positive impact. It is prudent for organisations to try bibliotherapy as human nature usually do not want to be criticized, condemned and complained against. Bibliotherapy can trigger motivated action to individuals in organisations without being forced. This paper explores the concept of bibliotherapy and its potential implementation in achieving organisational goals. Bibliotherapy, traditionally used as a therapeutic tool for individuals, can be adapted to the organisational context to support employees in personal and professional development. It discusses the various ways in which bibliotherapy can be used to enable the achievement of goals. By leveraging bibliotherapy, organisations can foster a culture of continuous learning, innovation, resilience and inclusivity, ultimately contributing to the achievement of organisational goals.Bibliotherapy refers to carefully planned and structured interactions with literature guided by scaffolded questions and formally produced reflections to foster a motivated action. In today's DVUCADD environment an environment characterized by dynamic, volatile, uncertain, ambiguity, diversity and disruptive and competitive business environment, organisations are constantly seeking innovative approaches to enhance employee well-being, foster leadership development, build cohesive teams, manage change effectively and promote diversity and inclusion. Bibliotherapy, a form of therapy that uses literature to support individuals in addressing personal issues and achieving personal goals, presents an underutilized yet promising avenue for achieving these organisational objectives. By leveraging the power of bibliotherapy, organisations can create a culture of continuous learning, personal growth and resilience among their employees, ultimately contributing to the attainment of organizational goals
IMPLEMENTATION OF CLUSTER BASED SECURE HYBRID NETWORK FOR MANETS USING RNSR-ECC
No full textMANET (Mobile Ad hoc Networks) has led to the emergence of on-the-fly networking among the communicating nodes. However, they are vulnerable to a variety of attacks, especially in the routing layer because of open environment and communication takes place based on the mutual trust among the nodes. This paper introduces Elliptical Curve hybrid Cryptography (ECC) to overcome the drawbacks of previous work (remaining energy) for MANET of the research done in this area. Cryptography has different properties unlike conventional networks and introduces new issues as security solution for MANET. This research aims to propose CH-RNSR with hybrid cryptography (ECC), the main aim of the proposed research CH-RNSR with ECC algorithm is to increase the remaining energy with the number of malicious nodes detected during the communication through acknowledgement base than existing protocols with help of one of leading simulation model called Network Simulator 2 (NS2.34)
Recommendations for Changes in Education Practice in Sociology for Students
No full textThe fundamental subject behind various professions is education beliefs. Both learning and teaching are complex tasks. An effective communication between the student and the teacher is essential to implement these tasks. A proper language, symbolism and technical vocabulary is essential for realizing the basics of instructions in Sociology. There are many difficulties faced by the students in learning Sociology such as; complexities with abstract direction and time concepts, mistakes like recalling, reading and writing numbers, reversals, omissions, transpositions, substitutions and additions. This paper of research has been designed to identify the existing difficulties faced by the students in learning Sociology and recommend some solutions and changes in the education practice, which could be made in Sociology teaching for the students. The research study encapsulates a survey, which comprise of 14 teachers of Sociology and 200 students. Both open-ended and closed ended questions were present in the questionnaire, which was designed for the proposed research. Some of the common difficulties encountered by the students in learning Sociology is identified in the present scenario and also the perceptions of the teachers about the mathematical difficulties faced by students were identified and recommendations were made finally, which were contemplated on the learning strategies and the beliefs of the students
Analysis of Query Optimization Using Deep Reinforcement Learning Using Particle Swarm Optimization Algorithms
No full textQuery optimization is a well-studied problem in the database industry, with numerous solutions proposed over the last several decades. The success of deep reinforcement learning (DRL) has generated new opportunities in query optimization. One of the most difficult tasks in query optimization and query plan generation is determining the order in which join operations between tables are done (i.e. relations). Even if the final results of a query remain identical regardless of join order, the order in which the tables of a query are joined can have a significant impact on query execution time. Deep reinforcement learning, in particular a data-driven method to reasoning about enumeration heuristics, provides a novel algorithmic viewpoint on join enumeration. We must now control what training data the model views and how that data is featured, rather than the standard tunable parameters of a query optimizer. The algorithm makes few assertions about the cost model's structure or the search space's topology. We demonstrate that Q learning optimizes plans well across many different cost models for a small set of training queries. On the TPC-H database, the Query Optimization Algorithms Q-Learning and PSO (Particle Swarm Optimization) are assessed. During the evaluation, the optimizer failed to complete one query within the maximum time permitted, whereas the deep reinforcement learning-based models (Q-Learning) and heuristics model (PSO) managed. Of course, the standard join ordering problem is NP-hard, and practical algorithms use heuristics to make the search for a good plan efficient. A novel method for Query Optimization using Particle Swarm Optimization (QOPSO) and Deep Q Learning (DQL) for parameter tuning of Join Operation Cost and Processing Cost is suggested in this paper
Deep ConvBi-LSTM: A Robust 3D Room Layout Estimation Model for Indoor Environment
No full textRoom layout estimation is importance in recent times due to its extended application area. This process is highly challenging due to several factors affecting the room image such as clutter, occlusions, illuminations, etc. It is important to accurately identify the 3D layout of the room from a single 2D room image. The available techniques focused on determining the 3D layout but with limited number of features. It is important for a model to be fed with large number of features to result in successful predictions. To this extent, the proposed model introduced a robust 3D layout estimation framework for indoor environment. Initially, the input image is pre-processed and then subjected to layout estimation where our proposed model predicted both the edge maps and semantic labels for the image. For prediction, the proposed framework utilized the Deep ConvBi-LSTM model and a score function is defined and maximized by remora optimization algorithm (ROA) to obtain the optimal 2D layout from the candidate set. Finally, the 3D output is reconstructed from the 2D layout based on the layout coordinates and camera orientations. The experimental results of the proposed model proved the efficiency of the model in providing the desired performance
The Role of Artificial Intelligence in Neurosciences: An Approach to Neuroplasticity in the Era of AI for Personalized Rehabilitation
No full textThe accurate diagnosis of any disease is often a challenging task for healthcare professionals to ensure effective and high-quality patient care. However, with the digitization of health records and the discovery of Artificial Intelligence (AI), possibilities of human error in diagnosing diseases have greatly reduced. Artificial intelligence is the study of methods for developing artificially intelligent machines with certain abilities in problem-solving and self-decision support, just like the human brain. The technology base for the design of AI systems has borrowed most of its concepts from the architecture of the human brain. Neurosciences concentrate on the study of the human nervous system and brain, from disease to structure. Artificial intelligence technologies and algorithms have, in altogether, ushered in a paradigm shift for disease diagnosis that have armed healthcare professionals with valid and effective tools. AI has certainly become an indispensable tool in the field of neurology diagnosis, providing unparalleled capacities for the interpretation and analysis of intricate neurological data. The main goal of this review is to highlight the emerging AI technologies that are revolutionizing the management of neurological disorders and improving patients' overall functional outcomes. Neuroplasticity and AI integrated within Brain-Computer Interfaces (BCIs) embrace a novel paradigm for complex and most dependent rehabilitation. The use of AI is likely to yield massive growth for patients, stabilizing and magnifying the neuroplasticity process, especially for those undergoing rehabilitation
DP-IOT AD: DIFFERENTIALLY PRIVATE IOT ANOMALY DETECTION WITH UTILITY-PRIVACY TRADE-OFF OPTIMIZATION
No full textThe rapid proliferation of Internet of Things (IoT) devices has led toan unprecedented volume of data generated from diverse sources, necessitating robust and scalable anomaly detection mechanisms to ensure network security and operational reliability. However, the sensitive nature of IoT data poses significant privacy challenges. This paper proposes a novel privacy-preserving anomaly detection model specifically designed for IoT networks. The model integrates differential privacy techniques to anonymize data while maintaining its utility for anomaly detection. The proposed system architecture includes an IoT Gateway for data aggregation and preprocessing, a Privacy Module employing differential privacy for data anonymization, and a Feature Extraction component that derives relevant features from the anonymized data. We validate our model using multiple IoT datasets, demonstrating its effectiveness in detecting anomalies while preserving data privacy. Our experimental results show that the proposed model achieves high accuracy, precision, and recall, with minimal impact on data utility, thereby offering a promising solution for secure and privacy-aware anomaly detection in IoT networks
A Literature Review on Software Defect Prediction: Trends, Methods, and Frameworks
No full textIdentifying possible problems at an early point in the development lifecycle is one of the most important things that software defect prediction can do to enhance software quality and minimize development costs. This is one of the most crucial roles that software defect prediction can play. Of all the functions that software can perform, this is one of the most crucial ones. This literature review aims to offer a thorough examination of the research trends, methodologies, and frameworks utilized in the field of software defect prediction. This study analyzes a broad range of scholarly publications. These publications cover a wide variety of topics related to defect prediction, including dataset features, prediction models, assessment measures, and prediction approaches. Within the context of minimizing the negative consequences of defects on software quality and project schedules, the review emphasizes the significance of software defect prediction. This investigation identifies significant research themes such as the use of machine learning algorithms, feature selection approaches, and ensemble methods in defect prediction. The paper also scrutinizes the challenges and limitations associated with the diverse defect prediction methodologies currently in use. These include the imbalance of the dataset, the bias in feature selection, and the overfitting of the model. Additionally, it highlights the development of research fields and the opportunities for future study, such as the incorporation of domain knowledge, the incorporation of varied data sources, and the development of advanced approaches to predictive modeling. Furthermore, it acknowledges the existence of these opportunities. In its entirety, this literature review provides researchers and practitioners working in the field of software engineering with critical insights into the present state of the art in software defect prediction