International Journal on Recent and Innovation Trends in Computing and Communication
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A Proposed Framework for Financial Institutions Using Blockchain Tecnology
Get PDFBlockchain technology plays a pivotal role in the banking industry, being recognized as one of the most crucial sectors. With the financial market expanding and the demand for banking services on the rise, the need for a robust banking system capable of offering top-notch services to clients is paramount. Regrettably, the current banking system in Egypt falls short of meeting these requirements. However, there is hope on the horizon, as blockchain technology has garnered widespread interest in the global banking community for its potential to mitigate fraud and other threats to banking operations. This study offers a concise overview of various blockchain architectures and trading systems, delving into their types and the popular platforms currently being utilized worldwide., such as Automated Clearing House (ACH)
A Novel Hybrid Protocol and Code Related Information Reconciliation Scheme for Physical Layer Secret Key Generation
Get PDFWireless networks are vulnerable to various attacks due to their open nature, making them susceptible to eavesdropping and other security threats. Eavesdropping attack takes place at the physical layer. Traditional wireless network security relies on cryptographic techniques to secure data transmissions. However, these techniques may not be suitable for all scenarios, especially in resource-constrained environments such as wireless sensor networks and adhoc networks. In these networks having limited power resources, generating cryptographic keys between mobile entities can be challenging. Also, the cryptographic keys are computationally complex and require key management infrastructure. Physical Layer Key Generation (PLKG) is an emerging solution to address these challenges. It establishes secure communication between two users by taking advantage of the wireless channel's inherent features. PLKG process involves channel probing, quantization, information reconciliation (IR) and privacy amplification to generate symmetric secret key. The researchers have used various PLKG techniques to get the secret key, sTop of Form
till they face problems in the IR scheme to obtain symmetric keys between the users who share the same channel for communication. Both the code based and protocol based methods proposed in the literature have advantages and limitations related to their performance parameters such as information leakage, interaction delay and computation complexity. This research work proposes a novel IR mechanism that combines the protocol and code-based error correction methods to obtain reduced Bit Mismatch Rate (BMR), reduced information leakage, reduced interaction delay, and reduced computational time to enhance physical layer secret key's quality. In the proposed research work, the channel samples are generated using the Received Signal Strength (RSS) and Channel Impulse Response (CIR) parameters. These samples are quantized using Vector Quantization with Affinity Propagation Clustering (VQAPC) method to generate the preliminary key. The samples collected by the two users who wish to communicate, (for example Alice and Bob) will be different due to noise in the channel and hardware limitations. Hence their preliminary keys will be different. Removing this discrepancy between Bob's and Alice's initial keys, using novel Hybrid Protocol and Code related Information Reconciliation (HPC-IR) scheme to generate error corrected key, is the most important contribution of this research work. This key is further encoded by the MD5 hash function to generate a final secret key for exchanging information between two users over the wireless channel. It is observed that the proposed HPC-IR scheme achieves BMR of 19.4%, information leakage is 0.002, interaction delay is 0.001 seconds and computation time is 0.02 seconds
An Overview of Inflammatory Spondylitis for Biomedical Imaging Using Deep Neural Networks
Get PDFAnkylosing Spondylitis (AS) is an axial spine inflammatory illness and also chronic that might present with a range of clinical symptoms and indicators. The illness is most frequently characterized by increasing spinal stiffness and persistent back discomfort. The affect of the sacroiliac joints, spine, peripheral joints, entheses and digits are the main cause of the illness. AS symptoms include reduced spinal mobility, aberrant posture, hip and dactylitis, enthesitis, peripheral arthritis, and buttock pain. With their exceptional picture classification ability, the diagnosis of AS illness has been transformed by deep learning techniques in artificial intelligence (AI). Despite the excellent results, these processes are still being widely used in clinical practice at a moderate rate. Due to security and health concerns, medical imaging applications utilizing deep learning must be viewed with caution. False instances, whether good or negative, have far-reaching effects on the well-being of patients and these are to be considered. These are extracted from the fact of the state-of-the-art of deep learning (DL) algorithms lack internal workings comprehension and have complicated interconnected structure, huge millions of parameters, and also a "black box" aspect compared to conventional machine learning (ML) algorithms. XAI (Explainable AI) approaches make it easier to comprehend model predictions, which promotes system reliability, speeds up the diagnosis of the AS disease, and complies with legal requirements
The Influence of Talent Mobility on Employee Performance in South Indian Software Enterprises
Get PDFEnsuring the continual improvement and performance of employees is a problem for any IT company in a competitive industry. Therefore, it is crucial for organisations to implement staff development methods in order to enhance employee performance. The objective of this study is to examine the impact of talent mobility on employee performance in software firms located in southern India. The survey included a total of 624 workers. The data were analysed using the structural equation modeling (SEM) model with the AMOS 23 software. The findings demonstrate that various dimensions of talent management, such as talent strategy, work/role design, workforce planning, workplace design, recruiting and selection, learning and development/career management, succession management, pay and rewards, diversity, equity, and inclusion, have a significant impact on employee performance
IOT based Intelligent Home Safety Control Centre
Get PDFThis research focuses on the development of an IOT (IoT) intelligent home safety control centre. System consists of a user side, a safety control centre, and a terminal node, with each component having specific functionalities to enhance the safety and safety of the intelligent home environment. Key modules include data encrypting/decrypting, safety communication, control centre of user access and verification of node identity, reliable platform for credibility verification, and log inspect and alarm. The system ensures data safety, authentication, credibility analysis, and system monitoring, thereby improving the safety performance and running efficiency of the intelligent home system
Trust-Based Routing Selection Policy on Mobile Ad-Hoc Network Using Aodv Routing Protocol
Get PDFThis study presents an enhanced Ad-hoc On-demand Distance Vector (AODV) routing protocol, termed Proposed_TAODV, designed to improve security in Mobile Ad-Hoc Networks (MANETs) of 150 nodes by incorporating trust-based mechanisms. Through a comprehensive simulation, the Proposed_TAODV is evaluated against existing AODV and Dynamic Source Routing (DSR) protocols under conditions of increasing malicious node presence. The results reveal that Proposed_TAODV maintains a higher Packet Delivery Ratio, experiences lower Average End-to-End Delay, and achieves greater Throughput compared to the benchmarks, indicating its superior performance and robustness. The integration of Direct Trust Evaluation, Indirect Trust Evaluation, and Trust Aggregation methods into the AODV protocol clearly enhances the MANET's resilience to security threats, establishing the Proposed_TAODV as a promising approach for securing MANETs against various forms of attacks and network disruptions
Image Recognition and Computer Vision in ML
No full textThis exploration investigates the unique scene of picture acknowledgment and PC vision inside a machine getting the hang of, utilizing Convolutional Neural Network (CNN), Support Vector Machine (SVM), K-Closest Neighbors (KNN), and Random Forest algorithms on the CIFAR-10 dataset. The review digs into their unmistakable exhibitions, giving a near examination that thinks about exactness, computational proficiency, and power. CNN arose as the leader, accomplishing an extraordinary precision of 80%, highlighting its ability in progressive element extraction. SVM and Random Forest displayed cutthroat exhibitions with exactnesses of 65% and 75%, separately, exhibiting their harmony among precision and computational expense. KNN, while basic, confronted difficulties in dealing with high-layered picture information, bringing about a lower precision of 45%. In the more extensive setting, the exploration lines up with related work, stressing the multi-layered uses of picture acknowledgment. From progressions in multi-name picture acknowledgment to applications in medical care, development, and biology, the review adds to the advancing scene of picture acknowledgment research
Profile of Dital Information System (SIDIA) As a Learning Platform to Accelerate Digital Transformation
Get PDFThe aim of this research was to develop SIDIA UNESA learning platform to improve teaching and learning process activities through the use of digital technology and digitalized teaching materials. This research method used the development of ADDIE model to develop the SIDIA platform, which included Analysis, Design, Development, Implementation and Evaluation stages. The research results at the Analysis, Design, Development stages produced the SIDIA platform profile, a platform that integrates the academic system and Learning Management System (LMS) with the following functions: 1) lecture preparation to facilitate lecturers in preparing Semester Lesson Plan and Student Assignment Plan based on the Outcome based Education Curriculum (OBE); 2) lecture implementation to facilitate lecturers and students in carrying out asynchronous and synchronous learning; 3) lecture assessment to facilitate lecturers in carrying out assessments and reporting assessment results. The integration of academic system and LMS results in well-administered teaching and learning process in the form of digital teaching materials and increases LMS activities on SPADA Indonesia page
Navigating the Landscape of Robust and Secure Artificial Intelligence: A Comprehensive Literature Review
Get PDFAddressing the multidimensional nature of Artificial Intelligence assurance, this thorough survey is dedicated to elaborating on various aspects of ensuring the reliability and safety of computerized systems. It steers through the turbulent seas of model enervates, unmodelled phenomena, and security menaces to give an elaborate lit review. The review touches upon the boisterous ways of addressing these intricate mitigation strategies for model errors used in the past, the challenges of under-specification with modern ML models, and how understanding uncertainty is crucial. In addition, it evaluates the AI system’s security basis, the emerging Adversary Machine Learning field, and its processes necessary for testing and evaluation of weaker adversarial case studies. The review of literature also looks upon the situation of DoD context, how the terrain surrounding developmental and operational testing is altering with all these shifts in culture that must be implemented if not to implement robust but secure AI implementation
Parallel Complementary Virtual Arrays Algorithm for Direction of Arrival (DOA) Estimation
Get PDFThis Paper discusses the challenges faced by previous method 2D Direction of Arrival (DOA) systems, such as low degrees of freedom, poor resolution, and significant estimation errors in scenarios with small snapshots. In response to these issues, the present method proposes a low-complexity 2D Direction of Arrival (DOA) estimation algorithm based on a parallel complementary virtual array.
The algorithm utilizes two mutually parallel complementary linear arrays to generate a virtual array, addressing the limitations of traditional parallel arrays. It constructs an extended matrix with enhanced 2D angular degrees of freedom using covariance and cross-covariance matrices. The final step involves obtaining automatic matching 2D angle estimates through Singular Value Decomposition (SVD) and Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT).
In comparison to traditional 2D DOA estimation methods, the proposed algorithm better exploits the information from the array's received data. It can identify more incoming signals, offering high resolution without the need for 2D linear search or angle parameter matching. Importantly, it demonstrates effective estimation even in scenarios with low Signal-to-Noise Ratio (SNR) and small snapshots. Experimental simulation results validate the effectiveness and reliability of the proposed algorithm