International Journal of Data Informatics and Intelligent Computing

Adoption of Blockchain Technology for Data Management of Human Resource Demands in Organizational Enterprises

Paryati, Prabhdeep Singh — Sat, 13 Apr 2024 00:00:00 +0000

Human resource data accuracy has become a significant factor in assessing the effectiveness and performance of human resource management (HRM) in businesses. Numerous human resource hazards originating from asymmetric information continue to cost firms money and even put companies out of business despite the rapid progress of mobile technology and Internet technology. Blockchain is a transaction platform that makes use of the immutability qualities of immutable data records. Because of the dispersed nature of this technology, it has a broad variety of applications in numerous industries. Seeing the potential of this new technology, we picked the HRM sector since this data must be kept private and secret while still having great research value. This research presents a unique blockchain-based encryption method for establishing an HRM data method that decreases the danger of HRM data integrity. The data is authenticated using smart contracts, and data is encrypted using the proposed Improved Identity-based blowfish encryption algorithm (IIBEA) with Particle Swarm Optimization (PSO). New blocks are verified using the Proof of Work (PoW) consensus process. The suggested model's metrics are examined and compared to traditional encryption approaches. This model solves the lack of difference in the authenticity of HRM information, and it will give real and effective information to the HRM of organizational companies.

Asynchronous Federated Learning with Grey Wolf Optimization for the Heterogeneity IoT Devices

Prabhdeep Singh, Bambang Hari Kusumo — Thu, 25 Apr 2024 00:00:00 +0000

The Internet of Things (IoT) creates new options for real-time data collection and Machine Learning (ML) model development. Nonetheless, it's feasible that a particular IoT device does not have sufficient computational power to train and implement a complete learning model. However, there are significant communication costs and data security and privacy concerns associated with sending actual data to a centralised server with a lot of computational power. Federated Learning (FL) is a potential way to train ML models using low-powered devices and Edge Servers (ES) since it is a distributed ML architecture. However, the vast majority of the works in existence make the unsustainable assumption of a synchronized parameters update manner with similar IoT nodes and reliable communications networks. To increase training efficiency and accelerate the speed for heterogeneous IoT devices in an unreliable network environment, we designed an Asynchronous Federated Learning strategy with Grey Wolf Optimization (AFL-GWO) in this research. In particular, we develop a Lightweight Node Selection (LNS) technique and propose an AFL-GWO model to efficiently complete learning tasks. To ensure that diverse IoT nodes with varying computational capabilities and network connectivity are represented in the global learning aggregate, the proposed technique makes node selections on an iterative basis. We show through extensive experiments that our suggested AFL-GWO system outperforms the state-of-the-art techniques on identically and independently distributed (IID) and non-IID data distribution in a variety of contexts.

Automated Fall Detection for Disabled Individuals Using Mobile Phone Sensors and Machine Learning: A Survey

Sun, 05 May 2024 00:00:00 +0000

Fall risks to health and safety are especially dangerous for those with impairments. An automated fall detection system is necessary, especially in medical and senior care. The elderly and individuals with impairments are particularly susceptible to falls, which frequently result in severe injuries and complications, thereby presenting a considerable threat to their overall health. The early discovery and response to a fall incidence can reduce immobilization and consequent health complications, saving lives. Automatic fall detection systems quickly and reliably indicate falls and dispatch medical or emergency assistance. Researchers have introduced various automatic fall detection methods using machines or deep learning. Most fall detection systems depend on wearable or stationary sensors, which restricts the user's mobility and accessibility. Conversely, mobile sensor-based fall detection leverages the widespread presence of smartphones by obtaining motion information via their integrated accelerometers and gyroscopes. Our primary objective is to develop a reliable fall detection method using a mobile phone sensor and machine learning. This paper examines several methods employed in the identification of falls and emphasizes the significance of utilizing mobile phone sensors in the process of fall detection. It also discusses recent research in this domain and highlights research challenges. This could potentially foster further innovation in the field.