Cloud-fog computing has emerged as the contemporary approach for processing and analyzing Internet of Things applications due to its ability to offer remote resources. Cloud fog computing technology provides shared resources, information, and software packages, supporting distributed parallel systems in an open environment. It constructs and manages virtual machines to enhance efficiency and attractiveness. We have consistently strived to tackle challenges affecting the efficiency of cloud fog computing, including ineffective resource utilization and response times. The improvement of these challenges can be achieved through effective task scheduling and load balancing between Virtual Machines, this problem considered as NP-hard problem. This paper proposes a Hybrid K-means Clustering Honey Bee algorithm (HKCHB) to cluster Virtual Machines into two or more clusters. Subsequently, the hybrid Honey Bee algorithm is employed for task scheduling, enhancing load balance performance. The proposed algorithm is compared with other task scheduling and load balancing algorithms, including Round Robin, Ant Colony, Honey Bee, and Particle Swarm Optimization Algorithm, utilizing the CloudSim Simulator. The results demonstrate the superiority of the proposed algorithm, yielding the lowest response time. Specifically, the response time is reduced by 22.1%, and processing time is reduced by 47.9%, while throughput is increased by 95.4%. These improvements are observed under the assumption of multiple tasks in a heterogeneous environment, utilizing one or two Data Centers with Virtual Machines. This contribution gives the impression that network systems based on the Internet of Things and cloud fog computing will be improved in the future to operate within the framework of real-time systems with high efficiency.

Smart Vehicle Accident Prevention System is an innovative solution aimed at enhancing road safety and reducing the occurrence of accidents. Leveraging the Internet of Things (IoT) technology, this system combines real-time data acquisition, analysis, and intelligent decision-making algorithms to provide an effective accident prevention mechanism. The Vehicle Accident Prevention System is a com-prehensive project that aims to enhance road safety by utilizing Arduino microcontrollers and various sensors, including an alcohol sensor, temperature sensor, IR sensor and ultrasonic sensor. This report provides a detailed overview of the system’s design, implementation, and functionality.

Speaker diarization is the ability to compare, recognize, comprehend and segregate different sound waves on the basis of the identity of the speaker. This work aims to accomplish this process by segmenting, embedding and clustering the extracted features from the speech sample. In this work, Mel-Frequency Cepstral Coefficients (MFCC) are extracted and fed into Bi-Directional Long Short-Term Memory (Bi-LSTM) model for segmentation. Then d- vectors are extracted using pre-trained models from pyannote libraries. Spectral Clustering is used to group and segregate the audio of one speaker from another. The experimentation is carried out on two speaker speech audio files and the results indicate that the diarization is successful. The diarization error rate of 9.4% for a 2-speaker audio file is the lowest DER achieved for the given data set. This indicates the efficiency of the system and also justifies the combination of methods chosen at each step. By considering such exciting technical trends, we believe the work presented in the paper represents a valuable contribution for the community by providing the recent developments using Bi-LSTM and spectral clustering methods, which enables the future development towards speaker diarization.

Switched Reluctance Motor (SRM) has been successfully used for its excessive efficiency and higher strength to torque ratio. However, the only demerit it has its radial pressure and acoustic noise. When SRM achieves higher speeds, it tends to generate more force between stator and as a result acoustic noise with higher decibels is a concern. In this paper, a layout is used for reduction of both radial force and acoustic noise for eight/6 SRM using the fuzzy logic controller by controlling the speed and current as a feedback loop. The mathematical models are framed to resolve glitches associated to radial pressure and acoustic noise. In this proposed method the SRM produces a very low noise level when it rotates at the speed of 1200 RPM. This method also has been implemented in MATLAB/Simulink platform mainly to reduce the acoustic noise at higher speed in SRM.

Assuring product safety and quality in the explosives manufacturing process is critical today to protect worker and environmental safety. Temperature and humidity in the manufacturing plant are critical factors to consider because they can impact the manufacturing process and the quality of the final product. In this work, we design a temperature and humidity monitoring system for 31 explosive materials company using ethernet communication standard. In explosives factories, this communication standard is more suitable than other commonly used wireless communication technologies. We tested the system at 31 explosive materials factory. Test results show that the system operates stably and accurately. This system assists factory operators in increasing production efficiency, reducing dangers, and ensuring the quality of explosives.