The sailfish optimizer is a recent swarm-intelligence-based optimization algorithm which mimics the hunting behavior of sailfish in the ocean. It consists of two types of populations, namely, sailfish and sardine, where sailfish represent the candidate solutions and sardines freely maneuver in the search space. Existing research studies have shown that the sailfish optimizer suffers from limited global exploration capability, with local optimum stagnation and slow convergence speed. To address these limitations, an improved sailfish optimizer, namely, the Multi-Strategy Sailfish Optimizer, is proposed in this study. This improved version incorporates a modified search strategy for both sailfish and sardines, a non-linear attack power parameter, an improved hunting procedure, and a dynamic sardine population. The impact of all suggested improvements is analyzed experimentally. Several experiments with single-objective problems presented at the Congress on Evolutionary Computation in 2022 are performed to prove the effectiveness and efficiency of the proposed algorithm. This improved algorithm is compared with well-known optimization algorithms, such as the whale optimization algorithm, the sine–cosine algorithm, etc., and improved variants of those algorithms. A convergence behavior analysis is also performed using convergence graphs. Furthermore, the significance of the work is statistically validated. The analysis indicates that the Multi-Strategy Sailfish Optimizer performs significantly better than other optimization algorithms. It is also applied to solve the tension/compression spring design problem and the mobile robot path planning problem.

Taking good quality outdoor images poses interesting challenges since such scenes often experience poor visibility caused by weather conditions such as smog or fog. Image enhancement plays an important role in any field of life such as military, security surveillance, monitoring cameras for traffic and autonomous Vehicle. Night Scene enhancement has become an important element for video surveillance. Images taken in night time settings with poor lighting conditions have lots of information but, due to dark regions, details can easily be imperceptible. Important information and activities of interest cannot be seen without enhancement. A baseline that enhances night frames using a daytime prior captured by the same static camera. After registering day and night backgrounds, we use a background model to isolate moving objects and estimate per-pixel illumination in CIE-Lab space (L* a* b*). We then perform region-wise fusion of daytime and night-time illuminations using night with an adaptive threshold TT, while preserving the chroma of moving objects. On five public sequences, our method improves no-reference quality (NIQE ↓ x is 22.95% and LOE ↓ y 24.75 %). However, the proposed approach is very sensitive to still camera and color image. The experimental results show the effectiveness in efficiency and quality as compared with the past techniques.

There has been a significant growth in the production of electrical energy from renewable energy sources in recent years. It comes from major power plants that are connected to the grid or from standalone systems in isolated locations without grid supplies. Distributed renewable energy generation on the building's roof that is connected to the grid is now acknowledged as a viable electrical energy source. Depending on the utility, integration of the distributed hybrid power system (HPS) with the grid can be either on-grid, off-grid or hybrid. Photovoltaic (PV) is used in this study to model and examine these three systems. This paper proposed inverter with Canadian bifacial solar panels and lithium-ion battery system and Hilio graphic real time monitoring app. Understanding the distinctions, advantages, and limitations of each system type is essential for selecting appropriate solar solutions tailored to specific energy needs and environmental conditions and its economic analysis is experimentally done and achieved realistic results.

Smart home automation is a controlled integration of electrical and electronic systems designed to manage various types of home equipment. This smart technology covers the way how to control centralized system and offers a comprehensive control of home security. In this paper, a smart home automation system with security features is designed and implemented with a mobile application by using Wi-Fi connections. This study is done by using Raspberry Pi controller which is used to control the input and output devices. Input devices PIR sensor to detect motion and provide smart control, HC-SR04 for water level and a Picam to capture image to enhance security are integrated. The study proved to be a good choice for the user as the results are reliable and functional. The results provide its effectiveness in reducing electricity bill of (15%-20%) by managing the load as per the requirement. The data received from the PIR sensor contribute to monitor the movement and output device control while camera enhance the security by image processing. This system provides a user-friendly home automation control by using simple mobile application.