Recommendation Systems: A Systematic Review

This article presents a comprehensive and objective systematic review of existing research on recommendation systems with regards to core theory, latest studies, various applications, current attitudes, and potential future applications. The research is mainly based on exploring professional peer-reviewed studies and articles and using their abstracts to create a comprehensive and unbiased review of existing research. The following search terms were used to identify articles and studies for the research: recommendation systems; recommender systems; core theory of recommender systems; current attitudes towards recommendation systems; latest studies on recommendation systems; applications of recommendation systems; potential studies on recommendation systems; and future potential applications of recommendation systems. The research also used the advanced search filter to locate recent studies for comparison by limiting the search by year to find studies published from 2021 onwards. Most literature on this area highlights the importance of recommendation systems in almost all aspects of modern life. Specifically, recommendation systems have become critical components in business, health care, education, marketing, and social networking domains. Additionally, most studies identified reinforcement of learning and deep learning techniques as significant developments in the field. These techniques form the backbone of most modern recommendation systems. The primary concern that could hinder further evolution systems is their consequent filter bubble effects which many studies showed to be problematic. Healthcare is a central area that shows tremendous potential for these systems. Although recommender systems have been implemented in this domain, there remains a lot of untapped potential that, if unleashed, could revolutionize medicine and healthcare. But the problems facing these systems have to be tackled first to establish trust. Keywords: Recommendation systems, Recommender systems, Deep learning, Reinforcement learning DOI: 10.7176/CEIS/13-4-04 Publication date:August 31st 202

Low-temperature photoluminescence spectra of highly excited quantum wires

Optical spectra of highly excited quantum wires at low temperatures have been studied within the dynamically screening approximation. We found a strong Fermi-edge singularity (FES) in the photoluminescence spectra. The spectral shape and FES intensity strongly depend on temperature in agreement with recent experimental results

A nutrient method for cutivation of macroalgae Ulva papenfussii

Macroalgae species of the genus Ulva are widely distributed in the wild. Many species of this genus has been used as food as an attractive material for the study of materials, fuels, food etc.. In this paper, we are focusing on nutrient method for cultivation of Ulva papenfussii and A nutrient source for cultivation of U. papenfussii was also investigated with the perspective of utilizing the produced biomass for feed. U. papenfussii is fragmented into 1 × 1 cm size, then it keep in Ulva extract of 0.1 g/l concentrate for 7 days. Then continue to keep fragments in the following conditions: 20 ml/l of PES medium, 700 μmol photon/m2/s of light, 25oC of temperature, 3% of salinity, 28 days of time. Under this condition the productivity U. papenfussii was 17.8 g/l of weight and its growth rate was 4.3–6.5% day. Nutritional cultivation is successful for U. papenfussii speceies, which is of great importance to study the potential of producing seaweed varieties like blades for commercial application of seaweed species.

Extended state observer based load frequency controller for three area interconnected power system

In this paper, we develop a new extended state variable observer based LFC scheme for three-area interconnected power systems. The extended state observerbased load frequency controllers are developed which utilize disturbance estimation techniques. The propose control approach assures that the fluctuating things of the load frequencies reaches to a safer range and the load frequencies can also be made at a very minimal not to have an effect on power quality and power flow in multi-area interconnected power system. The results of the simulations using MATLAB/SIMULINK done did not only address that the proposed newly control method works effectively but also change powerfully the parameter variations of the interconnected areas of the power system. Especially, it works very well to limit disturbances impact on interconnected areas in the system. Therefore, the performance of interconnected power system under different multi-conditions is simulated with the new control method to demonstrate the feasibility of the system

Optimal linear quadratic Gaussian control based frequency regulation with communication delays in power system

In this paper, load frequency regulator based on linear quadratic Gaussian (LQG) is designed for the MAPS with communication delays. The communication delay is considered to denote the small time delay in a local control area of a wide-area power system. The system is modeled in the state space with inclusion of the delay state matrix parameters. Since some state variables are difficult to measure in a real modern multi-area power system, Kalman filter is used to estimate the unmeasured variables. In addition, the controller with the optimal feedback gain reduces the frequency spikes to zero and keeps the system stable. Lyapunov function based on the LMI technique is used to re-assure the asymptotically stability and the convergence of the estimator error. The designed LQG is simulated in a two area connected power network with considerable time delay. The result from the simulations indicates that the controller performed with expectation in terms of damping the frequency fluctuations and area control errors. It also solved the limitation of other controllers which need to measure all the system state variables

An Improved MobileNet for Disease Detection on Tomato Leaves

Tomatoes are widely grown vegetables, and farmers face challenges in caring for them, particularly regarding plant diseases. The MobileNet architecture is renowned for its simplicity and compatibility with mobile devices. This study introduces MobileNet as a deep learning model to enhance disease detection efficiency in tomato plants. The model is evaluated on a dataset of 2,064 tomato leaf images, encompassing early blight, leaf spot, yellow curl, and healthy leaves. Results demonstrate promising accuracy, exceeding 0.980 for disease classification and 0.975 for distinguishing between diseases and healthy cases. Moreover, the proposed model outperforms existing approaches in terms of accuracy and training time for plant leaf disease detection