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Optical fibers are utilized widely for data transmission systems because of their capacity to carry extensive information and dielectric nature. Network architectures utilizing multiple wavelengths per optical fiber are used in central, metropolitan, or broad‐area applications to link thousands of users with a vast range of transmission speeds and capacities. A powerful feature of an optical communication link is sending several wavelengths through the 1300‐to‐1600‐ nm range of a fibre simultaneously. The technology of integrating several wavelengths onto a similar fiber is called wavelength division multiplexing (WDM). The principle of WDM utilized in concurrence with optical amplifiers has an outcome in communication links that permit rapid communications among users in the world's countries. This paper presents an overview of the challenges of fibre optic communication. This paper offers an outline of the areas to be the most relevant for the future advancement of optical communications. The invention of integrated optics and modern optical fibers takes place in the field of optical equipment and components.

<p><span>Facial exprestion recognition as a recently developed method in computer vision is founded upon the idea of analazing the facial changes in which are witnessed due to emotional impacts on an individual. This paper provides a performance evaluation of a set of supervised classifiers used for facial expression recognition based on minimum features selected by chi-square. These features are the most iconic and influential ones that have tangible value for result dermination. The highest ranked six features are applied on six classifiers including multi-layer preceptron, support vector machine, decision tree, random forest, radial baised function, and k-nearest neioughbor to figure out the most accurate one when the minum number of features are utilized. This is done via analyzing and appraising the classifiers’ performance. CK+ is used as the research’s dataset. Random forest with the total accuracy ratio of 94.23 % is illustrated as the most accurate classifier amongst the rest. </span></p>

Facial expression recognition (FER) has achieved an extreme role in research area since the 1990s. This paper provides a comparison approach for FER based on three feature selection methods which are correlation, gain ration, and information gain for determining the most distinguished features of face images using multi-classification algorithms which are multilayer perceptron, Naïve Bayes, decision tree, and K-nearest neighbor (KNN). These classifiers are used for the mission of expression recognition and for comparing their proportional performance. The main aim of the provided approach is to determine the most effective classifier based on minimum acceptable number of features by analyzing and comparing their performance. The provided approach has been applied on CK+ dataset. The experimental results show that KNN is proven to be better classifier with 91% accuracy using only 30 features.

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