Recycling is one of the most important approaches to safeguard the environment since it aims to reduce waste in landfills while conserving natural resources. Using deep Learning networks, this group of wastes may be automatically classified on the belts of a waste sorting plant. However, a basic set of connected layers may not be adequate to give satisfactory accuracy for such multi output classifier tasks. To optimize the gradient flow and enable deeper training for network design with multi label classifier, this study suggests a residual-based deep learning convolutional neural network. For network training, ten classes have been explored. The Directed Acyclic Graph (DAG) is a structure with hidden layers that have inputs, outputs, and other layers. The DAG network's residual-based architecture features shortcut connections that bypass some levels of the network, allowing gradients of network parameters to travel freely among the network output layers for deeper training. The methodology includes: 1) preparing the data and creating an augmented image data store; 2) defining the main serially-connected branches of the network architecture; 3) defining the residual interconnections that bypass the main branch layers; 4) defining layers, and finally; 5) creating a residual-based deeper layer graph. The concept is to split down the multiclass classification problem into minor binary states, where every classifier performs as an expert by concentrating on discriminating between only two labels, improving total accuracy. The results achieve (2.861 %) training error and (9.76 %) a validation error. The training results of this classifier are evaluated by finding the training error, validation error, and showing the confusion matrix of validation data

The success of institutions is in providing good services to customers through making various and strategic decisions as quickly and accurately as possible. Expert systems are important in making strategic decisions by improving the quality of decision-making. An expert system is an information system that relies on systems based on knowledge bases. It may also contain a knowledge base in a particular field and advanced programming methods that make a computer capable of thinking, deducing, and providing advice and expertise. This research aims to improve the period of time in decision making by using multiple experts systems and decision support system. The new system is implemented in MATLAB. For analyzing the proposed system, the data is collected by the questionnaire method and distributing 70 questionnaires to managers, heads of departments, employees, and those responsible for making various decisions in the institution under study. The valid questionnaires were 63. A questionnaire and the data were analyzed using the statistical program (SPSS). Based on the obtained data, the linguistic variables were created for the time period of the data, and the skills of the decision-maker as inputs for the proposed expert system, and a stage was added to the stages of the proposed expert system to diagnose the problem and set the goal to obtain the correct strategic decision. To obtain this advantage, let’s use expert systems compared to traditional methods of decision-making

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The strategy of the global telecommunications companies always provide the best services to users is the high speed in the transfer of reliable data and a large amount of data (calls) with the least effort and cost. This study focuses on the use of simulations for the implementation of Multiplexing technique and DE Multiplexing technique through programming using Visual Basic language. The simulation helps to give an easy and clear idea of how Multiplexing technique and DE Multiplexing technique work. The system was created and implemented using the Visual Basic 8.0.