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Copyright of International Journal of Economic & Social Research is the property of Abant Izzet Baysal University, Faculty of Economics & Administrative Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Using the Convolutional Neural Network (CNN) algorithm, this research aims to create a better lecturer attendance application that improves the attendance system and creates peace of mind when lecturers arrive at national universities. The author analyzes the results of applying deep learning algorithms to an experimental face recognition system that uses convolutional neural networks. The purpose of this study is to show that deep learning algorithms can improve the accuracy and efficiency of recording presence. In addition, the goal of this research is to create a timekeeping application using face recognition technology that is expected to have a high level of accuracy. In addition, this research includes a modification of the CNN model. This modification resulted in an epoch value of 75 for training of 100% and test of 95%. Analysis of results, drawing conclusions, and suggestions for additional development are the final stages of this research. Evaluation of the integrated system is done by collecting actual attendance data and comparing it with the attendance records created by the system. This validation will help explain the performance of the system and find problems or vulnerabilities that may need to be fixed. [ABSTRACT FROM AUTHOR]

The identification of individuals through finger vein patterns has become a prominent biometric technique due to its non-invasiveness and uniqueness. Convolutional neural networks (CNNs) have been at the forefront of this technology, offering impressive recognition rates within large, labeled datasets. Despite their successes, the application of CNNs to finger vein recognition remains a challenging task, largely due to the high dimensionality of input data and the multitude of classification outputs required. This paper presents an optimized CNN model designed to address the intricacies of finger vein image classification. It is posited that increasing the number of feature extraction layers, coupled with a strategic selection of kernel sizes for each layer, significantly enhances model accuracy. Through a series of systematic experiments, the optimal layer configurations were identified, resulting in an architecture that surpasses previous models in classification precision. The proposed CNN architecture demonstrates a classification accuracy exceeding 99%, an improvement over existing method. It is noteworthy that the development of this model has been constrained by the limited scale of current finger vein databases, which poses risks of overfitting. Hence, the expansion of these databases is suggested as a future avenue to reinforce the robustness of the training process. The results depicted in this study underscore the potential of deep learning techniques in biometric security, with the advanced CNN model setting a new benchmark in finger vein recognition. [ABSTRACT FROM AUTHOR]

There have been several security solutions based on artificial intelligence (AI), such as intrusion detection systems (IDS),cyberattacks are increasing because big data is increasing by using the internet on all sides of life, therefore, unbalanced data poses a serious problem in intrusion detection systems. The proposed detection system that is based on deep learning Convolutional Neural Network(CNN )partitions data into training and testing., Creating the classifier model for the Principal Component Analysis (PCA )technique of reducing features, is required for the development of intelligent analytic tools that need data pre-treatments and deep learning algorithm-performance enhancement. The UNSW-NB15 data set is used According to experimental findings, We employed a number of evaluation tools to assess the proposed NIDCNN strategy relying on the UNSW-NB15 data set that takes 30% of it for testing and after processing this part of the data became used to evaluate the proposed system. Measures such as a classifier's F-Score, precision, and sensitivity (Recall) are evaluated. Classifier performs better than the other approaches at determining if the data stream is normal or malicious. which is used to assess deep learning's effectiveness, the suggested model results from a high level of accuracy. The experimental findings demonstrate the suggested system's ability to accelerate the intrusion detection process while reducing memory and CPU usage. Experimental results prove the theoretical considerations.Because the UNSW-NB15 data set contains a wide range of patterns that accurately represent contemporary real network traffic, New NIDS algorithms can therefore be assessed using it. [ABSTRACT FROM AUTHOR]

The agricultural sector plays a crucial role in the majority of developing countries like India. But in recent times agriculture production is following a downward trend due to various plant diseases with an increase in investment costs. This work conducts a survey on deep learning techniques training issues related to a Chili leaf diseases dataset. Especially the work focused on the viability of the Squeeze-Net training architecture on the Chili leaves to train the two classes of diseased leaves namely Geminivirus and Mosaic. The dataset comprised of 160 Chili diseased photographs deployed from the Kaggle public domain is subjected to the Squeeze-Net convolutional neural network (CNN) to test the training accuracy. The obtained training accuracy ranges from 50% to 100% by considering various training properties like CNN optimizers SGDM, ADAM, and RMSPROP w.r.t Max_Epoches and assigning Dropout probability, Strides, Dilation factor, and padding values as constants. From the simulation is observed that the Squeeze-Net CNN architecture is achieving 100% accuracy in ADAM, and RMSPROP, where Max_Epoches are 40 and 35 respectively. But it suggested that the applicability of RMSPROP is good for training the Chili Dataset, where Max_Epoches are very less compared to the ADAM and SGDM. [ABSTRACT FROM AUTHOR]

Since the early days of 20 20, COVID-19 has tragic effects on the lives of human beings all over the world. To combat this disease, it is important to survey the infected patients in an inexpensive and fast way. One of the most common ways of achieving this is by performing radiological testing using chest X-Rays and patient coughing sounds. In this work, we propose a Convolutional Neural Network-based solution which is able to identify the positive COVID-19 patients using chest X-Ray images. Multiple CNN models have been adopted in our work. Each of these models provides a decision whether the patient is affected with COVID-19 or not. Then, a weighted average selection technique is used to provide the final decision. To test the efficiency of our model we have used publicly available chest X-ray images of COVID positive and negative cases. Our approach provided a classification performance of 88.5%. [ABSTRACT FROM AUTHOR]