Artificial intelligence (AI) has become a cornerstone of modern technology, revolutionizing industries from healthcare to finance. Convolutional neural networks (CNNs) are a subset of AI that have emerged as a powerful tool for various tasks including image recognition, speech recognition, natural language processing (NLP), and even in the field of genomics, where they have been utilized to classify DNA sequences. This paper provides a comprehensive overview of CNNs and their applications in image recognition tasks. It first introduces the fundamentals of CNNs, including the layers of CNNs, convolution operation (Conv_Op), Feat_Maps, activation functions (Activ_Func), and training methods. It then discusses several popular CNN architectures such as LeNet, AlexNet, VGG, ResNet, and InceptionNet, and compares their performance. It also examines when to use CNNs, their advantages and limitations, and provides recommendations for developers and data scientists, including preprocessing the data, choosing appropriate hyperparameters (Hyper_Param), and evaluating model performance. It further explores the existing platforms and libraries for CNNs such as TensorFlow, Keras, PyTorch, Caffe, and MXNet, and compares their features and functionalities. Moreover, it estimates the cost of using CNNs and discusses potential cost-saving strategies. Finally, it reviews recent developments in CNNs, including attention mechanisms, capsule networks, transfer learning, adversarial training, quantization and compression, and enhancing the reliability and efficiency of CNNs through formal methods. The paper is concluded by summarizing the key takeaways and discussing the future directions of CNN research and development. 

Convolutional Neural Networks: A Survey

Accurate breast cancer detection using automated algorithms remains a problem within the literature. Although a plethora of work has tried to address this issue, an exact solution is yet to be found. This problem is further exacerbated by the fact that most of the existing datasets are imbalanced, i.e., the number of instances of a particular class far exceeds that of the others. In this paper, we propose a framework based on the notion of transfer learning to address this issue and focus our efforts on histopathological and imbalanced image classification. We use the popular VGG-19 as the base model and complement it with several state-of-the-art techniques to improve the overall performance of the system. With the ImageNet dataset taken as the source domain, we apply the learned knowledge in the target domain consisting of histopathological images. With experimentation performed on a large-scale dataset consisting of 277,524 images, we show that the framework proposed in this paper gives superior performance than those available in the existing literature. Through numerical simulations conducted on a supercomputer, we also present guidelines for work in transfer learning and imbalanced image classification.

Imbalanced Breast Cancer Classification Using Transfer Learning

Recognition from faces is a popular and significant technology in recent years. Face alterations and the presence of different masks make it too much challenging. In the real-world, when a person is uncooperative with the systems such as in video surveillance then masking is further common scenarios. For these masks, current face recognition performance degrades. An abundant number of researches work has been performed for recognizing faces under different conditions like changing pose or illumination, degraded images, etc. Still, difficulties created by masks are usually disregarded. The primary concern to this work is about facial masks, and especially to enhance the recognition accuracy of different masked faces. A feasible approach has been proposed that consists of first detecting the facial regions. The occluded face detection problem has been approached using Multi-Task Cascaded Convolutional Neural Network (MTCNN). Then facial features extraction is performed using the Google FaceNet embedding model. And finally, the classification task has been performed by Support Vector Machine (SVM). Experiments signify that this mentioned approach gives a remarkable performance on masked face recognition. Besides, its performance has been also evaluated within excessive facial masks and found attractive outcomes. Finally, a correlative study also made here for a better understanding.

Masked Face Recognition Using Convolutional Neural Network

Multimodal biometrics technology has recently gained interest due to its capacity to overcome certain inherent limitations of the single biometric modalities and to improve the overall recognition rate. A common biometric recognition system consists of sensing, feature extraction, and matching modules. The robustness of the system depends much more on the reliability to extract relevant information from the single biometric traits. This paper proposes a new feature extraction technique for a multimodal biometric system using face–iris traits. The iris feature extraction is carried out using an efficient multi-resolution 2D Log-Gabor filter to capture textural information in different scales and orientations. On the other hand, the facial features are computed using the powerful method of singular spectrum analysis (SSA) in conjunction with the wavelet transform. SSA aims at expanding signals or images into interpretable and physically meaningful components. In this study, SSA is applied and combined with the normal inverse Gaussian (NIG) statistical features derived from wavelet transform. The fusion process of relevant features from the two modalities are combined at a hybrid fusion level. The evaluation process is performed on a chimeric database and consists of Olivetti research laboratory (ORL) and face recognition technology (FERET) for face and Chinese academy of science institute of automation (CASIA) v3.0 iris image database (CASIA V3) interval for iris. Experimental results show the robustness.

Face–Iris Multimodal Biometric Identification System

https://doi.org/10.1016/j.compbiomed.2022.105383

An automated diagnosis and classification of COVID-19 from chest CT images using a transfer learning-based convolutional neural network

The COVID-19 is an infectious disease that primarily affects the lungs and leads to death in the severe stage. It also changes the lung CT scans of affected patients. For introducing a more convenient COVID-19 identification technique during this pandemic, we have implemented a simple convolution neural network (CNN) based model by using lung CT images. And finally, we have used LeNet-5 CNN architecture for this purpose. For training and testing purposes, we have obtained a dataset that contained 349 COVID-19 lung CT frames and 397 number of NON COVID-19 CT frames. We have introduced the data augmentation technique and got 1744 CT frames of COVID-19 and 1588 CT frames of NON COVID-19 patients. Among them, we have used 80% of lung CT frames for training purposes and 20% frames for testing purposes. The total number of trainable parameters of our LeNet-5 CNN architecture was 82,146. After completing the whole process, we got the accuracy of 86.06%, f1 score of 87%, the precision of 85%, and recall of 89%, and area under the ROC curve of 0.86 for COVID-19 detection.

/pdf/detection-of-covid-19-from-ct-image-by-the-novel-lenet-5-cnn-7r2liz7feo.pdf

Detection of COVID 19 from CT Image by The Novel LeNet-5 CNN Architecture

The use of human biometrics for automatic identity verification has become widespread. Mostly used human biometrics are face, fingerprint, iris, gait, retina, voice, hand geometry etc. Among them iris is an externally visible, yet protected organ whose unique epigenetic pattern remains stable throughout one's whole life. These characteristics make it very attractive to use as a biometric for identifying individuals. This paper presents a detailed study of iris recognition technique. It encompasses an analysis of the reliability and the accuracy of iris as a biometric of person identification. The main phases of iris recognition are segmentation, normalization, feature encoding and matching. In this work automatic segmentation is performed using circular Hough transform method. Daugman's rubber sheet model is used in normalization process. Four level phase quantization based 1D Log-Gabor filters are used to encode the unique features of iris into binary template. And finally the Hamming distance is considered to examine the affinity of two templates in matching stage. We have experimented a better recognition result for CASIA-iris-v4 database.

Human iris as a biometric for identity verification

It's very difficult for an intelligent control system to recognize a person accurately as like humans. Though face recognition has some grand difficulties because of facial expression, illumination, lighting condition, pose etc., but the recognition of faces is very important for many applications such as: video surveillance, retrieval of an identity from a database for criminal investigations, home and office security, facebook and forensic applications. Within the last decade, the technological revolution has taken place all over the world based on computer vision. Face recognition has proved to be a challenging problem in computer vision. So, researches of computer science, biometrics, pattern recognition and neuroscientists have shown their deep interest in developing a consistent and proficient face recognition system. In this paper, we have proposed a method for face recognition by combining PCA and Back-propagation neural network which efficiently works in a constrained environment. Experiments demonstrate good results for face recognition on AT&T(ORL) database.

http://ieeexplore.ieee.org/iel7/7482923/7488029/07488138.pdf

PCA and back-propagation neural network based face recognition system

The outline and implementation of a low power real-time gas leakage detection and LoRa wireless communication technology-based notification system are accompanied by the multistage safety features in the kitchen as well as in the house have been inaugurated in this paper. The proposed system comprises a LoRa client and a LoRa gateway. LoRa client was made by the RFM69HW LoRa module, Arduino Uno, and some sensing devices, which were installed in the kitchen. Primarily a LoRa gateway was installed in our community. This LoRa gateway was associated with a cloud server (Ubidots) by employing Wi-Fi networks as transmission media. When gas leakage was detected, liquid crystal display (LCD), and the buzzer were activated, thereupon a GPS sensor identified the geographical position of the affected area, and LoRa client stored the measured data to Ubidots IoT platform, afterword data was sent to the user and police station, and eventually, the main power circuit at home was tripped off, and the exhaust fan was activated for avoiding further accidents. Here, for the efficient use of heat from a conventional gas stove in Bangladesh, and subsequently, for the secure power supply to the whole system, power was generated from the unused heat during cooking using the seeback concept of thermoelectric power generation(TEG) module.

A Novel Smart Gas Stove with Gas Leakage Detection and Multistage Prevention System Using IoT LoRa Technology

Every year many students apply for graduate admission to different universities. To select an applicant, each university has different selection criteria such as GRE score, CGPA, research background, statement of purpose, letter of recommendation, university rating etc. There are some web applications as well as some consultancy services for suggesting the appropriate university based on students’ portfolio. These help to give an idea which universities should be applied for admission. But they have limitations because humans are incapable of considering all the conditions and universities. Moreover, web applications have accuracy problems. In this study, we have proposed a deep neural network (DNN) to predict the chance of getting admitted to a university according to the students portfolio. All the selection criteria are considered here to predict the chance of admission. The DNN model has been compared with existing methods in terms of different performance metrics including mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), R-squared score. It has shown the most promising result that includes R-squared score of 0.8538 and MSE of 0.0031. The proposed method has also outperformed all the existing methods in each benchmark.

Graduate Admission Chance Prediction Using Deep Neural Network

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