An Automatic Environment Monitoring System Using a MobileNet Transfer Learning

The universities have an important role to encourage and support the Sustainable Development Goals (SDGs). UIN Alauddin Makassar as one of the universities in Indonesia establishes a green campus program to support it. An automatic environment system was built to ensure the cleanliness of the university environment. A comfortable and healthy work environment is expected can improve the productivity and motivation of the academic civities. The system was built using a MobileNet architecture that using a transfer learning approach. It can detect the cleanliness level of the environment which consists of three classes: “Clean”, “Less_Clean”, and “Dirty” in real-time. The dataset used to train the model was obtained by capturing images of the environment around the university.  The best result of the model was achieved by using an Adam optimizer with applying a dropout in the last layer of the network. The total accuracy of the model is about 83%

Research and Development of the Pupil Identification and Warning System using AI-IoT

Currently, pupils being left in the classroom, in the house or in the car is happening a lot, causing unintended incidents. The reason is that parents or caregivers of pupils go through busy and tiring working hours, so they accidentally leave pupils in the car, indoors, or forget to pick up students at school. In this paper, we developed an algorithm to recognize students who use neural networks and warn managers, testing on a model integrated Raspberry Pi 4 kit programmed on Python in combination with cameras, sim modules, and actuators to detect and alert abandoned pupils to the manager to take timely remedial measures and avoid unfortunate circumstances. With the ability to manage students, the system collects and processes images and data on student information for artificial intelligence (AI) systems to recognize when operating. The system of executive structures serves to warn when students are left in the car, in the classroom, or in the house to avoid unintended incidents or safety risks

An Efficient Bar Code Image Recognition Algorithm for Sorting System

Copyright © The Authors. In the sorting system of the production line, the object movement, fixed angle of view, light intensity and other reasons lead to obscure blurred images. It results in bar code recognition rate being low and real time being poor. Aiming at the above problems, a progressive bar code compressed recognition algorithm is proposed. First, assuming that the source image is not tilted, use the direct recognition method to quickly identify the compressed source image. Failure indicates that the compression ratio is improper or the image is skewed. Then, the source image is enhanced to identify the source image directly. Finally, the inclination of the compressed image is detected by the barcode region recognition method and the source image is corrected to locate the barcode information in the barcode region recognition image. The results of multitype image experiments show that the proposed method is improved by 5+ times computational efficiency compared with the former methods, and can recognize fuzzy images better.Scientific Research Starting Project of SWPU [Zheng, D., No. 0202002131604]; Major Science and Technology Project of Sichuan Province [Zheng, D., No. 8ZDZX0143]; Ministry of Education Collaborative Education Project of China [Zheng, D., No. 952]; Fundamental Research Project [Zheng, D., Nos. 549, 550]

Impact of Image Preprocessing Methods and Deep Learning Models for Classifying Histopathological Breast Cancer Images

Early diagnosis of cancer is very important as it significantly increases the chances of appropriate treatment and survival. To this end, Deep Learning models are increasingly used in the classification and segmentation of histopathological images, as they obtain high accuracy index and can help specialists. In most cases, images need to be preprocessed for these models to work correctly. In this paper, a comparative study of different preprocessing methods and deep learning models for a set of breast cancer images is presented. For this purpose, the statistical test ANOVA with data obtained from the performance of five different deep learning models is analyzed. An important conclusion from this test can be obtained; from the point of view of the accuracy of the system, the main repercussion is the deep learning models used, however, the filter used for the preprocessing of the image, has no statistical significance for the behavior of the system.Spanish Government PID2021-128317OB-I00Government of Andalusia P20-0016

Deep neuro‐fuzzy approach for risk and severity prediction using recommendation systems in connected health care

Internet of Things (IoT) and Data science have revolutionized the entire technological landscape across the globe. Because of it, the health care ecosystems are adopting the cutting‐edge technologies to provide assistive and personalized care to the patients. But, this vision is incomplete without the adoption of data‐focused mechanisms (like machine learning, big data analytics) that can act as enablers to provide early detection and treatment of patients even without admission in the hospitals. Recently, there has been an increasing trend of providing assistive recommendation and timely alerts regarding the severity of the disease to the patients. Even, remote monitoring of the present day health situation of the patient is possible these days though the analysis of the data generated using IoT devices by doctors. Motivated from these facts, we design a health care recommendation system that provides a multilevel decision‐making related to the risk and severity of the patient diseases. The proposed systems use an all‐disease classification mechanism based on convolutional neural networks to segregate different diseases on the basis of the vital parameters of a patient. After classification, a fuzzy inference system is used to compute the risk levels for the patients. In the last step, based on the information provided by the risk analysis, the patients are provided with the potential recommendation about the severity staging of the associated diseases for timely and suitable treatment. The proposed work has been evaluated using different datasets related to the diseases and the outcomes seem to be promising

Image-based deep learning reveals the responses of human motor neurons to stress and VCP-related ALS

OBJECTIVES: Although morphological attributes of cells and their substructures are recognized readouts of physiological or pathophysiological states, these have been relatively understudied in amyotrophic lateral sclerosis (ALS) research. MATERIALS AND METHODS: In this study, we integrate multichannel fluorescence high-content microscopy data with deep-learning imaging methods to reveal - directly from unsegmented images - novel neurite-associated morphological perturbations associated with (ALS-causing) VCP-mutant human motor neurons (MNs). RESULTS: Surprisingly, we reveal that previously unrecognized disease-relevant information is withheld in broadly used and often considered 'generic' biological markers of nuclei (DAPI) and neurons (β III-tubulin). Additionally, we identify changes within the information content of ALS-related RNA binding protein (RBP) immunofluorescence imaging that is captured in VCP-mutant MN cultures. Furthermore, by analysing MN cultures exposed to different extrinsic stressors, we show that heat stress recapitulates key aspects of ALS. CONCLUSIONS: Our study therefore reveals disease-relevant information contained in a range of both generic and more specific fluorescent markers, and establishes the use of image-based deep learning methods for rapid, automated and unbiased identification of biological hypotheses

A Mobile Palmprint Authentication System Using a Modified MNT Algorithm, Circular Local Binary Pattern, and CNN (mobileNet)

A few approaches have been proposed for hand segmentation in palmprint recognition. Skin-color information does not process sufficient information for discrimination in complex backgrounds and variable illumination. The use of guides has also been proposed, which restricts hand placement during capturing. Contour tracing algorithms have also been proposed in the literature. This worked in an even background scenario with no objects or patterns around the hand. In the case of uneven background with objects present, the traditional contour tracing algorithm cannot accurately segment the hand from the background. Hence, this paper proposes a modified Moore Neighbor Tracing (MNT) algorithm for hand detection and key-point extraction in complex backgrounds. The hand image is converted to grey, and the edges in the hand image are detected. The modified algorithm then transverses selected edges and returns the peak and valleys of each finger. This is then used to crop the palm. The modified algorithm improves the accuracy of hand detection in complex backgrounds with an F-Score of 0.8657. A mobile palmprint biometric system was also presented using Circular Local Binary Pattern (CLBP) and Convolutional Neural Network (CNN). The system showed an accuracy of 98.3% for hands captured with the mobile device and the CASIA online database. An accuracy of 99.0% was also recorded for GPDS and PolyU online databases

An approach for classification of Alzheimer’s disease using deep neural network and brain magnetic resonance imaging (MRI)

Alzheimer’s disease (AD) is a deadly cognitive condition in which people develop severe dementia symptoms. Neurologists commonly use a series of physical and mental tests to diagnose AD that may not always be effective. Damage to brain cells is the most significant physical change in AD. Proper analysis of brain images may assist in the identification of crucial bio-markers for the disease. Because the development of brain cells is so intricate, traditional image processing algorithms sometimes fail to perceive important bio-markers. The deep neural network (DNN) is a machine learning technique that helps specialists in making appropriate decisions. In this work, we used brain magnetic resonance scans to implement some commonly used DNN models for AD classification. According to the classification results, where the average of multiple metrics is observed, which includes accuracy, precision, recall, and an F1 score, it is found that the DenseNet-121 model achieved the best performance (86.55%). Since DenseNet-121 is a computationally expensive model, we proposed a hybrid technique incorporating LeNet and AlexNet that is light weight and also capable of outperforming DenseNet. To extract important features, we replaced the traditional convolution Layers with three parallel small filters (1 × 1, 3 × 3, and 5 × 5). The model functions effectively, with an overall performance rate of 93.58%. Mathematically, it is observed that the proposed model generates significantly fewer convolutional parameters, resulting in a lightweight model that is computationally effective.Web of Science123art. no. 67

ReSTiNet : On improving the performance of Tiny-YOLO-Based CNN architecture for applications in human detection

Human detection is a special application of object recognition and is considered one of the greatest challenges in computer vision. It is the starting point of a number of applications, including public safety and security surveillance around the world. Human detection technologies have advanced significantly in recent years due to the rapid development of deep learning techniques. Despite recent advances, we still need to adopt the best network-design practices that enable compact sizes, deep designs, and fast training times while maintaining high accuracies. In this article, we propose ReSTiNet, a novel compressed convolutional neural network that addresses the issues of size, detection speed, and accuracy. Following SqueezeNet, ReSTiNet adopts the fire modules by examining the number of fire modules and their placement within the model to reduce the number of parameters and thus the model size. The residual connections within the fire modules in ReSTiNet are interpolated and finely constructed to improve feature propagation and ensure the largest possible information flow in the model, with the goal of further improving the proposed ReSTiNet in terms of detection speed and accuracy. The proposed algorithm downsizes the previously popular Tiny-YOLO model and improves the following features: (1) faster detection speed; (2) compact model size; (3) solving the overfitting problems; and (4) superior performance than other lightweight models such as MobileNet and SqueezeNet in terms of mAP. The proposed model was trained and tested using MS COCO and Pascal VOC datasets. The resulting ReSTiNet model is 10.7 MB in size (almost five times smaller than Tiny-YOLO), but it achieves an mAP of 63.74% on PASCAL VOC and 27.3% on MS COCO datasets using Tesla k80 GPU