LITERATURE REVIEW: PENGENALAN WAJAH MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK

Facial recognition to detect the identity of the gallon user's face in honesty in the school environment has many methods such as local, global, and hybrid approaches. The main problem of using the gallon of honesty is that the program uses the Self-service system, which is a self-service system, where the buyer serves itself unattended. The water charging activity is still found by users who are dishonest, such as taking water but not putting money into the place that has been provided, the thing that should be when the user fills the water then the user must also enter Money into the box provided. Because of the absence of supervision in this program of honesty then it is difficult to know who is dishonest in order to be able to do prevention for the dishonesty that has occurred when using the gallon of honesty program. Facial recognition using the Convolutional Neural Network (CNN) method to classify images. A literature review is used to analyse and focus on techniques in conducting facial recognition on the use of gallons of honesty. Keywords: facial recognition, convolutional neural network methods, a gallon of honest

Robotic Mobile Holder (For CAR Dashboards)

In the current smart tech world, there is an immense need of automating tasks and processes to avoid human intervention, save time and energy. Nowadays, mobile phones have become one of the essential things for human beings either to call someone, connect to the internet, while driving people need mobile phones to receive or make a call, use google maps to know the routes and many more. Normally in cars, mobile holders are placed on the dashboard to hold the mobile and the orientation of the phone needs to be changed according to the driver's convenience manually, but the driver may distract from driving while trying to access mobile phone which may lead to accidents. To solve this problem, an auto adjustable mobile holder is designed in such a way that it rotates according to the movement of the driver and also it can even alert the driver when he feels drowsiness. Image Processing is used to detect the movement of the driver which is then processed using LabVIEW software and NI myRIO hardware. NI Vision development module is used to perform face recognition and servo motors are used to rotate the holder in the required position. Simulation results show that the proposed system has achieved maximum accuracy in detecting faces, drowsiness and finding the position coordinates

Modeling biological face recognition with deep convolutional neural networks

Deep convolutional neural networks (DCNNs) have become the state-of-the-art computational models of biological object recognition. Their remarkable success has helped vision science break new ground and recent efforts have started to transfer this achievement to research on biological face recognition. In this regard, face detection can be investigated by comparing face-selective biological neurons and brain areas to artificial neurons and model layers. Similarly, face identification can be examined by comparing in vivo and in silico multidimensional "face spaces". In this review, we summarize the first studies that use DCNNs to model biological face recognition. On the basis of a broad spectrum of behavioral and computational evidence, we conclude that DCNNs are useful models that closely resemble the general hierarchical organization of face recognition in the ventral visual pathway and the core face network. In two exemplary spotlights, we emphasize the unique scientific contributions of these models. First, studies on face detection in DCNNs indicate that elementary face selectivity emerges automatically through feedforward processing even in the absence of visual experience. Second, studies on face identification in DCNNs suggest that identity-specific experience and generative mechanisms facilitate this particular challenge. Taken together, as this novel modeling approach enables close control of predisposition (i.e., architecture) and experience (i.e., training data), it may be suited to inform long-standing debates on the substrates of biological face recognition.Comment: 41 pages, 2 figures, 1 tabl

Health Assessment of Eucalyptus Trees Using Siamese Network from Google Street and Ground Truth Images

Urban greenery is an essential characteristic of the urban ecosystem, which offers various advantages, such as improved air quality, human health facilities, storm-water run-off control, carbon reduction, and an increase in property values. Therefore, identification and continuous monitoring of the vegetation (trees) is of vital importance for our urban lifestyle. This paper proposes a deep learning-based network, Siamese convolutional neural network (SCNN), combined with a modified brute-force-based line-of-bearing (LOB) algorithm that evaluates the health of Eucalyptus trees as healthy or unhealthy and identifies their geolocation in real time from Google Street View (GSV) and ground truth images. Our dataset represents Eucalyptus trees’ various details from multiple viewpoints, scales and different shapes to texture. The experiments were carried out in the Wyndham city council area in the state of Victoria, Australia. Our approach obtained an average accuracy of 93.2% in identifying healthy and unhealthy trees after training on around 4500 images and testing on 500 images. This study helps in identifying the Eucalyptus tree with health issues or dead trees in an automated way that can facilitate urban green management and assist the local council to make decisions about plantation and improvements in looking after trees. Overall, this study shows that even in a complex background, most healthy and unhealthy Eucalyptus trees can be detected by our deep learning algorithm in real time

SEMINAR NASIONAL INOVASI TEKNOLOGI DAN ILMU KOMPUTER ( 2021 ) TEMA: “Prospek Menjadi Technopreneur Dimasa Pandemi”

Kegiatan Seminar Nasional Inovasi Teknologi dan Ilmu Komputer (SNITIK 2021) merupakan kegiatan yang rutin diadakan Fakultas Teknologi dan Ilmu Komputer, Universitas Prima Indonesia (FTIK UNPRI). Pada awalnya seminar ini dinamakan Semnas FTIK dan dilaksanakan selama 4 tahun, setelah itu namanya diubah menjadi SNITIK dengan ruang lingkup yang lebih luas. Di tahun ketujuh dilaksanakannya Seminar ini, diangkat tema “Prospek Menjadi Technopreneur Dimasa Pandemi.”. Dampak Pandemi Covid-19 sangat mempengaruhi beberapa sektor industri dan usaha global. Selama masa pandemi Covid-19, kebanyakan Customer lebih sering belanja secara online karena dianggap lebih mudah dan praktis. Hal ini yang menunjukkan lapangan usaha sekarang sangat berhubungan erat dengan teknologi. Sehingga perlunya memanfaatkan teknologi dalam mengembangkan model bisnis baru untuk menciptakan peluang usaha. Kondisi ini mendorong industri menggunakan sumber daya manusia lulusan perguruan tinggi yang kompeten dan memiliki jiwa techopreneur