Reviewerly/exHarmony huggingface dataset:
Image preprocessing and hyperparameter optimization on pretrained model MobileNetV2 in white blood cell image classification

White blood cells play a role in maintaining the immune system which consists of several types such as neutrophils, lymphocytes, monocytes, eosinophils and basophils. MobileNetV2 is one of the pretrained convolutional neural network (CNN) models that provides excellent advantages and performance in classifying images. In this research was conducted to find out how to apply optimization hyperparameters and the impact of image processing on white blood cell image classification using MobileNetV2, so that it is expected to find a combination of preprocessing and combination of hyperparameter values that can produce the highest accuracy value. To maximize the classification process, before classifying the image, several stages of image preprocessing are carried out, namely cropping, grayscale, resizing and augmentation. Hyperparameter tuning was carried out for an experiment to improve model performance. The three main parameters used in hyperparameter tuning are learning rate, batch size, and number of epochs. Performance optimization model performance will be measured using accuracy, sensitivity, specificity and using a confusion matrix. Based on the experimental results in this study, it shows that the best learning rate value is 0.00001, the best batch size value is 32, and the best epoch value is 250.
Silhouette vanished contour discovery of aerial view images by exploiting pixel divergence

&lt;span lang="EN-US"&gt;An Image's Edge Detection is the process of finding and pinpointing sharp discontinuities in an image. Detecting the edges of an image significantly reduces the quantity of data and removes unnecessary information while keeping the fundamental structural aspects of an image. Edge detection is essential when it comes to image categorization in computer vision and object identification. The primary goal of this research is to investigate several strategies for edge detection and shadow of objects in Aerial view Images. Machine vision, face detection, medical imaging, and object detection are just a few examples of applications where image segmentation has been widely utilized. Image segmentation is categorizing or identifying sub-patterns in a given image. Many algorithms and strategies for picture segmentation have been presented to improve segmentation issues in a given application area. Techniques such as threshold-based and region-based picture segmentation were used in this study. An edge detection method such as Sobel, Prewitt and Roberts and the canny approach is applied to the benchmark image and compared with the proposed Octagonal Pixel Divergence Edge Detection (ODED) algorithm. MATLAB R2018b was used for this research, and findings show that the proposed approach is more effective than the other methods, with a quality image with edges. &lt;/span&gt;
Emotions and gesture recognition using affective computing assessment with deep learning

Emotions have an important role in education. Affective development, attitudes, and emotions in learning are measured using affective assessment. This method is the right way to determine the student’s affective development. However, the process did not run optimally because the teacher found it difficult to collect student’s affective data. This paper describes the development of a system that can assist teachers in carrying out affective assessment. The system was developed using a v-model that aligns the verification phase with the validation. The use of the system is carried out during learning activities. The emotion detection system detects through body gestures using PoseNet to generate emotional data for each student. The detection results are then processed and displayed on an information system in the form of a website for affective assessment. The accuracy of emotion detection got validation values of 84.4% and 80.95% after being tested at school. In addition, the acceptance test with the usability aspect of the system by the teacher got a score of 77.56% and a score of 79.85% by the students. Based on several tests carried out, this developed system can assist the process of implementing affective assessment.
Online panel data quality: a sentiment analysis based on a deep learning approach

The rise of online access panels has profoundly changed the market research landscape. Often presented by their owners as very powerful tools, they nevertheless raise important scientific questions, particularly regarding the representativeness of the samples they produce and, consequently, the validity of the information they provide. In this paper, we present an innovative approach, based on deep learning and sentiment analysis techniques, to assess in real time the representativeness of an online panel sample. The idea is to measure the extent to which the opinions of an online panel converge with opinions on social networks. To validate the proposed method, we conducted a case study on the emerging discussion on Coronavirus disease (COVID-19) vaccination. The results not only proved the representativeness of online panel sample, but also demonstrated the feasibility and effectiveness of our approach.
Store product classification using convolutional neural network

Stores sells consumer goods, mainly food products and other household products at retail. The products sold in stores vary greatly, in order to be time efficient in the fast-paced era and the current technological era requires artificial intelligence technology. In the artificial intelligence branch, there is a specific or detailed learning process known as deep learning. One of the branches of deep learning is the convolutional neural network(CNN). This research intends to employ a CNN architecture to facilitate and streamline the time and cost of the store’s product sorting process. The test is conducted with 1,050 product images divided into 35 labels and divided into three data, namely 80% data training 10% data validation and 10% data test. The image used is preprocessed with a size of 256×256 pixels. The data was trained with six convolution layers and an epoch of 50 with an execution time of 33 minutes so as to achieve an accuracy of 91.37%.
Face mask detection and counting using you only look once algorithm with Jetson Nano and NVDIA giga texel shader extreme

Deep learning and machine learning are becoming more extensively adopted artificial intelligence techniques for machine vision problems in everyday life, giving rise to new capabilities in every sector of technology. It has a wide range of applications, ranging from autonomous driving to medical and health monitoring. For image detection, the best reported approach is the you only look once (YOLO) algorithm, which is the faster and more accurate version of the convolutional neural network (CNN) algorithm. In the healthcare domain, YOLO can be applied for checking the face mask wearing of the people, especially in a public area or before entering any closed space such as a building to avoid the spread of the air-borne disease such as COVID-19. The main challenges are the image datasets, which are unstructured and may grow large, affecting the accuracy and speed of the detection. Secondly is the portability of the detection devices, which are generally dependent on the more portable like NVDIA Jetson Nano or from the existing computer/laptop. Using the low-power NVDIA Jetson Nano system as well as NVDIA giga texel shader extreme (GTX), this paper aims to design and implement real-time face mask wearing detection using the pre-trained dataset as well as the real-time data.
Image captioning to aid blind and visually impaired outdoor navigation

Artificial intelligence technology has dramatically improved the quality of services for human needs, one of which is technology to improve the quality of services for the blind and visually impaired, particularly technology that can help them understand visual sights to facilitate navigation in their daily lives. This study developed an image captioning model to aid the blind and visually impaired in outdoor navigation. The image captioning model employs the encoder-decoder method, with the convolutional neural network (CNN) feature extraction and attention layer as encoders and the long short-term memory (LSTM) as decoders. ResNet101 and ResNet152 are used in the encoder to extract image features. The results of the extraction and caption are forwarded to the attention layer and the LSTM network. The attention layer uses the Bahdanau attention mechanism. The accuracy of the model is calculated using the bilingual evaluation understudy score (BLEU), metric for evaluation of translation with explicit ordering (METEOR) and recall-oriented understudy for gisting evaluation-longest common subsequence (ROUGE-L). ResNet101 performed the best on BLEU-4, scoring 91.811% and 94.0337% in the METEOR evaluation. The captioning results show that the model is quite successful in displaying a simple caption that is suitable for each image.
Off-line handwritten signature recognition based on genetic algorithm and euclidean distance

Biometric authentication is a technology that has become significant in the high level of personal identity security. This paper provides a signature recognition system. This paper provides a static signature recognition system (SSRS). We have classified the signature in two ways. The first method uses the genetic algorithm (GA), considering that the signature is the chromosome with 35 genes, and each feature is a gene. With applying the processes of the GA between chromosomes and the formation of generations in sequence until we reach the optimal solution by finding the chromosome closest to the chromosome that enters the system. In the second method, we have classified the signature by calculating the Euclidean Distance between the query signature and the signatures stored in the database. The signature closest to a confirmed threshold is considered the desired goal. The database uses 25 handwritten signatures (15 signatures for training and five original signatures, and five fake signatures written by other people for testing), so we have a database of 500 signatures. With a 94% discrimination rate, the genetic recognition system (GRS) was able to access the solutions, and with a (91% rate) the euclidean recognition system (ERS) was done. The application uses MATLAB.
Vehicle detection system based on shape, color, and time-motion

&lt;span lang="EN-US"&gt;Vehicle detection application can assist in-vehicle surveillance functions and have implications for various fields. A vehicle can be identified through the license number attached to its license plate, the color and its shape. Vehicle detection can make use of multimedia sensors so that the design and detection performances can be optimal. Sensor performances are influenced by factors such as the number of multimedia sensors, sensor placement, sensor positioning, and schemes in case of system failure. This study makes use of multimedia sensors with cameras equipped by a phase detection auto focus (PDAF) technology which is like a pair of eyes to see an object. This study analyses 134 vehicles with number detection and various colors to see the effect on the detection and recognition processes. The cars were passed through the camera 10 times at a speed of 10-15 km/hour with various camera distances and positions. Various values and depths of the images were generated. The farther the distance the higher the disparity values. For maximum distance of 50 m, disparity is 6.20×106 and image depth is 16.88×109. Vehicle color influences detection with orange has the best accuracy, but the gray has the largest path error value.&lt;/span&gt;
The evaluation of convolutional neural network and genetic algorithm performance based on the number of hyperparameters for english handwritten recognition

&lt;span&gt;Convolutional neural network (CNN) has been widely applied to image recognition, especially Handwritten English Recognition. CNN's performance is good if the hyperparameter values are correct. However, the determination of precise hyperparameters is not a trivial task. This task is made more difficult when combined with a larger number of hyperparameters resulting in a high dimensionality of the search space. Usually, hyperparameter optimization uses a finite number. Previous studies have shown that a large number of hyperparameters can result in optimal CNN performance. However, the studies only apply to text mining datasets. This study offers two novelties. First, it applied 20 hyperparameters and their ranges to handwritten English. Second, this paper conducted seven experiments based on different hyperparameters and the number of hyperparameters. This paper also compares the existing methods, namely random and grid search. The experiment resulted in the proposed model being superior to the existing methods. EX3 is better than other experiments and a larger number of hyperparameters and layer-specific hyperparameter values are unimportant.&lt;/span&gt;
Lexicon-based sentiment analysis for Kannada-English code-switch text

Sentiment analysis is the process of computationally recognizing and classifying the attitudes conveyed in each text towards a particular topic, product, etc. which is either positive or negative. Sentiment analysis is one of the interesting applications of natural language processing (NLP) and which is used to analyze the social media. Text in social media is casual and it can be written either in code-switch or monolingual text. Several researchers have implemented sentiment analysis on monolingual text, though sentiments can be expressed in code-switch text. Sentiment analysis can be applied through deep learning (DL), machine learning (ML), or a Lexicon-based approach. Machine learning (ML) and deep learning (DL) methods are time-consuming, computationally expensive, and need training data for analysis. Lexicon-based method does not require training data and requires less time to find the sentiments in comparison with ML and DL. In this paper, we propose the Lexicon-based approach (NBLex) to analyze the sentiments expressed in Kannada-English code-switch text. This is the first effort that targets to perform sentiment analysis in Kannada-English code-switch text using the Lexicon-based approach. The proposed approach performed with better accuracy of 83.2% and 83% of F1-score.
Fault tolerant control of two tanks system using gain-scheduled type-2 fuzzy sliding mode controller

To save the robustness of type 2 fuzzy logic control technique and to avoid the high energy consumption that represents the sliding mode control (SMC) technique control technique, without failing the performance of the system, we propose a new fault tolerant control method based on gain-scheduled sliding mode control with interval type 2 fuzzy logic (FTCGST2FSMC) applied to the hydraulic system (two tanks system) with an actuator fault. The proposed control scheme avoids a difficult modeling, due to the chatter effect of the SMC, guarantees the stability studied by Lyapunov with the robustness of the system. The gains of the control with the SMC controller are modified and changed by an adaptation with a technique based on type 2 fuzzy logic, used to improve the gains of the controller when the fault is added, the proposed FTCGST2FSMC controller has been compared with the sliding mode controller. The results obtained confirm the robustness and the performance of this method, in the presence of the actuator fault effect.
Text detection and recognition through deep learning-based fusion neural network

&lt;p&gt;Text recognition task involves recognizing the text from the natural image; it possesses various application, which aids information extraction through data mining from street view like images. Scene text recognition involves two stages i.e., text detection and text recognition, in the past several mechanisms has been proposed for accurate identification, these mechanisms are either traditional approach or deep learning-based. All the existing deep-learning methodology fails as this comprises character data and image data, further this research develops an optimal architecture fusion neural network (FNN) for text identification and recognition. FNN comprises several layers of convolutional neural network as well as recurrent neural network. Within FNN architecture convolutional layer is utilized for the feature extraction and recurrent layer is utilized for attaining the feature classification prediction. Further, an optimal training architecture is established for the enhancement of classification accuracy. Here Devanagari MLT-19 dataset is utilized for the evaluation of FNN. Three different parameters are considered during evaluation i.e., script word identification, character recognition rate (CRR) and word recognition rate (WRR). Further comparison with existing models is performed to establish the proposed model efficiency and it shows FNN methodology observes 98.67% of script identification accuracy, 84.65% of WRR and 92.93% of CRR.&lt;/p&gt;
The Impact of Quantum Computing on Cryptography

Abstract: The purpose of this paper's abstract is to explain how quantum computing works in terms of current cryptography and to provide the reader a rudimentary understanding of post-quantum algorithms. Community key encoding methods affected, symmetric structures affected, the influence on hash purposes, upright quantum cryptography, distinctions amongst quantum and standard computing, obstacles in quantum computation, and quantum procedures (Shor's and Grover's). The PostQuantum Cryptography section specifically discusses various mathematically based quantum crucial circulation techniques, lattice-built cryptography, multivariate-built cryptography, hash-based signs, and code-based encoding. One of the modern technologies in today's society is quantum computation. The advance of quantum computing applications is the focus of numerous communities and research institutions worldwide. Another developing field at the moment that is becoming stable is artificial intelligence. The major goal of this work is to determine the effects of the development of quantum computing research on applications involving artificial intelligence. Hence, computational methods are utilised in the study's methodology. So that this study's findings about the expanding impact of quantum computing research for a particular application of artificial intelligence can be drawn. The impact and potential of quantum computing on the subject of artificial intelligence is also discussed in this study, along with how quantum computing affects that discipline.
A Comprehensive Overview on Intelligent Spam Email Detection

Abstract: Spam, usually referred to as unsolicited commercial or bulk e-mail, has recently become a major issue on the internet. Time, storage, and transmission bandwidth are all wasted by spam. Spam email has been a growing issue for years. Nowadays, automatic email filtering appears to be the most successful strategy for preventing spam. Only several years ago most of the spam could be reliably dealt with by blocking e-mails coming from certain addresses or filtering out messages with certain subject lines. Spammers started employing a number of cunning strategies to get beyond filtering techniques, such as utilizing random sender addresses and/or adding random characters to the message subject line's beginning or conclusion. Machine learning techniques now a days are used to automatically filter the spam e-mail in a very successful rate. Machine learning field is a subfield from the broad field of artificial intelligence, this aims to make machines able to learn like human. Understanding, observing, and providing knowledge about a statistical occurrence are all terms used here. In the first place, data collection and representation are typically problem-specific (i.e., for email messages), and in the second place, e-mail feature selection and feature reduction aim to lower the dimensionality (i.e. the number of features).Finally, the e-mail classification phase of the process finds the actual mapping between training set and testing set. Machine Learning approach includes lots of algorithms that can be used in e-mail filtering like Naïve Bayes, K-nearest neighbour, Support VSector Machine, classifiers. In conclusion, we try to summarize the performance results of the few machine learning methods in terms of spam precision and accuracy.
Online Voting System Using Face Recognition and OTP(One-Time Password)

Abstract: The fundamental idea behind this system is to provide an online voting system that will aid in reducing fraud in manual voting systems and earlier iterations of online voting that used a webcam for face recognition and OTP generating. Also, we are introducing a location-free voting mechanism for voters who are unable to visit the polling location (hometown). Here, we suggest a method that uses various layers of authentication, starting with face recognition and continuing with OTP verification using validation data, to guarantee the device's dependability. Only after being identified and verified against the provided database of registered voters may each voter access the system. When the information provided and the corresponding face match, the voter will be permitted to move through with selecting their top candidate from the panel.
Text Guided Image Using Machine Learning

bstract: Given a corrupted image, image inpainting aims to complete the image and outputs a plausible result. When we complete the missing region, we always borrow the information from a known area, which is aimless and causes unsatisfactory results. In our daily life, some other information is often used for corrupted images, such as text descriptions. Therefore, we introduce the use of text information to guide image inpainting. To fullfill this task, We introduce an inpainting model named TG Net (Text-Guided Inpainting Network). We provide a text-image gated feature fusion module to fuse text feature and image feature deeply. A mask attention module is proposed to enhance the consistency of known areas and the repaired area. Extensive quantitative and qualitative experiments on three public datasets with captions demonstrate the effectiveness of our method. The goal of our paper is to semantically edit parts of an image matching a given text that describes desired attributes (e.g., texture, colour, and background), while preserving other contents that are irrelevant to the text. To achieve this, we propose a novel generative adversarial network (Mani GAN), which contains two key components: text-image affine combination module (ACM) and detail correction module (DCM). The ACM selects image regions relevant to the given text and then correlates the regions with corresponding semantic words for effective manipulation. Meanwhile, it encodes original image features to help reconstruct text-irrelevant contents. The DCM rectifies mismatched attributes and completes missing contents of the synthetic image. Finally, we suggest a new metric for evaluating image manipulation results, in terms of both the generation of new attributes and the reconstruction of text-irrelevant contents. Extensive experiments on the CUB and COCO datasets demonstrate the superior performance of the proposed method. Code is available at this https URL.