The trend of wearing masks in public is growing in recent years all
            over the world due to COVID-19 epidemic. In particular, public
            security check systems can not effectively recognize the masked
            faces, but removing masks for passing authentication will increase
            the risk of virus infection. We break the problem into two stages:
            mask object detection and image completion of the removed mask
            region. The results in this project suggest that Face Mask Detection
            and cGAN are a promising approach for many Masked Face-Unmasked Face
            translation tasks, especially those involving security applications.
            Future work includes, adding accuracy metrics, and implement the
            system for an embedded system.
			

            In this project work, we proposed and designed the reliable
            self-healing fiber Bragg grating (FBG) sensor network for improving
            FBG sensor capacity in the network based on intensity and wavelength
            division multiplexing (IWDM), and effectively detected the central
            Bragg wavelength of five FBGs using gate recurrent unit (GRU)
            techniques. In this work, we conducted the experiment strain applied
            to FBG1 sensor, while the other four FBGs keep fixed. During strain
            applied to FBG1, the Bragg wavelength of FBG1 shifts to long
            wavelength. In this situation, the spectra of five FBGs are found
            either non-overlapped or partially overlapped or completely
            overlapped. However, try to identify the Bragg wavelength of each
            FBG from partially or completely overlapped spectra using the
            traditional peak detection (CPD) method is very challenging and may
            not be accurate. To solve this limitation of CPD techniques, we
            proposed the GRU algorithm to accurately identify the Bragg
            wavelength of FBGs in the condition of the partially or fully
            overlapped spectra. The Bragg wavelength detection result shows that
            our proposed GRU model have a good performance in terms of accuracy
            and computational time to detect accurate Bragg wavelength of each
            FBG compare to LSTM and MLP models even the spectra of three FBGs
            are overlapped.
          

            Brief Introduction of Mask RCNN and model application
          

            Recurrent Neural Network is natural termed for the great
            process in the Natural language processing task. However, in most
            previous works, the models are learned based on single-task
            supervised objective, which often suffer from insufficient training
            data. In this project we trained our model so efficient and
            compared with six different models and trained with the high
            accuracy model on our own dataset. Experiments on five benchmark
            text generator, for instance if you want an articles in high grammar
            articles and highly fluent you can use this method to produces
            that by giving a name of country it will provide you the historical
            moments of that country. The entire network is trained jointly
            on this entire task. Furthermore, DL approaches that have been
            explored and evaluated in different application domains in NLP
            are included in this survey.
          

            Conditional GAN is a variant type of GAN, which is able to decide
            type of generated image, to produce the right image we want. We
            proposed a number generator model, and using CNN-trained model to
            classify generated images, to evaluate our CGAN performance.
          

            According to statistics from the Taiwan’s CDC, the top ten causes of
            death among citizens from 2018 to 2020 are also cancer. The fourth
            leading cause of female death is breast cancer. The order is right
            behind to lung cancer, liver cancer, and rectal cancer. They are all
            caused by various environmental factors and lifestyle habits in
            recent years. Mammography has been regarded by medical experts at
            internal and external as a non-invasive detection method for
            detecting breast cancer in early stage and effectively reduce the
            mortality rate. So, we decided to combine mammography with deep
            learning as the topic for improving accuracy of detection. Expect to
            reduce the proportion of deaths which caused by breast cancer.
          

            DeepFaceLab is a software to change someone’s face in the image or
            video, we use it to change the face of video for the final result.
          

            There are several different types of traffic signs like speed
            limits, no entry, traffic signals, turn left or right, children
            crossing, no passing of heavy vehicles, etc. Traffic signs
            classification is the process of identifying which class a traffic
            sign belongs to.
			
			In this Python project example, we will
            build a deep neural network model that can classify traffic signs
            present in the image into different categories. With this model, we
            are able to read and understand traffic signs which are a very
            important task for all autonomous vehicles.
          

            Sentiment Analysis (SA) is a progressive research area in the
            process of text mining. SA or the Opinion Mining (OM) is the process
            of examining people’s opinions, emotions and attitudes towards the
            entity. The entity represents the events, inpreiduals, and topics.
            This paper focuses to design a novel classifier approach for the
            sentimental classification based on the amazon cell phone reviews.
            Initially, the input cell phone review is pre-processed by employing
            the stop word removal, and stemming technique. In the feature
            extraction phase, the all-caps, emoticon, numerical words, hash
            tags, sentiwordnet, elongated words, punctuation marks, and negation
            features are extracted in an efficient way from the pre-processed
            output. Moreover, the sentimental classification performance is
            carried out using the developed Deep Residual Network classifier
            approach. Meanwhile, the efficiency of the developed Deep Residual
            Network technique is computed based on the performance metrics, such
            as accuracy, sensitivity, and specificity. Besides, the proposed
            Deep Residual Network approach obtained efficient classification
            performance with respect to the maximum accuracy of 0.953, higher
            sensitivity of 0.944, and maximal specificity of 0.944,
            respectively.
          

            We use the CNN function of Matlab to build a model to classify the
            image of our member's cat
          

            The development of Prognostics and Health Management (PHM)
            technology has allowed humans to move from passively solving the
            failures of machinery and equipment to proactively predicting when
            abnormalities and failures will occur. This technology not only
            allows machinery and equipment to operate more reliably, At the same
            time, losses are reduced. This research mainly analyzes the characteristics 
			of the fuse heating system to determine whether the
            process is normal or abnormal, and further explores the detailed
            classification of abnormal processes, uses random forest to perform
            feature selection to reduce the amount of noise, then constructs
            with 1D-CNN model. The model is constructed with two sampling
            methods (random sampling, stratified sampling) and finally evaluated
            by 4 indicators in the confusion matrix, which are accuracy,
            precision, recall rate, and F1 score. The result of the study is
            that stratified sampling is better than random sampling in all four
            indicators of the confusion matrix.
          

            This is our final project - GitHub Project Analysis, which talking
            about CAPTCHA break.
          

            Image classification involves assigning a class label to an image,
            whereas object localization involves drawing a bounding box around
            one or more objects in an image. Object detection is more
            challenging and combines these two tasks and draws a bounding box
            around each object of interest in the image and assigns them a class
            label.
          

            Our project use the latest YOLOv4-tiny as our real time object
            detection method and achieved mask recognition
          

            Using bidirectional LSTM to train a binary classification model that
            can tell if a text contains AD or not. predict model deployment +
            Email warning system and parsing tweets from twitter developer api
            are build with Flask.
          

            face mask recognition
          

            Pump Sensor Data for Predictive Maintenance
          

            Our project is to recognize the speaker of audios. We select MFCCs
            to extract the features and use MLP, LSTM, and CNN techniques to
            train the model.
          

            Generating music has a few notable differences from generating
            images and videos. First, music is an art of time, necessitating a
            temporal model. Second, music is usually composed of multiple
            instruments/tracks with their own temporal dynamics, but
            collectively they unfold over time interdependently. Lastly, musical
            notes are often grouped into chords, arpeggios or melodies in
            polyphonic music, and thereby introducing a chronological ordering
            of notes is not naturally suitable. this project focuses on building
            a GAN Model for Music Generation from Lakh Pypiano Roll dataset
            using specifically DCGAN to explore the use of DCGAN instead of
            conventional images and text dataset problems.
          

            Recognize Handwritten Numbers