Miracle-dz/UniFAR-FTUs huggingface dataset:
DelNet based Systolic Peak Delineation in Remote PPG Signal from Facial Video Frames

Remote photoplethysmography (rPPG) technology has gained popularity in a short span of time due to its potential applications in cardiac health assessment. The existing studies have been accomplished under certain environmental constraints and focuses only on accurate heart rate (HR) estimation. The aim of this research work is to utilise a deep neural network (DNN) for the estimation of a reliable rPPG waveform with correct systolic peak localization. We propose a delineating network (DelNet) that is designed without any constraints. The rPPG waveform estimated from the network may be used to measure other cardiac parameters along with the HR value. The method is tested and validated on publicly available UBFC-rPPG dataset and also, compared with state-of-the-art approaches. The method is evaluated by comparing the derived HR and HRV parameters from the estimated rPPG waveform with the corresponding parameters measured from the ground truth PPG signal. For averaged HR, mean absolute error 1.132, root mean square error 1.448, standard deviation of error 0.927, and correlation coefficient 0.994 are found. Similarly, the resulted parameters of HRV analysis on estimated rPPG and PPG exhibit insignificant error values. The performance results show the effectiveness of the proposed scheme and its capability for cardiac healthcare system.
Classification of Ocular Diseases using Retinal Fundus Images by Leveraging Advance Transfer Learning and Deep Learning Techniques

The assistance of computer aided diagnostics will balance the insufficient number of ophthalmologists burdened with an increasing number of patients suffering from Ocular disease (OD). Multiple disease prediction offers the potential to address this problem. Therefore, we propose a multi-class classification model to predict Cataract (C), Glaucoma (G) and Diabetic Retinopathy (DR) using Retinal Fundus Images (RFI) and leveraging Artificial Intelligence (AI) and Deep Learning (DL) techniques. The proposed model consists of eight convolution layers, five max-pool layers, and an average-pool layer. LeakyReLu activation function was used to improve results, and dropouts were used to regulate/handle overfitting. The model’s performance was evaluated using Accuracy (A), Precision (P), Sensitivity (S) and F1-score (F1). Comparison of our model was done with state-of-the-art pre-trained network models, such as VGG16, ResNet50, EfficientNetB0, and DenseNet161. The results of our proposed model outperforms the existing pre-trained models.
Rehabilitative use of fNIRS controlled lower limb exoskeleton

One of the most prevalent roots of disability around the globe is stroke. The majority of patients are supposed to depend on family members and caretakers for their remaining lives due to partial or complete paralysis. Various forms of assistive technology have been offered for post-stroke rehabilitation to provide improvements to the impacted body limbs of the disabled. The users in many of these systems have little control over the duties and receive no input on the state of the brain controlled lower limb exoskeleton (BCLLE). Furthermore, there is no system in place to monitor body motions. The suggested system addresses problems in which the exoskeleton is controlled by human intentions. BCLLE has a versatile design that may be tailored as per the needs of the handicap. The exoskeleton is modelled after human anatomy to fit the patient’s body. The exoskeleton is controlled by brain signals using functional Near Infrared Spectroscopy (fNIRS). With our suggested method, classification accuracy of more than 70% is achieved, which is significantly greater than all currently available options.
A Heuristic Approach to Identify Influential Node in Complex Social Networks

Multiple features of the complex network are exhibited among the nodes in time-varying social networks. Community detection has numerous approaches, but the majority of methods are restricted to disjoint community situations. Solutions to disjoint community situations do not reflect well on real-world social networks. An innovative heuristic method has been proposed to recognize community structure in real-world networks and to identify the most influential node within each community. The proposed concept on a recognized community network has been tested in order to validate it. This work introduced one new network metric, the Dependency factor which computes the influence of a node. Our results are compared with other existing approaches using real-life datasets. We have compared the dependency factor with other network centralities. The computational case study is done over a well-known Bengali novel, namely Chaar Adhyay written by Nobel laureate Rabindranath Tagore. The analytical results are more accurate than the state-of-the-art approaches.
Tomato Leaf Disease Detection using Enhanced Weight Assignment Based Loss Calculation Method

Plant diseases pose a significant threat to global food production and are a major challenge for crop management. Deep learning techniques have demonstrated remarkable success in plant disease detection, but most vision-based approaches rely on datasets captured in lab environments, limiting their applicability to real field images. The lack of large-scale non-lab datasets remains a significant challenge in enabling vision-based plant disease detection. This work focuses on leveraging sufficiently available in-lab datasets to enhance the effectiveness of plant disease recognition in real-world scenarios. This work proposes a region-reweighting-based approach that utilizes in-lab images for model training and in-field images for testing. The images are first partitioned into patches, and patch features are extracted using a pre-trained convolutional neural network (CNN). The clustering of these patches is performed to assign weights based on their similarity with other disease-containing patches. These assigned weights are considered during model training on the patches. Using this approach an accuracy of up to 92% is achieved on the disease class whose accuracy otherwise resulted in less than 90% on the state-of-the-art models. The work in this paper improves the identification accuracy of an individual disease class at the same maintaining overall accuracy as well.
Slang language Detection and Identification In Text

The prevalence of internet slang in modern communication channels, such as the internet and mobile services, has led to the emergence of new linguistic phenomena. While some of these terms are previously registered words with new applications, others fall into the category of out-of-vocabulary slang. Dismissing internet slang and replacing it with unfamiliar phrases is no longer viable, as these terms carry significant connotations in social media writings. This research paper focuses on the detection and identification of slang words using a range of techniques. The study aims to improve the understanding of internet slang and enable accurate interpretation of social media texts. By employing various methods, the research achieved a high accuracy rate of 95.37% in detecting slang words within sentences. The results of this research emphasize the significance of accurately identifying and understanding internet slang in social media posts. Accurately identifying and interpreting these terms enhances our ability to understand the nuances and context of online conversations. The high detection accuracy showcases the effectiveness of the employed techniques and contributes to the field of natural language processing. By continuously improving the detection and identification of internet slang, deeper insights can be gained into the evolving linguistic landscape of online communication.
Hindi Fake News Detection using Stacking Ensemble Method

Day by day, usage of social media is increasing amongst the users giving rise to certain issues which need attention. Since information can be shared across social media very quickly, it has become a popular medium of information sharing. Users use these platforms to share information to individuals, groups and to communities. As information is spread across social media platforms very quickly, misinformation also gets spread very quickly. Detecting fake contents is very important at the initial stage so that spread of fake information can be prevented. In existing research most of the research carried out is for English news. Very few researchers have attempted to develop a model for other languages. We present a machine learning solution for fake information identification for Hindi News articles. We carried out experiments on three datasets using various machine learning approaches such as Support Vector Machine, Logistic Regression, Decision Tree, Multinomial NB and Random Forest, these learners are used individually, and their performance is evaluated. Then we use these learners in stacking ensemble method and evaluated the performance. To extract features from news TF/IDF is used. Results show that RF performed well over all datasets as individual learner. Ensemble learning resulted in significant improvement in the performance over individual learners in all performance evaluation metrics. In some cases, results of LR are closer to that of RF.
Deformation and metamorphism within the Main Central Thrust zone, Arun Tectonic Window, eastern Nepal

Abstract The Lesser Himalayan Arun Tectonic Window comprises the Upper Precambrian, polymetamorphosed metasediments of the Nawakot Complex. These are tectonically overlain by Precambrian gneisses of the Higher Himalayan Kathmandu Complex. The main phase metamorphism within the Higher Himalayan rocks predated thrusting along the Main Central Thrust. By contrast, the main metamorphism (M2) within the Nawakot Complex commenced synchronously with it being underthrust during D2 along the Main Central Thrust Zone beneath the amphibolite facies rocks of the Kathmandu Complex. During D2 rocks on both sides of the Main Central Thrust Zone underwent intensive mylonitization within a several hundred meter wide zone of non-coaxial shearing. Although the stratigraphic order within the Nawakot Complex is the right way up, rocks within the Arun Tectonic Window show a concentric inverted metamorphic zoning, ranging from lower greenschist facies in the core of the window, up to lower amphibolite facies at the outer edge of the window. This ‘inverted metamorphism’ (M2) of the Nawakot Complex originated as a result of syn- to post-D2 downward heat conduction from the ‘hot’ hanging wall of the Main Central Thrust onto the ‘cold’ footwall. This heat flow also caused the strong retrograde overprinting at the base of the Kathmandu Complex. After a static period, a late- to post-M2 NNE-shortening (D3) commenced along the Main Boundary Thrust. The Arun Tectonic Window is the result of doming of the Main Central Thrust during D4 followed by erosional exhumation.
Parkinson’s Disease Detection Using Machine Learning Techniques

Parkinson's disease is a progressive, central nervous system disorder that impairs mobility and includes tremor-like symptoms. Typically, brain scans from clinical checks are used to diagnose Parkinson's disease. Nearly 0.58 million people are suffering from Parkinson’s disease as estimated in 2016. The identification of Parkinson's disease (PD) results in mistakes, huge amounts of medical expenses, and could cause trouble for the patients. Telemonitoring is using technology to remotely diagnose, treat, or consult with a patient. This addresses the problems with the clinical verification of Parkinson's illness. Patients with this illness may get treatment based on their motor-related symptoms, voice fluctuations from speech processing, or CAD-based image processing. Voice has lately been found to function as a biomarker for several neurological illnesses [1][12]. So, to reduce the risk and detect the disease at an early stage, we are trying to create a model using speech features and machine learning techniques. In order to detect the people suffering with Parkinson’s disease, we propose to use various techniques like Naïve Bayes, SVM, KNN, and Decision tree classifier. Such models can help people in the medical field to identify if an individual is affected by Parkinson’s disease at an early stage. We are driven to make substantial progress in this area because there isn't an early detection system created exclusively for persons with Parkinson's disease. The entire model was developed by using machine learning models and CNN [2].
Predicting User Perception Regarding Usefulness of Google Classroom Through Machine Learning Algorithms

To investigate the attitudes of the students at the tertiary level of different institutes towards using google classroom as a convenient online platform for learning the English Language, especially contemplating reading and writing skills have been performed. A survey had been conducted including 448 EFL students at the tertiary level in Bangladesh from four different institutes to figure out the result of this research. Data collection through a series of questionnaires had been posited based on learning reading and writing skills of the English Language. Finally, analyzing the data using machine learning algorithms, we concluded that google classroom is useful and effective in terms of learning the English language by elevating reading and writing skills of the students in Bangladesh based on the overall findings of both the survey and analyzing the data. Students' positive responses had been found towards using google classroom through this study.
Designing and validating an MD5 Hash Algorithm using the UVM Verification Framework and Checkers

The MD5 algorithm is widely employed for generating fixed-size hash values to ensure data integrity. By incorporating MD5 into the UVM verification framework, designers can efficiently verify the algorithm's functionality within digital designs. The UVM framework provides a structured environment with standardized components, including agents, sequences, and checkers, facilitating the development and verification of complex designs. Dedicated UVM checkers designed for MD5 verification automate and streamline the verification process, ensuring the accurate operation of the MD5 algorithm. These checkers monitor input and output data, comparing the generated hash value with the expected outcome, enabling thorough verification of the algorithm's integrity. Integrating MD5 into the UVM verification framework with checkers improves the verification process's efficiency, reducing manual efforts and enhancing reliability. This integration allows designers and verification engineers to effectively validate the implementation and accuracy of the MD5 hash algorithm in digital designs.
A Biometric-Finger Vein Authentication System for Security Purpose using Deep Learning Technique

Biometrics uses human physiological characteristics, is one way of protecting personal information. The usage of finger vein as a form of biometric become most popular method recently. Finger vein authentication provides a high level of security and accuracy, making it a reliable biometric authentication method. Finger vein authentication system compares the vascular structure of a person's finger with previously acquired data. This technique involves identifying patterns in vein images of human fingers below the skin's surface. The proposed system aims to enhance the security of user authentication by utilizing the unique features of finger vein patterns. The finger vein image is acquired from the database. The preprocessing done in order to remove the noise by means of Gaussian median filter in spatial domain and frequency domain. The segmentation of the image carried out through line tracking method which provided the better contrast image. The system utilizes Convolutional Neural Networks for feature extraction and the features are matched with the finger vein database. Then Deep Learning Approach used for classification of finger vein patterns between genuine and imposter users. For real-time communication the scanner scans the finger vein and send to Arduino board for storage followed by MATLAB for processing and classification of the images. The result is sent through GSM module as alarm or message. Then the information also stored in IoT for future references. with the user a GSM Module is integrated. The proposed system gives an accuracy of 96%. Thus, system is beneficial in several security applications such as access control, identity verification, banking system, and financial transactions.
Automated Diagnosis of Lumpy Skin Diseases based on Deep Learning Feature Fusion

Lumpy skin disease, a tremendously contagious viral disease of cattle and buffalo caused by a Poxviridae family virus commonly referred to as the Neethling virus, has a low fatality rate. The lumpy skin disease has wreaked havoc throughout India. The lumpy skin disease virus (LSDV) infects cattle with lumpy skin disease (LSD). The tremendous economic impact of LSDV in cattle makes it one of the most important animal poxviruses. As a result, precise detection and classification are crucial in lumpy skin disease therapy to avoid its spread, enable timely treatment, minimize economic losses, and ensure the well-being of affected animals. Numerous research strategies based on traditional machine learning (ML) and deep learning (DL) have been introduced for LSD detection and categorization. Traditional ML classifiers necessitate hand-crafted features, which is time-consuming. On the contrary, DL is particularly robust in feature extraction and has recently been widely employed for classification and detection. Therefore, in this paper, we propose a model based on the fusion of Resnet-50 and VGG-19 deep learning features to detect lumpy skin disease. The dimension of the feature vector has been reduced by the principal component analysis (PCA). To categorize diseases, this reduced feature is provided as input to different classifiers such as Naive Bayes, Decision Tree, Random Forest, and KNN. The performance of the model is evaluated at several phases, and the findings demonstrate that the suggested model outperformed the existing models for lumpy skin classification by obtaining an accuracy of 99%.
A Hybrid Machine Learning and Deep Learning Approach for Remote Sensing Scene Classification

This paper presents an alternative approach for the classification of remote sensing scene images using a combination of deep learning and classical machine learning techniques. The suggested model employs the EfficientNet architecture to extract features from remote sensing images, which are then enhanced through the use of three attention mechanisms - Channel, Spatial, and Local Relation Attention. Next, the feature vectors are reduced in dimensionality using principal component analysis (PCA) and support vector machine (SVM) is utilized for classification. To optimize the performance of the model, a grid search algorithm is used to find the best possible hyperparameters. The trained model is evaluated using metrics such as Macro F1 score and accuracy on a test dataset. The proposed model shows promising results and can be used for classification of image data of remote sensing in various applications. The efficacy of the presented approach is assessed on four datasets, UC Merced, EuroSAT, MLRSNet, and RSI-CB256
Smart Police: A Hybrid Deep Learning Model for Crime Proactivity Assessment

Conducting competent and precise investigations on criminal situations is necessary to establish peace, order, and justice in a community. With the increased use of modern sophisticated systems to track crime and violence, computer programmers may also significantly assist officials in law enforcement. Previous research was not successfully able to accomplish crime forecasting and precision of predictions made by learning models. This paper proposes CNN-LSTM hybrid approach for forecasting and predicting criminal behavior with improved accuracy. To understand the crimes data, the study uses different machine learning models such as Support Vector Machines, Naïve Bayes, k-Nearest Neighbors, Logistic Regression, Multilayer Perceptron’s (MLP), cloud gradient boosting (XGBoost), decision tree, random forests, time series analysis implementing a Convolutional Neural Network and (ARIMA) Autoregressive Integrated Moving Average and Long-Term Memory model and machine learning algorithm to improve error predictions. The demonstration of model was evaluated with various performance criteria. After extensive testing on real-time data, the CNN-LSTM model shows higher accuracy compared to existing models. Specifically, the model achieved 91% accuracy in the dataset of Denver and 90% in the Los Angeles dataset.
An Action-Level Assistant for Robotic Manipulation: User Experience and Performance

Achieving full autonomy in robotic manipulation tasks in unstructured environments remains challenging despite technological advancements. This paper introduces LOLA, a user-centric framework designed for seamless task creation across various robotic manipulators. A comparative user study with 20 participants revealed that LOLA closely matched user’s expectations highlighting its potential for broader application in robotic manipulation.
Efficient Classification Of Autism in Children based on Resnet-50 and Xception module

Children with autism spectrum disorder struggle to process emotions appropriately, which makes it difficult for them to understand facial expressions. It is crucial for autistic children to recognize emotions because emotion is a crucial component of good socialization. In the proposed method, we use the two different CNN architecture which includes Resnet-50 and Xception modules, and designed an app to classify autistic and non-autistic children. The results demonstrated that the accuracy of the Resnet-50 method is better when compared to the existing conventional methods.
A Mobile Application for Plant Disease Classification using Convolutional Neural Network

Mobile applications have now become the most efficient and direct way to deliver information to the user. Agriculture requires mobile applications that can identify plant diseases from pictures of a plant’s leaves. A model with fewer parameters is taken into consideration while implementing this from the existing models. A mobile application built on the Mobilenetv2 architecture is created to categorise plant diseases from leaf photos. The plant village dataset, a publicly accessible dataset that includes photos of both healthy and diseased plant leaves from crops grown all over the world, is taken into consideration for training a model. There are 54,305 samples of fourteen crops over 38 groups in the collection. Of these, 10,849 samples are considered for testing and the remaining 43,456 samples are used for training and validation. The proposed model has a 92.45% accuracy rate. The model has been implemented as a mobile application that will detect plant disease. This application will be an easy way for the farmers to detect the diseases and get a remedy at the earliest.