Joko Siswanto, Sri Yulianto Joko Prasetyo, Sutarto Wijono
et al.
Accurate predictions of the number of public transport passengers on buses in each region are crucial for operations. They are required by the planning and management authority for bus public transport. A deep learning-based LSTM prediction model is proposed to predict the number of passengers in 4 bus public transportation areas (central, north, south, and west), evaluated by MSLE, MAPE, and SMAPE with dropout, neuron, and train-test variations. The CSV dataset obtained from Auckland Transport(AT) New Zealand metro patronage report on bus performance(1/01/2019-31/07/2023) is used for evaluation. The best prediction model was obtained from the lowest evaluation value and relatively fast time with a dropout of 0.2, 32 neurons, and train-test 80-20. The prediction model on training and testing data improves with the suitability of tuning for four predictions for the next 12 months with mutual fluctuations. The strong negative correlation is central-south, while the strong positive correlation is north-west. Predictions are less closely interconnected and dependent, namely central-south. With its potential to significantly impact policy-making, this prediction model can increase public transport mobility in each region, leading to a more efficient and accessible public transport system and ultimately enhancing the public's daily lives. This research has practical implications for public transport authorities, as it can guide them in making informed decisions about service planning and resource allocation.
Electromyography (EMG) classification using machine learning techniques has gained significant attention in recent years due to its applications in various aspects such as prosthetic control, gesture recognition and muscle health monitoring. In this study, we explore the applications of both Machine Learning and Deep Learning techniques for the classification of EMG signals. In this work, a new CNN and DFNN models is proposed for this purpose achieving high accuracy with low computation time. Additionally, several machine learning algorithms are evaluated for EMG classification, including Random Forest, KNN, AdaBoost, Decision Tree with and without cross-validation were employed. Moreover, we investigate the impact of class balance on the performance of these models. Model selection and hyperparameter tuning are conducted to optimize the performance. The models are assessed based on accuracy, precision, recall and [Formula: see text]-score. The best results are obtained using random forest with an accuracy of 99.81% while the proposed CNN model achieved an accuracy of 99.61%. Experimental results proved the efficiency of the proposed work compared to other state-of-the-art works.
Muhammadjon Tursunov, Khabibullo Sabirov, Ramazon Alikulov
et al.
The results obtained on the photoelectric battery (PVB) and photothermal battery (PTB) based on a new type of autonomous moving cooling system photothermal devices (PTD) are presented in this research work. This new type of device (PTD) is self-cooling and has the ability to provide hot water for the village residents while increasing the efficiency of the PTB. There was created a new experimental copy of the device with a power of 300 W based on the design of the PTD with a new type of cooling system with a power of 60 W mentioned in previous scientific research. It consists of a 180 W PVB, a 60A•h battery, a 2kW inverter, a 50A controller, a radiator for cooling hot water, 5 cooling fans, a pump, and a cart-shaped structure for their installation. It is possible to get results in two different situations in the experimental copy with a new type of cooling system. It is possible to increase the efficiency of PTD by fully using the cooling system, and to obtain hot water for the household without a sharp decrease in efficiency by partially using the cooling system. There is mentioned a study of hot water regimes with a temperature of 40-50 0C for the agricultural sector, depending on the intensity of solar radiation and ambient temperature. Preliminary tests showed that the power of the PTD differs from the power of the PVB by up to 70 W. This new PTD showed that it is possible to use it in many other cases, such as water supply, lighting, watching TV, listening to the radio, using a refrigerator, charging computers and phones in rural areas.
Abstract Plant diseases significantly threaten global agriculture, impacting crop yield and food security. Nearly 30% of the crop yield is lost due to plant diseases. Efficient identification and classification of plant diseases through computer vision techniques have become imperative for timely intervention. However, popular plant disease datasets often suffer from data imbalance, with certain classes underrepresented, hindering the performance of machine learning models. Traditional data augmentation methods, such as rotation and flipping, are limited in their effectiveness, especially when faced with imbalanced datasets. To address this limitation, we explore advanced data augmentation techniques, including Generative Adversarial Networks (GANs) such as CycleGAN and LeafGAN, which have shown promise in generating synthetic images. However, we propose an innovative approach of Object-based single Style Transfer on a single neural network for augmenting the plant disease dataset. This technique focuses on mitigating data imbalance issues within datasets, which can adversely affect the model’s ability to generalize across diverse classes. The proposed method is compared with state-of-the-art data augmentation techniques, highlighting its superiority in addressing data imbalance issues. Our approach aims to produce more realistic and diverse synthetic images, leading to improved model generalization and accuracy in plant disease classification tasks validated using different classifiers. The efficiency of our approach is validated through extensive experimentation and benchmarking against existing methods.
Computational linguistics. Natural language processing, Electronic computers. Computer science
Tahereh Vasei, Harshil Gediya, Maryam Ravan
et al.
This study investigates the neurophysiological effects of transcutaneous electroacupuncture stimulation (TEAS) on brain activity, using advanced machine learning techniques. This work analyzed the electroencephalograms (EEG) of 48 study participants, in order to analyze the brain’s response to different TEAS frequencies (2.5, 10, 80, and sham at 160 pulses per second (pps)) across 48 participants through pre-stimulation, during-stimulation, and post-stimulation phases. Our approach introduced several novel aspects. EEGNet, a convolutional neural network specifically designed for EEG signal processing, was utilized in this work, achieving over 95% classification accuracy in detecting brain responses to various TEAS frequencies. Additionally, the classification accuracies across the pre-stimulation, during-stimulation, and post-stimulation phases remained consistently high (above 92%), indicating that EEGNet effectively captured the different time-based brain responses across different stimulation phases. Saliency maps were applied to identify the most critical EEG electrodes, potentially reducing the number needed without sacrificing accuracy. A phase-based analysis was conducted to capture time-based brain responses throughout different stimulation phases. The robustness of EEGNet was assessed across demographic and clinical factors, including sex, age, and psychological states. Additionally, the responsiveness of different EEG frequency bands to TEAS was investigated. The results demonstrated that EEGNet excels in classifying EEG signals with high accuracy, underscoring its effectiveness in reliably classifying EEG responses to TEAS and enhancing its applicability in clinical and therapeutic settings. Notably, gamma band activity showed the highest sensitivity to TEAS, suggesting significant effects on higher cognitive functions. Saliency mapping revealed that a subset of electrodes (Fp1, Fp2, Fz, F7, F8, T3, T4) could achieve accurate classification, indicating potential for more efficient EEG setups.
The overcomplete convolutional structure for biological images and volume segmentation is an excellent solution to the problem in which traditional codec methods cannot accurately segment the boundary regions. Although such methods perform well, the drawback that convolutional operations do not effectively learn global and remote semantic information interactions must be addressed. Accordingly, a new image segmentation network, KTU-Net, is proposed for the medical image segmentation of liver tumors. The network structure constitutes three branches: 1)Kite-Net, which is an overcomplete convolutional network that learns to capture input details and precise edges; 2)U-Net, which learns high-level features; 3)Transformer, which learns sequential representations of input bodies and efficiently captures global multiscale information. KTU-Net is designed for both early and late fusion, and a hybrid loss function is adopted to guide network training to achieve increased stability. From extensive experimental results regarding the LiTS liver tumor segmentation dataset, KTU-Net achieves higher or similar segmentation accuracy than other advanced 3D medical image segmentation methods such as KiU-Net, TransBTS, and UNETR. Fusing the three branching features, the average Dice scores of liver tumors are improved by 0.7% and 2.1%, achieving increased quality of features learned by the network and more accurate segmentation results of liver tumors, thus providing a reliable basis for doctors to perform precise liver tumor cell assessments and treatment plans.
This article offers a conceptual and methodological contribution to linguistics by exploring the potential value of using sentiment analysis (SA) for research in this field. Firstly, it discusses the limitations and advantages of using SA for linguistics research including the wider epistemological implications of its application outside of its original conception as a product reviews analysis tool. Methodologically, it tests its applicability against an established linguistic case: the correlation between subjective attitudes such as surprise, irritation and discontent and the use of the progressive. The language example is Italian for which this function of the progressive form has not been analyzed yet. The analysis applies FEEL-IT, a state-of-the-art transformer-based machine learning model for emotion and sentiment classification in Italian on language samples from various sources as collected in Evalita-2014 (238,556 words). The results show statistically significant correlations between negative subjective attitudes and the use of the progressive in line with previous accounts in other languages. The article concludes with a few additional propositions for practitioners and researchers using SA.
Deep learning has been instrumental in solving difficult problems by automatically learning, from sample data, the rules (algorithms) that map an input to its respective output. Purpose: Perform land use landcover (LULC) classification using the training data of satellite imagery for Moscow region and compare the accuracy attained from different models. Methods: The accuracy attained for LULC classification using deep learning algorithm and satellite imagery data is dependent on both the model and the training dataset used. We have used state-of-the-art deep learning models and transfer learning, together with dataset appropriate for the models. Different methods were applied to fine tuning the models with different parameters and preparing the right dataset for training, including using data augmentation. Results: Four models of deep learning from Residual Network (ResNet) and Visual Geometry Group (VGG) namely: ResNet50, ResNet152, VGG16 and VGG19 has been used with transfer learning. Further training of the models is performed with training data collected from Sentinel-2 for the Moscow region and it is found that ResNet50 has given the highest accuracy for LULC classification for this region. Practical relevance: We have developed code that train the 4 models and make classification of the input image patches into one of the 10 classes (Annual Crop, Forest, Herbaceous Vegetation, Highway, Industrial, Pasture, Permanent Crop, Residential, River, and Sea&Lake).
Rhizomer helps researchers and practitioners explore knowledge graphs available as Semantic Web data by performing the three data analysis tasks: overview, zoom and filter, and details-on-demand. This approach makes it easier for users to get an idea about the overall structure and intricacies of a dataset, when compared to existing approaches and even without prior knowledge. Rhizomer is helpful for data reusers, who want to know about the reuse opportunities of a given dataset, and for knowledge graph creators, who can check if the generated data follow their expectations. Rhizomer has been applied in many scenarios, from research and commercial projects to teaching.
We created a hand texture resource (with different skin tone versions as well as non-human hands) for use in virtual reality studies. This makes it easier to run lab and remote studies where the hand representation is matched to the participants’ own skin tone. We validate that the virtual hands with our textures align with participants’ view of their own real hands and allow to create VR applications where participants have an increased sense of body ownership. These properties are critical for a range of VR studies, such as of immersion.
Sanjay Goswami, Kshama D. Dhobale, Ravindra D. Wavhale
et al.
Abstract The field of cancer nanomedicine has made significant progress, but its clinical translation is impeded by many challenges, such as the difficulty in analyzing intracellular anticancer drug release by the nanocarriers due to the lack of suitable tools. Here, we propose the development of an image‐based strategy involving machine learning (ML) to evaluate anticancer drug such as doxorubicin hydrochloride (DOX) released by a nanocarrier inside the HCT116 colon cancer cells and its subsequent intracellular accumulation. This technique combines fluorescent cell imaging with ML‐based image analysis to assess and quantify the delivery of DOX by nanoparticles within them. We show that DOX in HCT116 cells was higher for multifunctional CNT‐DOX‐Fe3O4 nanocarrier than free DOX, indicating efficient and steady release of DOX as well as superior retentive property of the nanocarrier. Initially (1 and 4 hours), the luminance intensity of DOX in the cell cytoplasm delivered by CNT‐DOX‐Fe3O4 nanocarrier was ~0.34 and ~0.42 times lesser than that of free DOX delivered normally. However, at 24 and 48 hours posttreatment, the luminance intensity of DOX for CNT‐DOX‐Fe3O4 nanocarrier was ~1.98 and ~1.92 times higher than that of free DOX. Furthermore, the luminance intensity of DOX for CNT‐DOX‐Fe3O4 in the whole cell was ~1.35 and ~1.62 times higher than that of free DOX at 24 and 48 hours, respectively. The high‐throughput nature of our image analysis workflow allowed us to automate the process of DOX retention analysis and enabled us to devise ML‐based modeling to predict the percentage of anticancer drug retention in cells. The development of models to automatically quantify and predict intracellular drug release in cancer cells could benefit personalized treatments by optimizing the design of nanocarriers.
Nowdays the password encryption is an indispensable job used in software developing process in order to secure the important information. In this work it is proposed an original and efficient algorithm capable to encrypt a password. In this regard we considered the following terms: real password and encrypted password. The encryption algorithm is based on an original method of character processing.
The study investigated the relationship between employees’ expectation (EE) and organizational silence (OS). Design/Methodology: A survey of one hundred and eight (108) working class Professional Master Students was carried out eliciting responses through a self- constructed instrument that has Cronbach alpha of 0.864 and 0.825 reliability values for employees’ expectation and organizational silence respectively. Both Pearson product moment correlation and multiple regressions statistics were used to test the stated research hypotheses. Findings: It was found that employees’ expectations have a strong positive and statistically significant relationship with organization silence. The indices of employee expectations: employee control (EC), employee ownership (EO) and employee appreciation (EA) separately correlate positively and significantly with organization silence. The study also showed that they are strong predictors of organizational silence except employee control that is somewhat a weak predictor. In combination however, employee expectation is a strong, positive and significant predictor of organizational silence. The influence of employees’ expectations of control, appreciation and ownership explained 30.5% of organization silence. Conclusion/recommendation: The study established that the types of organization silence are not limited to the three existing one of quiescent, acquiescent and pro-social but also include accrual benefits. Also, the motives for organization silence of resignation, fear and other-oriented, were extended to include self-oriented benefits. Research implications: Organization scholars, business owners and researchers should seek for the expectations of the employees as they contribute to change and work related improvement in the quest to stem the tide of silence behaviour climate in business organization.
Green technology has drawn a huge amount of attention with the development of the modern world. Similarly with the development in communication technology the industries and researchers are focusing to make this communication as green as possible. In cellular technology the evolution of 5G is the next step to fulfil the user demands and it will be available to the users in 2020. This will increase the energy consumption by which will result in excess emission of co2. In this paper different techniques for the green communication technology and some challenges are discussed. These techniques include device-to-device communication (D2D), massive Multiple-Input Multiple-Output (MIMO) systems, heterogeneous networks (HetNets) and Green Internet of Things (IoT).
Due to the existence of multiple views in many real-world data sets, multi-view clustering is increasingly popular. Many approaches have been investigated, among which the subspace clustering methods finding the underlying subspaces of data have been developed recently. Although the subspace-based multi-view methods can achieve promising performance, the shared subspace information has not been fully utilized. To address this problem, a novel multi-view clustering model by simultaneously learning shared subspace and affinity matrix is proposed. In our method, a shared subspace is learned to preserve the effective consensus information of all views. Then, a subspace-based affinity matrix with adaptive neighbors is learned to assign the most suitable cluster to each data point. An iterative strategy is developed for solving this problem. Moreover, experiments on four benchmark data sets demonstrate that our algorithm outperforms other state-of-the-art algorithms.
Nowadays, in the after-treatment of diesel exhaust gas, a diesel particulate filter (DPF) has been used to trap nano-particles of the diesel soot. However, as there are more particles inside the filter, the pressure which corresponds to the filter backpressure increases, which worsens the fuel consumption rate, together with the abatement of the available torque. Thus, a filter with lower backpressure would be needed. To achieve this, it is necessary to utilize the information on the phenomena including both the soot transport and its removal inside the DPF, and optimize the filter substrate structure. In this paper, to obtain useful information for optimization of the filter structure, we tested seven filters with different porosities and pore sizes. The porosity and pore size were changed systematically. To consider the soot filtration, the particle-laden flow was simulated by a lattice Boltzmann method (LBM). Then, the flow field and the pressure change were discussed during the filtration process.
Point to Point jaringan nirkabel merupakan solusi untuk menghubungkan dua jaringan yang berada dilokasi yang berbeda dan sulit untuk dilewati kabel jaringan. SMA Universitas Klabat walaupun terletak satu kawasan dengan kampus utama universitas namun lokasi gedung agak jauh dan sulit untuk dilewati kabel. Tujuan dari penelitian ini adalah untuk membangun insfrastruktur jaringan internet lewat point to point, analisis dan desain access point yang akan digunakan serta pembagian bandwidth yang merata ke setiap client. Metode penelitian yang digunakan dalam penelitian ini adalah Network Development Life Cycle dengan tahapan analisis, desain, simulasi, implementasi, monitoring dan manajemen. Base station dan client yang digunakan adalah nano station M5 ubiquiti, access point yang digunakan untuk koneksi jaringan nirkabel disetiap gedung adalah ubiquiti UAP dan pembagian bandwidth diatur menggunakan mikrotik router board. Hasil implementasi dari penelitian ini adalah koneksi internet dapat tersalur dari kampus utama universitas ke SMA serta dapat digunakan oleh siswa dan guru baik diruangan kelas maupun dikantor.
Kata kunci—Point to Point, Bandwidth, Jaringan Nirkabel, Ubiquiti