Muhamad Faisal

Infertility has become a common problem in global health, and unsurprisingly, many couples need medical assistance to achieve reproduction. Many human behaviors can lead to infertility, which is none other than unhealthy sperm. The important thing is that assisted reproductive techniques require selecting healthy sperm. Hence, machine learning algorithms are presented as the subject of this research to effectively modernize and make accurate standards and decisions in classifying sperm. In this study, we developed a deep learning fusion architecture called SwinMobile that combines the Shifted Windows Vision Transformer (Swin) and MobileNetV3 into a unified feature space and classifies sperm from impurities in the SVIA Subset-C. Swin Transformer provides long-range feature extraction, while MobileNetV3 is responsible for extracting local features. We also explored incorporating an autoencoder into the architecture for an automatic noise-removing model. Our model was tested on SVIA, HuSHem, and SMIDS. Comparison to the state-of-the-art models was based on F1-score and accuracy. Our deep learning results accurately classified sperm and performed well in direct comparisons with previous approaches despite the datasets’ different characteristics. We compared the model from Xception on the SVIA dataset, the MC-HSH model on the HuSHem dataset, and Ilhan et al.’s model on the SMIDS dataset and the astonishing results given by our model. The proposed model, especially SwinMobile-AE, has strong classification capabilities that enable it to function with high classification results on three different datasets. We propose that our deep learning approach to sperm classification is suitable for modernizing the clinical world. Our work leverages the potential of artificial intelligence technologies to rival humans in terms of accuracy, reliability, and speed of analysis. The SwinMobile-AE method we provide can achieve better results than state-of-the-art, even for three different datasets. Our results were benchmarked by comparisons with three datasets, which included SVIA, HuSHem, and SMIDS, respectively (95.4% vs. 94.9%), (97.6% vs. 95.7%), and (91.7% vs. 90.9%). Thus, the proposed model can realize technological advances in classifying sperm morphology based on the evidential results with three different datasets, each having its characteristics related to data size, number of classes, and color space.

Coffee leaf diseases can significantly impact the productivity and quality of the crops. Accurate and timely identification of these diseases is crucial for effective management and control. This paper proposes a hybrid feature fusion approach for identifying coffee leaf disease, including early and late feature fusion. First, we propose several hybrid models to extract the information feature in the input images by combining MobileNetV3, Swin Transformer, and variational autoencoder (VAE). MobileNetV3, acting on the inductive bias of locality, can extract image features that are closer to one another (local features), while the Swin Transformer is able to extract feature interactions that are further apart (high-level features). These differently extracted features contain complementary information that enriches a unified feature map. Second, the extracted images from models are fused in the early fusion network. The early-fusion learner network is deployed to learn the rich information from the extracted feature. The late fusion network is implemented to comprehensively learn the fused feature before a classification network classifies coffee leaf diseases. The proposed hybrid feature fusion approach is evaluated on a challenging, real world Robusta Coffee Leaf (RoCoLe) dataset with various diseases, including red spider mite and leaf rust disease. The results show that our approach, the hybrid feature fusion of MobileNetV3 and Swin Transformer, outperforms the individual models with an accuracy of 84.29%. In conclusion, the hybrid feature fusion approach combining MobileNetV3 and Swin Transformer models is a promising approach for coffee leaf disease identification, providing accurate and timely diagnosis for effective management and control of the diseases in real-world conditions.

The early identification of plant diseases is crucial for preventing the loss of crop production. Recently, the advancement of deep learning has significantly improved the identification of plant leaf diseases. However, most approaches depend on a single convolutional neural network (CNN) to extract the leaf features, ignoring the opportunity to take full advantage of the feature richness available in the images. This paper explores a novel CNN model with multiple automated feature extractors, namely, dense fusion CNN (DFNet), for classifying plant leaf diseases. DFNet aims to increase the diversity of extracted features in order to improve discrimination. Instead of using a single‐CNN model, DFNet relies on a double‐pretrained CNN model, MobileNetV2 and NASNetMobile, as the feature extractor. The features extracted from each CNN are fused in the fusion layer using a fully connected network. The proposed method was evaluated using corn (Zea mays L.) and coffee (Coffea canephora) leaf disease datasets and compared to the existing models. The experiment showed that DFNet is superior and consistent to other CNN methods by achieving an accuracy of 97.53% for corn leaf diseases and 94.65% for coffee leaf diseases.

In the development of deep learning, several convolution neural network (CNN) models are designed to solve various tasks. However, these CNN models are complex and cumbersome to achieve state-of-the-art performance. The current CNN models remain to suffer from the problem of large models. Thus, model compression techniques are proposed to cope with the complex CNN models. Meanwhile, the selection of compressed model to suit the user requirement significantly contributes during the deployment process. This paper analyses two model compressions, namely the layerwise and the widthwise compression. The compression techniques are implemented in the MobileNetV1 model. Then, knowledge distillation is applied to compensate for the accuracy loss of the compressed model. We demonstrate the analysis of those compressed models from various perspectives and develop several suggestions on the trade-off between the performance and the compression rate. In addition, we also show that the feature that is learned by the compressed models using knowledge distillation has better representation compared to the vanilla model. Our experiment shows that the widthwise compression on MobileNetV1 achieves a compression rate of 42.27% and the layerwise compression achieves 32.42%, respectively. Furthermore, the improvement of the compressed models using knowledge distillation is notable for the widthwise compression with the increasing accuracy above 4.71%.

The development of the Internet of Things (IoT) in electronics, computer, robotics, and internet technology is inevitable and has rapidly accelerated more than before as the IoT paradigm is a promising solution in terms of solving real world problems, especially for digitizing and monitoring in real time. Various IoT schemes have successfully been applied to some areas such as smart health and smart agriculture. Since the agriculture areas are getting narrow, the development of IoT in agriculture should be prioritized to enhance crop production. This paper proposes the IoT scheme for long range communication based on Long Range (LoRa) modules applied to smart agriculture. The scheme utilizes the low power modules and long-distance communication for monitoring temperature, humidity, soil moisture, and pH soil. Our IoT design has successfully been applied to agriculture areas which have unstable network connections. The design is analyzed to obtain the maximum coverage using different spreading factors and bandwidths. We show that as the spreading factor increases to 12, the maximum coverage can be transmitted to 1000 m. However, the large coverage also comes with the disadvantages of the increased delays.

As the rapid development of information and communication technology systems offers limitless access to data, the risk of malicious violations increases. A network intrusion detection system (NIDS) is used to prevent violations, and several algorithms, such as shallow machine learning and deep neural network (DNN), have previously been explored. However, intrusion detection with imbalanced data has usually been neglected. In this paper, a cost-sensitive neural network based on focal loss, called the focal loss network intrusion detection system (FL-NIDS), is proposed to overcome the imbalanced data problem. FL-NIDS was applied using DNN and convolutional neural network (CNN) to evaluate three benchmark intrusion detection datasets that suffer from imbalanced distributions: NSL-KDD, UNSW-NB15, and Bot-IoT. The results showed that the proposed algorithm using FL-NIDS in DNN and CNN architecture increased the detection of intrusions in imbalanced datasets compared to vanilla DNN and CNN in both binary and multiclass classifications.