Recently, the concept of weakly supervised learning has gained popularity in the high-energy physics community due to its ability to learn even with a noisy and impure dataset. This method is valuable in the quest to discover the elusive beyond Standard Model (BSM) particle. Nevertheless, the weakly supervised learning method still requires a learning sample that describes the features of the BSM particle truthfully to the classification model. Even with the various theoretical framework such as supersymmetry and the quantum black hole, creating a BSM sample is not a trivial task since the exact feature of the particle is unknown. Due to these difficulties, we propose an alternative classifier type called the one-class classification (OCC). OCC algorithms require only background or noise samples in its training dataset, which is already abundant in the high-energy physics community. The algorithm will flag any sample that does not fit the background feature as an abnormality. In this paper, we introduce two new algorithms called EHRA and C-EHRA, which use machine learning regression and clustering to detect anomalies in samples. We tested the algorithms’ capability to create distinct anomalous patterns in the presence of BSM samples and also compare their classification output metrics to the Isolation Forest (ISF), a well-known anomaly detection algorithm. Five Monte Carlo supersymmetry datasets with the signal to noise ratio equal to 1, 0.1, 0.01, 0.001, and 0.0001 were used to test EHRA, C-EHRA and ISF algorithm. In our study, we found that the EHRA with an artificial neural network regression has the highest ROC-AUC score at 0.7882 for the balanced dataset, while the C-EHRA has the highest precision-sensitivity score for the majority of the imbalanced datasets. These findings highlight the potential use of the EHRA, C-EHRA, and other OCC algorithms in the quest to discover BSM particles.

The rapid advancement in pattern recognition via the deep learning method has made it possible to develop an autonomous medical image classification system. This system has proven robust and accurate in classifying most pathological features found in a medical image, such as airspace opacity, mass, and broken bone. Conventionally, this system takes routine medical images with minimum pre-processing as the model's input; in this research, we investigate if saliency maps can be an alternative model input. Recent research has shown that saliency maps' application increases deep learning model performance in image classification, object localization, and segmentation. However, conventional bottom-up saliency map algorithms regularly failed to localize salient or pathological anomalies in medical images. This failure is because most medical images are homogenous, lacking color, and contrast variant. Therefore, we also introduce the Xenafas algorithm in this paper. The algorithm creates a new kind of anomalous saliency map called the Intensity Probability Mapping and Weighted Intensity Probability Mapping. We tested the proposed saliency maps on five deep learning models based on common convolutional neural network architecture. The result of this experiment showed that using the proposed saliency map over regular radiograph chest images increases the sensitivity of most models in identifying images with air space opacities. Using the Grad-CAM algorithm, we showed how the proposed saliency map shifted the model attention to the relevant region in chest radiograph images. While in the qualitative study, it was found that the proposed saliency map regularly highlights anomalous features, including foreign objects and cardiomegaly. However, it is inconsistent in highlighting masses and nodules. ABSTRAK: Perkembangan pesat sistem pengecaman corak menggunakan kaedah pembelajaran mendalam membolehkan penghasilan sistem klasifikasi gambar perubatan secara automatik. Sistem ini berupaya menilai secara tepat jika terdapat tanda-tanda patologi di dalam gambar perubatan seperti kelegapan ruang udara, jisim dan tulang patah. Kebiasaannya, sistem ini akan mengambil gambar perubatan dengan pra-pemprosesan minimum sebagai input. Kajian ini adalah tentang potensi peta salien dapat dijadikan sebagai model input alternatif. Ini kerana kajian terkini telah menunjukkan penggunaan peta salien dapat meningkatkan prestasi model pembelajaran mendalam dalam pengklasifikasian gambar, pengesanan objek, dan segmentasi gambar. Walau bagaimanapun, sistem konvensional algoritma peta salien jenis bawah-ke-atas kebiasaannya gagal  mengesan salien atau anomali patologi dalam gambar-gambar perubatan. Kegagalan ini disebabkan oleh sifat gambar perubatan yang homogen, kurang variasi warna dan kontras. Oleh itu, kajian ini memperkenalkan algoritma Xenafas yang menghasilkan dua jenis pemetaan saliensi anomali iaitu Pemetaan Kebarangkalian Keamatan dan Pemetaan Kebarangkalian Keamatan Pemberat. Kajian dibuat pada peta salien yang dicadangkan iaitu pada lima model pembelajaran mendalam berdasarkan seni bina rangkaian neural konvolusi yang sama. Dapatan kajian menunjukkan dengan menggunakan peta salien atas gambar-gambar radiografi dada tetap membantu kesensitifan kebanyakan model dalam mengidentifikasi gambar-gambar dengan kelegapan ruang udara. Dengan menggunakan algoritma Grad-CAM, peta salien yang dicadangkan ini mampu mengalih fokus model kepada kawasan yang relevan kepada gambar radiografi dada. Sementara itu, kajian kualitatif ini juga menunjukkan algoritma yang dicadangkan mampu memberi ciri anomali, termasuk objek asing dan kardiomegali. Walau bagaimanapun, ianya tidak konsisten dalam menjelaskan berat dan nodul.