Data classification using the Dempster-Shafer method

In this paper, the Dempster-Shafer method is employed as the theoretical basis for creating data classification systems. Testing is carried out using three popular (multiple attribute) benchmark datasets that have two, three and four classes. In each case, a subset of the available data is used for training to establish thresholds, limits or likelihoods of class membership for each attribute, and hence create mass functions that establish probability of class membership for each attribute of the test data. Classification of each data item is achieved by combination of these probabilities via Dempster’s Rule of Combination. Results for the first two datasets show extremely high classification accuracy that is competitive with other popular methods. The third dataset is non-numerical and difficult to classify, but good results can be achieved provided the system and mass functions are designed carefully and the right attributes are chosen for combination. In all cases the Dempster-Shafer method provides comparable performance to other more popular algorithms, but the overhead of generating accurate mass functions increases the complexity with the addition of new attributes. Overall, the results suggest that the D-S approach provides a suitable framework for the design of classification systems and that automating the mass function design and calculation would increase the viability of the algorithm for complex classification problems

Intelligent system for spoken term detection using the belief combination

Spoken Term Detection (STD) can be considered as a sub-part of the automatic speech recognition which aims to extract the partial information from speech signals in the form of query utterances. A variety of STD techniques available in the literature employ a single source of evidence for the query utterance match/mismatch determination. In this manuscript, we develop an acoustic signal processing based approach for STD that incorporates a number of techniques for silence removal, dynamic noise filtration, and evidence combination using Dempster-Shafer Theory (DST). A ‘spectral-temporal features based voiced segment detection’ and ‘energy and zero cross rate based unvoiced segment detection’ are built to remove the silence segments in the speech signal. Comprehensive experiments have been performed on large speech datasets and consequently satisfactory results have been achieved with the proposed approach. Our approach improves the existing speaker dependent STD approaches, specifically the reliability of query utterance spotting by combining the evidences from multiple belief sources

Diagnosis Penyakit Utama Pisang karena Jamur Patogen dengan Dempster-Shafer

Pisang merupakan sumber penting karbohidrat, vitamin dan mineral, dapat ditemui hampir di seluruh bagian wilayah Indonesia. Budidaya pisang menghadapi beberapa masalah penting, salah satu faktornya adalah serangan hama dan penyakit. Ketidaktahuan para pembudidaya tanaman buah pisang dan masih sedikitnya dilakukan diagnosis penyakit tanaman pisang, menyebabkan turunnya kualitas pisang dan dapat menjadi ancaman turunnya kuantitas produksi pisang. Teori Dempster-Shafer evidence memungkinkan seseorang untuk menggabungkan evidence dari berbagai sumber dan sampai pada fungsi kepercayaan dengan memperhitungkan semua evidence yang tersedia. Sehingga Dempster-Shafer diusulkan untuk diterapkan pada 32 data uji simulasi yang dilakukan secara acak. Kesesuaian hasil diagnosis simulasi perhitungan Dempster-Shafer dengan hasil diagnosis pakar ditunjukkan dengan nilai akurasi sebesar 93%. Perbedaan diagnosis penyakit dan hasil simulasi dengan Dempster-Shafer menjadi hal yang penting untuk dilakukan penelitian lanjutan

PENERAPAN METODE DEMPSTER-SHAFER UNTUK DETEKSI PENYAKIT DEMAM BERDARAH DAN TIPUS

Metode Dempster-Shafer merupakan salah satu metode yang dapat diterapkan dalam proses deteksi penyakit, seperti halnya penyakit Demam Berdarah Dengue (DBD) dan tifus. Metode ini bekerja berdasarkan nilai keyakinan (belief), plausability, dan mass function, serta banyak digunakan karena dapat memberikan tingkat keyakinan gabungan dari beberapa gejala penyakit. Sistem deteksi penyakit DBD dan tifus ini dibuat untuk dapat mempermudah masyarakat dalam mencari tahu kemungkinan seseorang menderita penyakit DBD atau tifus berdasarkan gejala yang dialami. Berdasarkan akuisisi pengetahuan dari tiga orang dokter, pengetahuan dalam sistem ini direpresentasikan menjadi sepuluh aturan. Mekanisme yang digunakan adalah forward chaining, sehingga proses deteksi dimulai dari input user tentang gejala penyakit yang dialami, untuk kemudian dihitung nilai belief, plausability, dan nilai keyakinan gabungan, sampai menghasilkan keluaran berupa jenis penyakit yang diderita dan tingkat keyakinan akan penyakit tersebut.Kata Kunci : DBD, tifus, Dempster-Shafer, deteksi

Comparison of layer-stacking and Dempster-Shafer theory-based methods using Sentinel-1 and Sentinel-2 data fusion in urban land cover mapping

Data fusion has shown potential to improve the accuracy of land cover mapping, and selection of the optimal fusion technique remains a challenge. This study investigated the performance of fusing Sentinel-1 (S-1) and Sentinel-2 (S-2) data, using layer-stacking method at the pixel level and Dempster-Shafer (D-S) theory-based approach at the decision level, for mapping six land cover classes in Thu Dau Mot City, Vietnam. At the pixel level, S-1 and S-2 bands and their extracted textures and indices were stacked into the different single-sensor and multi-sensor datasets (i.e. fused datasets). The datasets were categorized into two groups. One group included the datasets containing only spectral and backscattering bands, and the other group included the datasets consisting of these bands and their extracted features. The random forest (RF) classifier was then applied to the datasets within each group. At the decision level, the RF classification outputs of the single-sensor datasets within each group were fused together based on D-S theory. Finally, the accuracy of the mapping results at both levels within each group was compared. The results showed that fusion at the decision level provided the best mapping accuracy compared to the results from other products within each group. The highest overall accuracy (OA) and Kappa coefficient of the map using D-S theory were 92.67% and 0.91, respectively. The decision-level fusion helped increase the OA of the map by 0.75% to 2.07% compared to that of corresponding S-2 products in the groups. Meanwhile, the data fusion at the pixel level delivered the mapping results, which yielded an OA of 4.88% to 6.58% lower than that of corresponding S-2 products in the groups

Классификация поверхностных дефектов основного металла трубопроводов по результатам комплексной диагностики

Рассмотрены вопросы классификации поверхностных эксплуатационных объемных и плоскостных дефектов по результатам комплексной диагностики ультразвуковым методом неразрушающего контроля с применением поверхностных волн Рэлея, генерируемых электромагнитно-акустическим преобразователем, и вихретокового метода. В работе представлены результаты отбора признаков с применением дисперсионного анализа (ANOVA) и алгоритма «экстра деревья» (Extra Trees Classifier), за счет чего выбран тип вихретокового преобразователя, оптимального для классификации поверхностных дефектов. Показана неоднозначность классификации поверхностных дефектов по амплитуде ультразвукового и вихретокового сигнала, а также фазе вихретокового сигнала по отдельности. Построены модели классификации поверхностных дефектов по типам объемный и плоскостной на основе статистических методов, таких как Байесовский вывод и теория Демпстера–Шафера. Оценена работоспособность построенных моделей классификации по таким метрикам, как коэффициент Жаккара и F1-мера.Исследование выполнено за счет гранта Российского научного фонда № 22-29-00524, https://rscf.ru/project/22-29-00524/

An Investigation into Factors Affecting the Chilled Food Industry

With the advent of Industry 4.0, many new approaches towards process monitoring, benchmarking and traceability are becoming available, and these techniques have the potential to radically transform the agri-food sector. In particular, the chilled food supply chain (CFSC) contains a number of unique challenges by virtue of it being thought of as a temperature controlled supply chain. Therefore, once the key issues affecting the CFSC have been identified, algorithms can be proposed, which would allow realistic thresholds to be established for managing these problems on the micro, meso and macro scales. Hence, a study is required into factors affecting the CFSC within the scope of Industry 4.0. The study itself has been broken down into four main topics: identifying the key issues within the CFSC; implementing a philosophy of continuous improvement within the CFSC; identifying uncertainty within the CFSC; improving and measuring the performance of the supply chain. However, as a consequence of this study two further topics were added: a discussion of some of the issues surrounding information sharing between retailers and suppliers; some of the wider issues affecting food losses and wastage (FLW) on the micro, meso and macro scales. A hybrid algorithm is developed, which incorporates the analytic hierarchical process (AHP) for qualitative issues and data envelopment analysis (DEA) for quantitative issues. The hybrid algorithm itself is a development of the internal auditing algorithm proposed by Sueyoshi et al (2009), which in turn was developed following corporate scandals such as Tyco, Enron, and WorldCom, which have led to a decline in public trust. However, the advantage of the proposed solution is that all of the key issues within the CFSC identified can be managed from a single computer terminal, whilst the risk of food contamination such as the 2013 horsemeat scandal can be avoided via improved traceability