Advanced Deep Learning for Medical Image Analysis

The application of deep learning is evolving, including in expert systems for healthcare, such as disease classification. Several challenges in the use of deep-learning algorithms in application to disease classification. The study aims to improve classification to address the problem. The thesis proposes a cost-sensitive imbalance training algorithm to address an unequal number of training examples, a two-stage Bayesian optimisation training algorithm and a dual-branch network to train a one-class classification scheme, further improving classification performance

A Framework for Optimum Contour Detection

The importance of contour detection have been acknowledged by researchers worldwide, and indeed dozens of methods have been introduced. However there is no single method suit with various conditions of digital images. Most of the time, a tedious work to select best method from dozens is required only to derive the most appropriate objects contour from a digital image. Once an object contour is recognized, further image analysis process can be computed efficiently. This condition is in contrast with human visual perception which employs contour detection as a preliminary process with minimal energy consumption before conducting exhaustive visual analysis. Therefore this research aims to develop a framework to automatically detecting optimum object contour by selecting the best method for each condition of input image. Efficient energy consumption will be achieved by applying mechanism based on multi criteria decision making

Objective measurement for edge and line oriented contour detection

The importance of contour detection has been acknowledged by researchers worldwide, and indeed dozens of methods have been introduced. However there is no single method suit with various conditions of digital images. Most of the time, a tedious work to select best method from dozens is required only to derive the most appropriate objects contour from a digital image. Once an object contour is recognized, further image analysis process can be computed efficiently. This condition is in contrast with human visual perception which employs contour detection as a preliminary process with minimal energy consumption before conducting exhaustive visual analysis. Therefore this research aims to develop a framework to automatically detecting optimum object contour by selecting the best method for each condition of input image. Efficient energy consumption will be achieved by applying mechanism based on multi criteria decision making. Experimental result achieves 76.47% accuracy for detecting object composing a set of digital images

PERANCANGAN SISTEM PAKAR DIAGNOSA KERUSAKAN MESIN BOILER MIURA EH-500F MENGGUNAKAN METODE FORWARD CHAINING BERBASIS WEB

Industri pada umumnya menggunakan mesin pembuat uap bertekanan atau yang disebut ketel uap atau juga yang sering disebut boiler. Karena tuntutan produktifitas dan seriring berjalannya hari mesin boiler akan mengalami penurunan efisiensi. Pada umunya mesin boiler memiliki jadwal perawatan yang sudah ditentukan atau sudah memiliki kontrak perawatan bersama supplier mesin boiler tersebut. Namun dalam praktiknya jika suatu ketika mesin mengalami kendala atau error maka penanggung jawab mesin harus melaksanakan perbaikan yang dilakukan saat itu juga. Karena jika tidakdiperbaiki, dapat mengganggu proses produksi yang ada. Dengan Sistem Pakar menggunakan metode forward chining, pelacakan kedepan yang memulai dari sekumpulan fakta-fakta dengan mencari kaidahyang cocok dengan dugaan/hipotesa yang ada menuju kesimpulan, diharapkan mampu menangani masalah waktu dalam proses mendiagnosa kerusakan mesin boiler saat terjadi kerusakan mesin boiler.Kata kunci : Kerusakan mesin boiler, sistem pakar, forward chainin

An aggregate method for thorax diseases classification

Abstract A common problem found in real-word medical image classification is the inherent imbalance of the positive and negative patterns in the dataset where positive patterns are usually rare. Moreover, in the classification of multiple classes with neural network, a training pattern is treated as a positive pattern in one output node and negative in all the remaining output nodes. In this paper, the weights of a training pattern in the loss function are designed based not only on the number of the training patterns in the class but also on the different nodes where one of them treats this training pattern as positive and the others treat it as negative. We propose a combined approach of weights calculation algorithm for deep network training and the training optimization from the state-of-the-art deep network architecture for thorax diseases classification problem. Experimental results on the Chest X-Ray image dataset demonstrate that this new weighting scheme improves classification performances, also the training optimization from the EfficientNet improves the performance furthermore. We compare the aggregate method with several performances from the previous study of thorax diseases classifications to provide the fair comparisons against the proposed method

Geologi dan Studi Biostratigrafi Formasi Pucangan Daerah Krikilan dan Sekitarnya, Kecamatan Kalijambe Kabupaten Sragen, Provinsi Jawa Tengah

Daerah penelitian berada pada desa Krikilan dan sekitarnya Kecamatan Kalijambe Kabupaten Sragen, Jawa Tengah. Maksud dan tujuan dalam penelitian ini adalah untuk mendapatkan umur relatif, lingkungan pengendapan dan korelasi antar satuan batuan. Satuan formasi pada daerah penelitian mencakup empat formasi yaitu Formasi Pucangan (Qpp), Formasi Kabuh (Qpk), Formasi Notopuro (Qpn), dan Alluvium (Qa). Dalam penelitian ini penulis melakukan beberapa analisis antara lain analisis petrography dan analisis biostratigrafi yang meliputi analisis palinologi, dan analisis mikropaleontologi , hasil dari kedua metode analisis biostratigrafi akan mendapatkan hasil berupa informasi biostratigrafi pada lokasi penelitian. Metode yang di gunakan penulis adalah metode Lintasan Geologi untuk mendapatkan ketebalan pada daerah studi khusus serta untuk menentukan zonasi umur dan lingkungan pengendapan menggunakan analisis palinologi dan anailisis foraminifera. Pada lokasi penelitian terbagi menjadi 3 satuan geomorfologi, Satuan Lembah Antiklin Krikilan, Satuan Perbukitan Homoklin Jetiskarangpung, dan Satuan Dataran Teras Sungai Cemoro. Pada lokasi penelitian terbagi menjadi lima satuan, Satuan Batulempung hitam, Satuan Batulempung, Satuan Batupasir, Satuan Konglomerat, dan Satuan Alluvial. Pada lokasi penelitian terdapat struktur geologi yang berkembang berupa lipatan dengan jenis antiklin. Pada sekitar formasi Pucangan berkembang lingkungan yang terdiri dari mangrove hingga rawa, sabana, dan pegunungan. Formasi Pucangan terendapkan pada kala early Pleistocene dengan lingkungan pengendapan laut dangkal hingga rawa, Formasi Pucangan bawah memilikiciri litologi Batulempung hitam yang banyak terdapat fosil cangkang moluska laut dan banyak terdapat dyno flagellate, foram test lining yang mencirikan lokasi pengendapan, setelah itu terendapkan batulempung dengan banyak polen acrostichum, zonocostitesramonae, padanidites yang menandakan bahwa lingkungannya berupa mangrove hingga rawa, berdasarkan hasil determinasi fosil pada kala ini memiliki umur middle Pleistocene pada umur ini juga terdapat lingkungan berupa sabana dengan ciri terdapat polen denganjenis monoporites annulatus yang melimpah, pada kala last Pleistocene lalu terdapat lingkungan berupa pegunungan pada lingkungan ini di cirikan dengan banyak nya polendengan jenis casuarina, dan laqiapolis sebagai penanda lingkungan. Paleobatimetriformasi Pucangan mendapatkan hasil dengan lingkungan Neritic berupa inner self ataulaut dangkal yang berdekatan dengan zona transisi. Selama proses pembentukan formasi formasi di sangiran banyak faktor lain yang mengakibatkan bentukan muka bumi sangiran pada saat ini, seperti pada saat pemebntukan formasi pucangan di kala early Pleistocene regresi yang mengakibatkan pendangkalan laut hal ini karena suplay sedimen lebih tinggi daripada tempat akomodasi pada kala Pliocene – Pleistocene terjadi kenaikan aktivitas vulkanik dari gunung lawu purba, pada kala mid Pleistocene terendapkan formasi Kabuh dengan ciri litologi batupasir dan dengan lingkungan laut dangkal berubah menjadi rawa dan perluasan sabana pada lapisan ini banyak di temukan fosil hewan danfosil manusia purba, lalu late Pleistocene terendapkan formasi Notopuro dengan cirilitologi konglomerat pada lapisan ini dapat sebagai kunci aktivitas vulkanik di sangiran meningkat drastis, lalu pada kala Holocene terjadi pengikisan di teras teras sungai dan terendapakan endapan aluviall di kelokan – kelokan sungai proses ini masih terjadi hingga saat ini

A scrutinized outliers rate for one class classification of green landscape

The Green area is unarguably the most crucial part of the Sustainable Development Goals (SDG). The importance of the green regions includes preserving biodiversity and stabilizing climates. However, sophisticated mapping tool such as LIDAR is considerably expensive and also not accessible by people in common. This study offers the one-class classification of green regions using authentic drone images. The research manages to scrutinize the outliers rate based on literature. The methodology is cheaper and very applicable-the classification results in 95 % accuracy and 93 % weighted-average F1 score. The technology behind the method includes a lightweight neural-network architecture, a weighted Huber loss and a final Softmax function. The results show that this study is promising for future use

A scrutinized outliers rate for one class classification of green landscape

The Green area is unarguably the most crucial part of the Sustainable Development Goals (SDG). The importance of the green regions includes preserving biodiversity and stabilizing climates. However, sophisticated mapping tool such as LIDAR is considerably expensive and also not accessible by people in common. This study offers the one-class classification of green regions using authentic drone images. The research manages to scrutinize the outliers rate based on literature. The methodology is cheaper and very applicable-the classification results in 95 % accuracy and 93 % weighted-average F1 score. The technology behind the method includes a lightweight neural-network architecture, a weighted Huber loss and a final Softmax function. The results show that this study is promising for future use

Characterisation of Arabica Coffee Pulp - Hay from Kintamani - Bali as Prospective Biogas Feedstocks

The huge amount of coffee pulp waste is an environmental problem. Anaerobic fermentation is one of the alternative solutions. However, availability of coffee pulp does not appear for year-round, whereas biogas needs continuous feedstocks for digester stability. This research uses coffee pulp from Arabica Coffee Factory at Mengani, Kintamani, Bali–Indonesia. The coffee pulp was transformed into coffee pulp-hay product by sun drying for preservations to extend the raw materials through the year. Characterization of coffee pulp-hay was conducted after to keep for 15 mo for review the prospect as biogas feedstocks. Several parameters were analyzed such as C/N ratio, volatile solids, carbohydrate, protein, fat, lignocellulose content, macro-micro nutrients, and density. The review results indicated that coffee pulp-hay is prospective raw material for biogas feedstock. This well-proven preservation technology was able to fulfill the continuous supply. Furthermore, some problems were found in the recent preliminary experiment related to the density and fungi growth in the conventional laboratory digester. Further investigation was needed to implement the coffee pulp – hay as biogas feedstocks

Characterisation of Arabica Coffee Pulp - Hay from Kintamani - Bali as Prospective Biogas Feedstocks

The huge amount of coffee pulp waste is an environmental problem. Anaerobic fermentation is one of the alternative solutions. However, availability of coffee pulp does not appear for year-round, whereas biogas needs continuous feedstocks for digester stability. This research uses coffee pulp from Arabica Coffee Factory at Mengani, Kintamani, Bali–Indonesia. The coffee pulp was transformed into coffee pulp-hay product by sun drying for preservations to extend the raw materials through the year. Characterization of coffee pulp-hay was conducted after to keep for 15 mo for review the prospect as biogas feedstocks. Several parameters were analyzed such as C/N ratio, volatile solids, carbohydrate, protein, fat, lignocellulose content, macro-micro nutrients, and density. The review results indicated that coffee pulp-hay is prospective raw material for biogas feedstock. This well-proven preservation technology was able to fulfill the continuous supply. Furthermore, some problems were found in the recent preliminary experiment related to the density and fungi growth in the conventional laboratory digester. Further investigation was needed to implement the coffee pulp – hay as biogas feedstocks