Application of Artificial Intelligence in predicting earthquakes: state-of-the-art and future challenges

Predicting the time, location and magnitude of an earthquake is a challenging job as an earthquake does not show specific patterns resulting in inaccurate predictions. Techniques based on Artificial Intelligence (AI) are well known for their capability to find hidden patterns in data. In the case of earthquake prediction, these models also produce a promising outcome. This work systematically explores the contributions made to date in earthquake prediction using AI-based techniques. A total of 84 scientific research papers, which reported the use of AI-based techniques in earthquake prediction, have been selected from different academic databases. These studies include a range of AI techniques including rule-based methods, shallow machine learning and deep learning algorithms. Covering all existing AI-based techniques in earthquake prediction, this paper provides an account of the available methodologies and a comparative analysis of their performances. The performance comparison has been reported from the perspective of used datasets and evaluation metrics. Furthermore, using comparative analysis of performances the paper aims to facilitate the selection of appropriate techniques for earthquake prediction. Towards the end, it outlines some open challenges and potential research directions in the field

Social group optimization–assisted Kapur's entropy and morphological segmentation for automated detection of COVID-19 infection from computed tomography images

The coronavirus disease (COVID-19) caused by a novel coronavirus, SARS-CoV-2, has been declared a global pandemic. Due to its infection rate and severity, it has emerged as one of the major global threats of the current generation. To support the current combat against the disease, this research aims to propose a machine learning–based pipeline to detect COVID-19 infection using lung computed tomography scan images (CTI). This implemented pipeline consists of a number of sub-procedures ranging from segmenting the COVID-19 infection to classifying the segmented regions. The initial part of the pipeline implements the segmentation of the COVID-19–affected CTI using social group optimization–based Kapur's entropy thresholding, followed by k-means clustering and morphology-based segmentation. The next part of the pipeline implements feature extraction, selection, and fusion to classify the infection. Principle component analysis–based serial fusion technique is used in fusing the features and the fused feature vector is then employed to train, test, and validate four different classifiers namely Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine with Radial Basis Function, and Decision Tree. Experimental results using benchmark datasets show a high accuracy (> 91%) for the morphology-based segmentation task; for the classification task, the KNN offers the highest accuracy among the compared classifiers (> 87%). However, this should be noted that this method still awaits clinical validation, and therefore should not be used to clinically diagnose ongoing COVID-19 infection

An Energy conserving routing scheme for wireless body sensor nanonetwork communication

Current developments in nanotechnology make electromagnetic communication possible at the nanoscale for applications involving body sensor networks (BSNs). This specialized branch of wireless sensor networks, drawing attention from diverse fields, such as engineering, medicine, biology, physics, and computer science, has emerged as an important research area contributing to medical treatment, social welfare, and sports. The concept is based on the interaction of integrated nanoscale machines by means of wireless communications. One key hurdle for advancing nanocommunications is the lack of an apposite networking protocol to address the upcoming needs of the nanonetworks. Recently, some key challenges have been identified, such as nanonodes with extreme energy constraints, limited computational capabilities, terahertz frequency bands with limited transmission range, and so on, in designing protocols for wireless nanosensor networks. This work proposes an improved performance scheme of nanocommunication over terahertz bands for wireless BSNs making it suitable for smart e-health applications. The scheme contains - a new energy-efficient forwarding routine for electromagnetic communication in wireless nanonetworks consisting of hybrid clusters with centralized scheduling; a model designed for channel behavior taking into account the aggregated impact of molecular absorption, spreading loss, and shadowing; and an energy model for energy harvesting and consumption. The outage probability is derived for both single and multilinks and extended to determine the outage capacity. The outage probability for a multilink is derived using a cooperative fusion technique at a predefined fusion node. Simulated using a nano-sim simulator, performance of the proposed model has been evaluated for energy efficiency, outage capacity, and outage probability. The results demonstrate the efficiency of the proposed scheme through maximized energy utilization in both single and multihop communications; multisensor fusion at the fusion node enhances the link quality of the transmission

Advances in crowd analysis for urban applications through urban event detection

The recent expansion of pervasive computing technology has contributed with novel means to pursue human activities in urban space. The urban dynamics unveiled by these means generate an enormous amount of data. These data are mainly endowed by portable and radio-frequency devices, transportation systems, video surveillance, satellites, unmanned aerial vehicles, and social networking services. This has opened a new avenue of opportunities, to understand and predict urban dynamics in detail, and plan various real-time services and applications in response to that. Over the last decade, certain aspects of the crowd, e.g., mobility, sentimental, size estimation and behavioral, have been analyzed in detail and the outcomes have been reported. This paper mainly conducted an extensive survey on various data sources used for different urban applications, the state-of-the-art on urban data generation techniques and associated processing methods in order to demonstrate their merits and capabilities. Then, available open-access crowd data sets for urban event detection are provided along with relevant application programming interfaces. In addition, an outlook on a support system for urban application is provided which fuses data from all the available pervasive technology sources and finally, some open challenges and promising research directions are outlined

Advanced power routing framework for optimal economic operation and control of solar photovoltaic-based islanded microgrid

© 2019 Institution of Engineering and Technology. All rights reserved. Energy sharing through a microgrid (MG) is essential for islanded communities to maximise the use of distributed energy resources (DERs) and battery energy storage systems (BESSs). Proper energy management and control strategies of such MGs can offer revenue to prosumers (active consumers with DERs) by routing excess energy to their neighbours and maintaining grid constraints at the same time. This paper proposes an advanced power-routing framework for a solarphotovoltaic (PV)-based islanded MG with a central storage system (CSS). An optimisation-based economic operation for the MG is developed that determines the power routing and energy sharing in the MG in the day-ahead stage. A modified droop controller-based real-time control strategy has been established that maintains the voltage constraints of the MG. The proposed power-routing framework is verified via a case study for a typical islanded MG. The outcome of the optimal economic operation and a controller verification of the proposed framework are presented to demonstrate the effectiveness of the proposed powerrouting framework. Results reveal that the proposed framework performs a stable control operation and provides a profit of 57 AU$/day at optimal conditions

Deep learning in mining biological data

Recent technological advancements in data acquisition tools allowed life scientists to acquire multimodal data from different biological application domains. Categorised in three broad types (i.e., images, signals, and sequences), these data are huge in amount and complex in nature. Mining such enormous amount of data for pattern recognition is a big challenge and requires sophisticated data intensive machine learning techniques. Artificial neural network based learning systems are well known for their pattern recognition capabilities and lately their deep architectures - known as deep learning (DL) - have been successfully applied to solve many complex pattern recognition problems. To investigate how DL - especially its different architectures - has contributed and utilised in the mining of biological data pertaining to those three types, a meta analysis has been performed and the resulting resources have been critically analysed. Focusing on the use of DL to analyse patterns in data from diverse biological domains, this work investigates different DL architectures' applications to these data. This is followed by an exploration of available open access data sources pertaining to the three data types along with popular open source DL tools applicable to these data. Also, comparative investigations of these tools from qualitative, quantitative, and benchmarking perspectives are provided. Finally, some open research challenges in using DL to mine biological data are outlined and a number of possible future perspectives are put forward

STRATEGI PEMASARAN JASA PENDIDIKAN DALAM MENINGKATKAN ANIMO MASYARAKAT DI PONDOK PESANTRREN AL KINANAH KOTA JAMBI

Penelitian ini membahas mengenai strategi pemasaran jasa pendidikan yang ada di Pondok Pesantren Modern Al Kinanah Kota Jambi. Tujuan dari skripsi ini adalah untuk mengetahui mengenai strategi pemasaran yang digunakan oleh Pondok Pesantren Al Kinanah Kota Jambi. Penelitian ini di muat dalam bentuk deskriptif kualitatif, yang menggunakan observasi, wawancara serta dokumentasi dalam pengumpulan datanya. Berdasarkan temuan penulis, dalam melakukan pemasarannya, Pondok Pesantren Modern Al Kinanah Kota Jambi menggunakan strategi bauran pemasaran. Strategi bauran pemasaran yang digunakan oleh Pondok Pesantren Modern Al Kinanah Kota Jambi meliputi unsur 5 P yaitu produk (product), yaitu dari segi kemahiran bahasa inggris dan arab, harga (price), yaitu dengan harga yang masih standard rata-rata di pasaran, lokasi (place), Pondok Pesantren Al Kinanah sendiri memiliki lokasi yang strategis yaitu di wilayah kota jambi, orang (people), memiliki sumber daya manusia (guru, pengasuh dan staf) yang berkompeten di bidangnya masing-masing, serta promosi (promotion), yaitu dilakukan dengan menggunakan penyebaran brosur, kalender, pamflet dan konten-konten di media sosial. Faktor pendukung dalam melakukan pemasarannya yaitu berupa promosi yang dilakukan dari mulut ke mulut oleh para orang tua. Sedangkan faktor penghambatnya adalah karna minimnya biaya. Keberhasilan strategi pemasaran yang dilakukan oleh Pondok Pesantren Modern Al Kinanah Kota Jambi dibuktikan dengan meningkatnya animo masyarakat di pondok tersebut

ABOT: an open-source online benchmarking tool for machine learning-based artefact detection and removal methods from neuronal signals

Brain signals are recorded using different techniques to aid an accurate understanding of brain function and to treat its disorders. Untargeted internal and external sources contaminate the acquired signals during the recording process. Often termed as artefacts, these contaminations cause serious hindrances in decoding the recorded signals; hence, they must be removed to facilitate unbiased decision-making for a given investigation. Due to the complex and elusive manifestation of artefacts in neuronal signals, computational techniques serve as powerful tools for their detection and removal. Machine learning (ML) based methods have been successfully applied in this task. Due to ML’s popularity, many articles are published every year, making it challenging to find, compare and select the most appropriate method for a given experiment. To this end, this paper presents ABOT (Artefact removal Benchmarking Online Tool) as an online benchmarking tool which allows users to compare existing ML-driven artefact detection and removal methods from the literature. The characteristics and related information about the existing methods have been compiled as a knowledgebase (KB) and presented through a user-friendly interface with interactive plots and tables for users to search it using several criteria. Key characteristics extracted from over 120 articles from the literature have been used in the KB to help compare the specific ML models. To comply with the FAIR (Findable, Accessible, Interoperable and Reusable) principle, the source code and documentation of the toolbox have been made available via an open-access repository

Efisiensi Reproduksi dan Produksi Susu Pada Berbagai Periode Laktasi Sapi Perah Friesian Holstein (FH)

Penelitian dengan tujuan untuk mengetahui efisiensi reproduksi dan produksi susu pada berbagai periode laktasi sapi perah Friesian Holstein telah dilaksanakan dari bulan mei sampai September 2017 di PT. Greenfields Indonesia. Metode yang digunakan adalah studi kasus dengan menggunakan data skunder berupa catatan reproduksi dan performans produksi susu sapi perah. Materi yang digunakan adalah catatan reproduksi dan produksi dari 473 ekor dengan rincian periode laktasi I, II, III dan IV masing-masing sebnayak 100 ekor dan periode laktasi V sebanyak 73 ekor. Variabel yang di amati variabel performans reproduksi : S/C, CI dan DO, dan performans Produksi : Lama kering, masa laktasi, produksi susu 305 hari, produksi susu harian, peak milk dan peak dim. Data sifat kuantitatif yang diperoleh dihitung simpangan bakunya, untuk mengetahui pengaruh periode laktasi menggunakan ANOVA dengan model pola searah dilanjut dengan uji BNT. Hasil analisis Pada periode laktasi I dan II diperoleh nilai S/C sebesar 2,6±1,3 dan 2,1±1,1 lebih rendah dibanding dengan periode laktasi III, IV dan V yaitu 3,7±2,0, 4,2±2,1 dan 4,1±2,3. DO pada laktasi I dan II yaitu 118,3±49,7 hari dan 97,6±35,1hari lebih rendah dibanding dengan periode laktasi III, IV dan V yaitu 153,5±65,8hari, 168,2±81,5hari dan 159,2±74,7hari. Calving Interval yang lebih rendah pada periode laktasi II yaitu 365,8±36,9 hari dibanding periode laktasi III, IV dan V yaitu 393,8±54,5, 406,1±78,3 dan 401,3±65,1 hari. ). Produksi susu 305 hari pada periode laktasi I, II, III yaitu 10174,00±1492,83 l, 10232,90±1036,62 l, 9209,20±977,90 l lebih tinggi dibanding dengan periode laktasi IV dan V yaitu 8426,00±1414,27 l dan 7902,05±2184,27 l. Sedangkan produksi susu harian pada periode laktasi I, II dan III yaitu 31,71±2,21 l, 32,00±5,13 l dan 31,06±5,13 l lebih tinggi dibanding dengan periode laktasi IV dan V yaitu 29,40±44 l dan 25,86±9,48 l. Disimpulkan bahwa aspek efisiensi reproduksi menunjukkan sapi FH periode laktasi I dan II lebih baik dari pada sapi FH periode laktasi III, IV dan V. Produksi susu sapi FH periode laktasi I, II dan III mempunyai produksi susu yang lebih tinggi dari pada periode laktasi IV dan V. Disarankan untuk meningkatkan produksi susu dan efisiensi reproduksi perlu perbaikan manajemen bibit, recording dan tatalaksana pemeliharaan yang baik

Towards a heterogeneous mist, fog, and cloud based framework for the Internet of Healthcare Things

Rapid developments in the fields of information and communication technology and microelectronics allowed seamless interconnection among various devices letting them to communicate with each other. This technological integration opened up new possibilities in many disciplines including healthcare and well-being. With the aim of reducing healthcare costs and providing improved and reliable services, several healthcare frameworks based on Internet of Healthcare Things (IoHT) have been developed. However, due to the critical and heterogeneous nature of healthcare data, maintaining high quality of service (QoS) -in terms of faster responsiveness and data-specific complex analytics -has always been the main challenge in designing such systems. Addressing these issues, this paper proposes a five-layered heterogeneous mist, fog, and cloud based IoHT framework capable of efficiently handling and routing (near-)real-time as well as offline/batch mode data. Also, by employing software defined networking and link adaptation based load balancing, the framework ensures optimal resource allocation and efficient resource utilization. The results, obtained by simulating the framework, indicate that the designed network via its various components can achieve high QoS, with reduced end-to-end latency and packet drop rate, which is essential for developing next generation e-healthcare systems