Art Approaches Research Articles

Feature Selection with Deep Reinforcement Learning for Intrusion Detection System

An intrusion detection system (IDS) becomes an important tool for ensuring security in the network. In recent times, machine learning (ML) and deep learning (DL) models can be applied for the identification of intrusions over the network effectively. To resolve the security issues, this paper presents a new Binary Butterfly Optimization algorithm based on Feature Selection with DRL technique, called BBOFS-DRL for intrusion detection. The proposed BBOFSDRL model mainly accomplishes the recognition of intrusions in the network. To attain this, the BBOFS-DRL model initially designs the BBOFS algorithm based on the traditional butterfly optimization algorithm (BOA) to elect feature subsets. Besides, DRL model is employed for the proper identification and classification of intrusions that exist in the network. Furthermore, beetle antenna search (BAS) technique is applied to tune the DRL parameters for enhanced intrusion detection efficiency. For ensuring the superior intrusion detection outcomes of the BBOFS-DRL model, a wide-ranging experimental analysis is performed against benchmark dataset. The simulation results reported the supremacy of the BBOFS-DRL model over its recent state of art approaches.

‘Senior Citizen’: Ironic Depiction of West Africa’s Herder-Famer Crisis in a Conceptual Ceramics Exposition

This paper proffers an art-speech of the farmer-herder crisis in Nigeria from the standpoint of the conceptual ceramic’s exposition. Using the figurative construct of ‘senior citizen’ and the visual art form of conceptual ceramics, this paper creates a sort of metaphorized analogy that depicts an allegory of Nigeria’s perennial farmer-herder crisis scenario, where the wellbeing of the citizenry appears to have been subordinated to those of the herders and their herds. The uncommon ceramic art approach that negates traditional production methods has been explored in this study to interrogate the outlined concerns. This process includes the pinching of plastic clay, placing it in the palm, rolling the clay with both palms into a stick-like shape, placing the stick at the foot of the fingers, tightly compressing the clay by closing the fists, and finally releasing the clay to reveal the product. This style that has been termed fragmented clay palm press terracotta on canvas proved effective and fluid enough to achieve the essence of this study. The intriguing situation of Nigeria’s socio-political metaphor vis-à-vis the prevailing farmer-herder conundrum is explored in an attempt to foreground the existential irony that this awkward situation portends.

Deep learning framework for cancer type classification using genome sequencing with AlexaNet

Tumors and cancers are the major diseases in humans and early prediction of cancers can save human lives. Cancer genome sequencing involves sequencing the whole genome of a group of cancer cells. It is a chemical laboratory approach for describing and identifying cancerous RNA or DNA cells.Therefore, this work develops adeep learning algorithm for classifying the cancer diseases from genomic sequences. Initially, Ada-boost descriptor is used to extract the deep features from the dataset, which identifies the inter and intra relationship between multiple correlated cancers. Then, deep learning based AlexNet model is used to classify the different types of cancers. Further, the simulation results shows that the proposed method resulted in superior performance as compared to state of art approaches

Improved Rat Swarm Based Multihop Routing Protocol for Wireless Sensor Networks

Wireless sensor networks (WSNs) encompass a massive set of sensor nodes, which are self-configurable, inexpensive, and compact. The sensor nodes undergo random deployment in the target area and transmit data to base station using inbuilt transceiver. For reducing energy consumption and lengthen lifetime of WSN, multihop routing protocols can be designed. This study develops an improved rat swarm optimization based energy aware multi-hop routing (IRSO-EAMHR) protocol for WSN. An important intention of the IRSO-EAMHR method is for determining optimal routes to base station (BS) in the clustered WSN. Primarily, a weighted clustering process is performed to group the nodes into clusters and select cluster heads (CHs). Next, the IRSO-EAMHR approach derives a fitness function containing three input parameters (residual energy, distance, and node degree) for routing process. The IRSO technique was designed by the integration of Levy movement concepts into the traditional RSO algorithm. The experimental result analysis of the IRSO-EAMHR technique is carried out and the outcomes are examined in various aspects. The simulation outcomes demonstrate the promising performance of the IRSO-EAMHR technique over the recent state of art approaches.

Automated Red Deer Algorithm with Deep Learning Enabled Hyperspectral Image Classification

Hyperspectral (HS) image classification is a hot research area due to challenging issues such as existence of high dimensionality, restricted training data, etc. Precise recognition of features from the HS images is important for effective classification outcomes. Additionally, the recent advancements of deep learning (DL) models make it possible in several application areas. In addition, the performance of the DL models is mainly based on the hyperparameter setting which can be resolved by the design of metaheuristics. In this view, this article develops an automated red deer algorithm with deep learning enabled hyperspectral image (HSI) classification (RDADL-HIC) technique. The proposed RDADL-HIC technique aims to effectively determine the HSI images. In addition, the RDADL-HIC technique comprises a NASNetLarge model with Adagrad optimizer. Moreover, RDA with gated recurrent unit (GRU) approach is used for the identification and classification of HSIs. The design of Adagrad optimizer with RDA helps to optimally tune the hyperparameters of the NASNetLarge and GRU models respectively. The experimental results stated the supremacy of the RDADL-HIC model and the results are inspected interms of different measures. The comparison study of the RDADL-HIC model demonstrated the enhanced performance over its recent state of art approaches.

Added value of thermodynamic white-box data for HCCI combustion prediction

Homogeneous charge compression ignition (HCCI) is a promising combustion process to reducing both greenhouse gas and pollutant emissions in the transportation sector. The combustion starts when the thermodynamic state of the cylinder charge reaches the auto-ignition properties of the fuel, which is challenging to model. In addition, the strong cyclic coupling due to required exhaust gas recirculation leads to a significant dependence on the combustion of the previous cycle.This work focuses on identifying accurate models to predict the combustion process for potential use in an MPC. In this work HCCI is achieved using negative valve overlap (NVO) on a fully flexible valve train. The fluctuating composition and temperature of the cylinder charge is not directly measurable and not taken into account in state of the art approaches. It is assumed that it has a decisive influence on the start of combustion. Hence, in this work, the thermodynamic state is modeled by a detailed physical white-box model, which is used as an additional input for the combustion prediction. To investigate the added value of the thermodynamic state data, an Artificial neural network (ANN) is trained to predict combustion both with and without the thermodynamic state data from the white-box model. The root-mean-square error of the ANN including thermodynamic data is reduced by 12.7%. Thus, by adding the thermodynamic white-box data, the HCCI combustion prediction can be significantly improved.

Critical Thinking Skills Using Science Technology Religion Engineering Arts and Mathematics (STREAM) Approach on Ecosystem Materials

One of the habits of mind developed in the 21st century is critical thinking skills. This study aims to describe the increase in students' critical thinking skills after learning using the approach of science, technology, religion, engineering, art, and mathematics (STREAM) on ecosystem materials. This research was conducted in one of the public high schools in Bandung Regency with a pre-experiment method whit one group pretest posttest design. The research sample was 35 students who were selected through purposive sampling technique. The instrument used is in the form of test questions that refer to the indicators of critical thinking skills as follows: giving simple explanations, building basic skills, concluding, providing further explanations and developing strategies and tactics. The data analysis technique used is to perform statistical tests. Based on the results of the study, the average value of the N-Gain critical thinking skills of students in classes using the STREAM approach was 67% in the medium category. The highest increase is in the indicator of setting strategy and tactics by 67% in the medium category. The lowest increase is the indicator providing a simple explanation of 49% in the medium category. The results of the study indicate that the STREAM approach can improve students' critical thinking skills

Plotter art and new approaches in this field

Plotterlar günümüzde tekrar popüler hale gelen ancak geçmişi bilgisayar sanatının başlangıcına kadar uzanan bir yazıcı türüdür. Yaygın olarak kullanmakta olduğumuz lazer yazıcılardan farklı olarak görüntüleri kâğıt üzerine üst üste birçok nokta çizerek oluşturmak yerine, sürekli çizgiler çizerek meydana getirirler. Çizim malzemesi olarak toner değil kalem, fırça gibi araçlar kullanırlar. Plotterlar 60 yıl kadar önce bilgisayar sanatı ile uğraşan ilk sanatçıların eserlerini dijital ekrandan fiziksel çıktılara aktarmak üzere kullandıkları bir araçtır. Bu yazıcılar özellikle algoritmik sanat adı altında eserlerini üreten Algoristler adlı grup tarafından oldukça kabul görmüş, grup tarafından dönemin imkân verdiği koşullarda çeşitli denemeler yapılmıştır. Zaman içerisinde ise baskı teknolojisindeki gelişmeler sonucunda daha işlevsel seçeneklerin oluşması ile birlikte kullanım alanını kaybetmiştir. Ancak son yıllarda özellikle birkaç ticari firmanın bu araçların bilgisayar destekli yeni türevlerini satışa sunması ve bunların tasarımcılar için ulaşılabilir maliyette ve boyutta olması, plotter sanatı için yeni bir deneysel zemin hazırlamıştır. Bu araştırma tasarımcılara modern plotterlar, plotter sanatı ve günümüzdeki plotterların kullanım alanları hakkında bilgi vermek üzere yapılmıştır. Araştırmada nitel araştırma yöntemlerinden doküman analizi kullanılmış, yakın tarihte plotter sanatı başlığı altında paylaşılan tasarımlar, kullanılan yöntem ve malzeme türüne göre sınıflandırılmıştır. Bu yazıcı türleri ile üretilebilecek eserler ve tekniğin avantajları hakkında ulaşılan sonuçlar araştırmanın son bölümünde paylaşılmıştır

Upcycling Fabric Waste for Home Decoration by Implementing Islamic Art Approach

Humanity's activities cause the destruction that occurs in the world as a caliphate, as stated in Surah Ar-Rum (30), verse 41 of the Quran. One of the products that might harm the ecosystem is waste. Various countries, including the Indonesian Government, have competed with numerous initiatives and policies to save environmental sustainability. The author of this study provided training on how to conserve the environment by processing trash, particularly fabric waste. Bina Nusantara University's community development initiative invites numerous assisted MSMEs to participate in a waste management training session. Participants are instructed on how to transform fabric waste into useful interior products. The techniques conducted start from joining fabric waste with patchwork, quilting, and applique connection techniques. From the connection results, the product's value can be increased again by adding a digital screen printing technique. Islamic Art is one of the many motifs that can be used. Implementing Islamic geometric patterns and floral, vegetal, and calligraphy designs on interior accessories are some examples of Islamic art motifs. Apart from being able to decorate the interior of the home, particularly by giving it an Islamic feel, the objective of creating this product is to conserve the environment through upcycling technology.

A scalable and energy efficient GPU thread map for [formula omitted]-simplex domains

This work proposes a new GPU thread map for m-simplex domains that improves its speedup along with the m-dimension and is energy efficient compared to other state of the art approaches. The main contributions of this work are (i) the formulation of an improved new block-space map H:Zm↦Zm for regular orthogonal simplex domains, which is analyzed in terms of resource usage, and (ii) the experimental evaluation in terms of speedup and energy efficiency with respect to a bounding box approach. Results from the analysis show that H has a potential speedup of up to 2× and 6× for 2 and 3-simplices, respectively. Experimental evaluation shows that H is competitive for 2-simplices, reaching 1.2×∼2.0× of speedup for different tests, which is on par with the fastest state of the art approaches. For 3-simplices H reaches up to 1.3×∼6.0× of speedup making it the fastest. The extension of H to higher dimensional m-simplices is feasible and has a potential speedup that scales as m! given a proper selection of parameters r,β which are the scaling and replication factors of the geometry, respectively. In terms of energy consumption, although H is among the highest in power consumption, it compensates by its short duration, making it one of the most energy efficient approaches. The results of this work show that H is a scalable and energy efficient map that improves the efficiency of GPU applications that need to process m-simplex domains, such as Cellular Automata or PDE simulations, among others.

INFUTPEDIA: Media Pembelajaran Berbasis Android dengan Pendekatan STEAM (Science, Technology, Engineering, Art, and Mathematics) Sebagai Alternatif Pemecahan Masalah di Indonesia

Indonesia's digital education ecosystem has very diverse control rooms. Several scenarios or strategies must be used to be implemented during the transformation of digital education after the COVID-19 pandemic. The existence of the COVID-19 pandemic for more than two years has provided space for teachers to carry out innovations, especially digital innovations. One of the results of innovations that have been successfully utilized to date is learning media. From the various learning activities carried out, there are various approaches used by teachers so that learning can be understood by students and is more meaningful, such as science, technology, engineering, art, and mathematics (STEAM) approaches to solve problems. In this study, the Research and Development (R&D) method was used. Learning based on the STEAM approach aims to increase students' understanding and knowledge of mathematics, science, and technology. As for the art elements, it is hoped that students will be accustomed to solving a problem in a creative way.In order to improve student skills, creativity and sensitivity to environment, the INFUTPEDIA (Indonesia for Future) innovation is here. INFUTPEDIA is an Android-based learning media with the STEAM approach as an alternative or solution to solving problems in Indonesia. Learning steps with the STEAM approach include: find identify problems and solutions, envision products, plan products, and manufacture and test products. So that this project-based exercise will train students to be able to solve problems and make decisions for the progress of Indonesia.

An Artful Approach: Reflecting on the Past to Forewarn and Foretell

On the wall of the Rijksmuseum hangs Rembrandt’s Jeremiah Lamenting the Destruction of Jerusalem. This work of art captures a weary prophet who is now grieving the previously forewarned and preventable suffering of his people and the destruction of their land. Acclaimed as the Master of Light, Rembrandt’s chiaroscuro achieves a dramatic effect. The dark, seemingly dream-like backdrop, depicts the calamitous consequences of Jeremiah’s unheeded warnings. His people bore cultural, ethnic, linguistic, and religious oppression; they suffered such horrors as colonialism as they and their land were exploited. Yet, beams of light burst through the darkness; Rembrandt illuminated Jeremiah with a seemingly divine touch of hope <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> . The prophet is dejected but not in despair. Historical narratives teach us Jeremiah never lost hope; decades of suffering did not breed bitterness as he held fast to the promise of justice and restoration for his people <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , <xref ref-type="bibr" rid="ref3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[3]</xref> , <xref ref-type="bibr" rid="ref4" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[4]</xref> .

The Vanitas of Sound Art

Abstract The author applies the concept of fermentation to sound through the prism of saturation and sonification, and studies the fermentation process as a medium and approach in sound art. He traces this medium in the works of Alexey Grachev, Rumore Bianco, Wouter Van Veldhoven, Sam Conran, and Vasily Bakanov. The article examines the history of the CYLAND Audio Archive and the formula for archiving, cataloging, and exhibiting works of sound art.

A Survey on the State of Art Approaches for Disease Detection in Plants

Agriculture is the main factor for economy and contributes to GDP. The growth of the economy of many countries is based on agriculture. As a result, the yield factor, quality and volume of agricultural products, play a critical role in economic development. Plant diseases and pests have become a major determinant of crop yields throughout the years, as such illnesses in plants offer a serious threat and impediment to higher yields or production in the agriculture industry. As a result, From the outset, it becomes the major duty to correctly monitor the plants, to detect diseases thoroughly, and to determine methods of controlling or monitoring these plant diseases pests in order to achieve a higher rate of production growth and minimal crop damage. Using machine vision, deep learning methods and tools for extracting and classifying features, It could be possible to build a reliable disease detection system. Numerous researchers have created and deployed various ways for detecting plant diseases and pests. The potential of these methods has been examined in this work.

Integrating professional machine translation literacy and data literacy

AbstractThe data-driven paradigm of neural machine translation is a powerful translation technology based on state-of-the art approaches in artificial intelligence research. This technology is employed extensively in the professional translation process, requiring an adequate degree of machine translation literacy on the part of professional translators. At the same time, the increasing datafication to be observed in modern society – both in private as well as in professional contexts – contributes to the rise in prominence of another digital literacy, i. e., data literacy, which is also of high relevance with regard to data-driven machine translation. The present paper analyses and discusses in detail the individual dimensions and subdimensions of professional machine translation literacy and data literacy and attempts to integrate the two concepts. It thereby lays the theoretical foundation for a didactic project concerned with teaching data literacy in its machine translation-specific form to students in the fields of translation and specialised communication studies.

An Ensemble of Deep Learning Enabled Brain Stroke Classification Model in Magnetic Resonance Images.

Brain stroke is a major cause of global death and it necessitates earlier identification process to reduce the mortality rate. Magnetic resonance imaging (MRI) techniques is a commonly available imaging modality used to diagnose brain stroke. Presently, machine learning (ML) and deep learning (DL) models can be extremely utilized for disease detection and classification processes. Amongst the available approaches, the convolutional neural network (CNN) models have been widely used for computer vision and image processing issues such as ImageNet, facial detection, and digit classification. In this article, a novel computer aided diagnosis (CAD) based brain stroke detection and classification (CAD-BSDC) model has been developed for MRI images. The proposed CAD-BSDC technique aims in classifying the provided MR brain image as normal or abnormal. The CAD-BSDC technique involves different subprocesses such as preprocessing, feature extraction, and classification. Firstly, the input image undergoes preprocessing using adaptive thresholding (AT) technique for improving the image quality. Followed by, an ensemble of feature extractors such as MobileNet, CapsuleNet, and EfficientNet models are used. Besides, the hyperparameter tuning of the deep learning models takes place using the improved dragonfly optimization (IDFO) algorithm. Moreover, satin bowerbird optimization (SBO) based stacked autoencoder (SAE) is used for the classification of brain stroke. The design of optimal SAE using the SBO algorithm shows the novelty of the work. The performance of the presented technique was validated utilizing benchmark dataset which includes T2-weighted MR brain image collected from the axial axis with size of 256 × 256. The simulation outcomes indicated the promising efficiency of the proposed CAD-BSDC technique over the latest state of art approaches in terms of various performance measures.

Runtime reconfiguration of data services for dealing with out-of-range stream fluctuation in cloud-edge environments

The integration of cloud and IoT edge devices is of significance in reducing the latency of IoT stream data processing by moving services closer to the edge-end. In this connection, a key issue is to determine when and where services should be deployed. Common service deployment strategies used to be static based on the rules defined at the design time. However, dynamically changing IoT environments bring about unexpected situations such as out-of-range stream fluctuation, where the static service deployment solutions are not efficient. In this paper, we propose a dynamic service deployment mechanism based on the prediction of upcoming stream data. To effectively predict upcoming workloads, we combine the online machine learning methods with an online optimization algorithm for service deployment. A simulation-based evaluation demonstrates that, compared with those state-of-the art approaches, the approach proposed in this paper has a lower latency of stream processing.

Conserved Water Networks Identification for Drug Design Using Density Clustering Approaches on Positional and Orientational Data.

This work describes the development and testing of a method for the identification and classification of conserved water molecules and their networks from molecular dynamics (MD) simulations. The conserved waters in the active sites of proteins influence protein-ligand binding. Recently, several groups have argued that a water network formed from conserved waters can be used to interpret the thermodynamic signature of the binding site. We implemented a novel methodology in which we apply the complex approach to categorize water molecules extracted from the MD simulation trajectories using clustering approaches. The main advantage of our methodology as compared to current state of the art approaches is the inclusion of the information on the orientation of hydrogen atoms to further inform the clustering algorithm and to classify the conserved waters into different subtypes depending on how strongly certain orientations are preferred. This information is vital for assessing the stability of water networks. The newly developed approach is described in detail as well as validated against known results from the scientific literature including comparisons with the experimental data on thermolysin, thrombin, and Haemophilus influenzae virulence protein SiaP as well as with the previous computational results on thermolysin. We observed excellent agreement with the literature and were also able to provide additional insights into the orientations of the conserved water molecules, highlighting the key interactions which stabilize them. The source code of our approach, as well as the utility tools used for visualization, are freely available on GitHub.

Experimental performance analysis of cloud resource allocation framework using spider monkey optimization algorithm

SummaryThe cloud services demand has increased exponentially in the last decade due to its plethora of services. It becomes a significant platform to compute large and diverse applications over the internet. On the contrary, on‐demand resource allocation to a variety of applications becomes a serious issue due to dynamic workload conditions and uncertainty in the cloud environment. Several existing state of art techniques often fails to allocate the optimal resources to forthcoming demands, leading to an imbalance workload over cloud platform, degrading the performance. This article introduces a secure and self‐adaptive resource allocation framework that addressed the mentioned issues and allocates the most suitable resources to users' applications while ensuring the deadline constraints. Further, the proposed framework is integrated with a metaheuristic algorithm named enhanced spider monkey optimization algorithm that is based on the intelligent foraging behavior of spider monkeys. The proposed algorithm finds an optimal resource for the user's application using the fission‐fusion approach and improves multiple influential parameters like time, cost, degree of load balancing, energy consumption, task rejection ratio and so on. The experimental CloudSim based results verified that the proposed framework performs superior to state of art approaches like PSO, GSA, ABC, and IMMLB.

VeChat: correcting errors in long reads using variation graphs

Error correction is the canonical first step in long-read sequencing data analysis. Current self-correction methods, however, are affected by consensus sequence induced biases that mask true variants in haplotypes of lower frequency showing in mixed samples. Unlike consensus sequence templates, graph-based reference systems are not affected by such biases, so do not mistakenly mask true variants as errors. We present VeChat, as an approach to implement this idea: VeChat is based on variation graphs, as a popular type of data structure for pangenome reference systems. Extensive benchmarking experiments demonstrate that long reads corrected by VeChat contain 4 to 15 (Pacific Biosciences) and 1 to 10 times (Oxford Nanopore Technologies) less errors than when being corrected by state of the art approaches. Further, using VeChat prior to long-read assembly significantly improves the haplotype awareness of the assemblies. VeChat is an easy-to-use open-source tool and publicly available at https://github.com/HaploKit/vechat.