Artificial Intelligence & Applications

International Conference on Advances in Computing & Information Technologies (CACIT 2021)

December 11 ~ 12, 2021, Chennai, India

https://inwes2021.org/cacit/index

Scope & Topics

International Conference on Advances in Computing & Information Technologies (CACIT 2021) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Computing and Information Technology Trends. The Conference looks for significant contributions to all major fields of the Computing and Information Technologies.

Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to.

Topics of Interest

Artificial Intelligence
Big Data
Bioinformatics and Algorithms
Blockchain
Business Intelligence
Computer Architecture and Real Time Systems
Computer Education
Data Science
Database and Data Mining
Deep Learning
Dependable, Reliable and Autonomic Computing
Devops Models, Practices, Challenges
Distributed and Parallel Systems
DSP/Image Processing/Pattern Recognition/Multimedia
E-Learning, E-Business, Enterprise Information Systems and E-Government
Embedded System and Software
Fuzzy Systems
Game and Software Engineering
Grid and Scalable Computing
Information Technology Management
Integrating Technology in Education
Intelligent Information and Database Systems
Internet of Things (IoT)
Knowledge Management
Machine Learning & Applications
Mobile and Ubiquitous Computing
Modeling and Simulation
Multimedia Systems and Services
Natural Language Processing (NLP) & Text Mining
Networking and Communications
Ontology and Semantic Web
Performance Evaluation
Scaled Agile Framework (SAFe) in the real World
Security and Information Assurance
Smart Cities
Software Automation, Software as a Service ( SaaS)
Software Engineering
Swarm Intelligence
Theoretical Computer Science
Web and Internet Computing

Paper Submission

Authors are invited to submit papers through the conference Submission system October 16, 2021. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this conference. The proceedings of the conference will be published by Computer Science Conference Proceedings in Computer Science & Information Technology (CS & IT)series (Confirmed).

Selected papers from CACIT 2021, after further revisions, will be published in the special issue of the following journal.

Important Dates

Submission Deadline : October 16, 2021
Authors Notification : November 25, 2021
Registration & Camera-Ready Paper Due : December 02, 2021

Contact Us

Here’s where you can reach us : cacitconference@yahoo.com or cacit@inwes2021.org

Paper Submission URL : https://inwes2021.org/submission/index.php

4th International Conference on Bioscience & Engineering (BIO 2021)

December 11 ~ 12, 2021, Chennai, India

https://inwes2021.org/bio/index

Scope

4^th International Conference on Bioscience & Engineering (BIO 2021) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications impacts and challenges of Bioscience and Engineering.

The goal of this Conference is to bring together researchers and practitioners from academia and industry to focus on Bioscience and Engineering advancements, and establishing new collaborations in these areas. Original research papers, state-of-the-art reviews are invited for publication in all areas of Bioscience and Engineering.

Topics of Interest

Biochemical Engineering
Biochemistry
Bioinformatics
Health Informatics
Biomedicine
Bioscience Engineering
Biotechnology
Bio-fermentation Technology
Food Science & Technology
Genetics
Geomicrobiology
Microbiology
Molecular Biology of Plants
Zoophysiology

Paper Submission

Authors are invited to submit papers through the Submission System by October 16, 2021. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this conference. The proceedings of the conference will be published by Computer Science Conference Proceedings in Computer Science & Information Technology (CS&IT) series(Confirmed).

Selected papers from BIO 2021, after further revisions, will be published in the special issue of the following journals

International Journal on Bioinformatics & Biosciences (IJBB)

Important Dates

Submission Deadline: October 16, 2021
Authors Notification: November 25, 2021
Registration & Camera-Ready Paper Due: December 02, 2021

Contact Us

Here’s where you can reach us : bioconfe@yahoo.com or bio@inwes2021.org

2nd International Conference on Big Data, Machine Learning and IoT (BMLI 2021)

December 11 ~ 12, 2021, Chennai, India

https://inwes2021.org/bmli/index

Scope

2^nd International Conference on Big Data, Machine Learning and IoT (BMLI 2021) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the areas of Big Data, Machine Learning and IoT.

Topics of Interest

Big Data

Big Data Techniques, models and algorithms
Big Data Infrastructure and platform
Big Data Search and Mining
Big Data Security, Privacy and Trust
Big Data Applications, Bioinformatics, Multimedia etc
Big Data Tools and systems
Big Data Mining
Big Data Management
Cloud and grid computing for Big Data
Machine Learning and AI for Big Data
Big Data Analytics and Social Media
5G and Networks for Big Data

Machine learning

Machine Learning Applications
Learning in knowledge-intensive systems
Learning Methods and analysis
Learning Problems
Deep Learning
Computer Vision
Bayesian Network
Data Mining

Internet of Things

IoT Connectivity and Networking
Electronics and Signal processing for IoT
IoT Experimental Results and Deployment Scenarios
IoT Applications and Services
IoT Security and Privacy
IoT-enabled Innovation and Entrepreneurship
Emerging Standards for an Internet of Things
IoT communication Systems and Network Infrastructures
RFID and Tagging Technologies
Web Services

Paper Submission

Selected papers from BMLI 2021, after further revisions, will be published in the special issue of the following journals

Important Dates

Submission Deadline : October 16, 2021
Authors Notification : November 15, 2021
Registration & Camera-Ready Paper Due : November 23, 2021

Contact Us

Here’s where you can reach us : bmliconfe@yahoo.com or bmli@inwes2021.org

USING SEMI-SUPERVISED CLASSIFIER TO FORECAST EXTREME CPU UTILIZATION

Nitin Khosla¹ and Dharmendra Sharma²

¹Assistant Director- Performance Engineering, ICTCAPM, Dept. of Home Affairs, Canberra, Australia

²Professor – Computer Science, University of Canberra, Australia

ABSTRACT

A semi-supervised classifier is used in this paper is to investigate a model for forecasting unpredictable load on the IT systems and to predict extreme CPU utilization in a complex enterprise environment with large number of applications running concurrently. This proposed model forecasts the likelihood of a scenario where extreme load of web traffic impacts the IT systems and this model predicts the CPU utilization under extreme stress conditions. The enterprise IT environment consists of a large number of applications running in a real time system. Load features are extracted while analysing an envelope of the patterns of work-load traffic which are hidden in the transactional data of these applications. This method simulates and generates synthetic workload demand patterns, run use-case high priority scenarios in a test environment and use our model to predict the excessive CPU utilization under peak load conditions for validation. Expectation Maximization classifier with forced-learning, attempts to extract and analyse the parameters that can maximize the chances of the model after subsiding the unknown labels. As a result of this model, likelihood of an excessive CPU utilization can be predicted in short duration as compared to few days in a complex enterprise environment. Workload demand prediction and profiling has enormous potential in optimizing usages of IT resources with minimal risk.

KEYWORDS

Semi-Supervised Learning, Performance Engineering, Load And Stress Testing, Machine Learning.

Original Source Link :

https://aircconline.com/ijaia/V11N1/11120ijaia04.pdf

http://airccse.org/journal/ijaia/current2020.html

A HYBRID ALGORITHM BASED ON INVASIVE WEED OPTIMIZATION ALGORITHM AND GREY WOLF OPTIMIZATION ALGORITHM

Wisam Abdulelah Qasim¹ and Ban Ahmed Mitras²

¹M.sc. Student, Department of Mathematics, College of Computer Science & Mathematics, Mosul, Iraq. ² Department of Mathematics, College of Computer Sciences & Mathematics, Mosul, Iraq

ABSTRACT

In this research, two algorithms first, considered to be one of hybrid algorithms. And it is algorithm represents invasive weed optimization. This algorithm is a random numerical algorithm and the second algorithm representing the grey wolves optimization. This algorithm is one of the algorithms of swarm intelligence in intelligent optimization. The algorithm of invasive weed optimization is inspired by nature as the weeds have colonial behavior and were introduced by Mehrabian and Lucas in 2006. Invasive weeds are a serious threat to cultivated plants because of their adaptability and are a threat to the overall planting process. The behavior of these weeds has been studied and applied in the invasive weed algorithm. The algorithm of grey wolves, which is considered as a swarm intelligence algorithm, has been used to reach the goal and reach the best solution. The algorithm was designed by SeyedaliMirijalili in 2014 and taking advantage of the intelligence of the squadrons is to avoid falling into local solutions so the new hybridization process between the previous algorithms GWO and IWO and we will symbolize the new algorithm IWOGWO. Comparing the suggested hybrid algorithm with the original algorithms it results were excellent. The optimum solution was found in most of test functions.

KEYWORDS

Invasive weeds optimization algorithm , grey wolves optimization algorithm , hybrid algorithms, optimization

Original Source Link : http://aircconline.com/ijaia/V11N1/11120ijaia03.pdf

http://airccse.org/journal/ijaia/current2020.html

AN ONTOLOGICAL ANALYSIS AND NATURAL
LANGUAGE PROCESSING OF FIGURES OF SPEECH

Christiana Panayiotou

Department of Communications and Internet Studies,
Technological University of Cyprus

ABSTRACT

The purpose of the current paper is to present an ontological analysis to the identification of a particular type of prepositional figures of speech via the identification of inconsistencies in ontological concepts. Prepositional noun phrases are used widely in a multiplicity of domains to describe real world events and activities. However, one aspect that makes a prepositional noun phrase poetical is that the latter suggests a semantic relationship between concepts that does not exist in the real world. The current paper shows that a set of rules based on WordNet classes and an ontology representing human behaviour and properties, can be used to identify figures of speech due to the discrepancies in the semantic relations of the concepts involved. Based on this realization, the paper describes a method for determining poetic vs. non-poetic prepositional figures of speech, using WordNet class hierarchies. The paper also addresses the problem of inconsistency resulting from the assertion of figures of speech in ontological knowledge bases, identifying the problems involved in their representation. Finally, it discusses how a contextualized approach might help to resolve this problem.

KEYWORDS

Ontologies, NLP, Linguistic creativity.

Original Source Link : http://aircconline.com/ijaia/V11N1/11120ijaia02.pdf

http://www.airccse.org/journal/ijaia/current2020.html

CAPSULE NETWORK PERFORMANCE WITH AUTONOMOUS NAVIGATION

Thomas Molnar and Eugenio Culurciello

ECE, Purdue University, 610 Purdue Mall, West Lafayette, 47907, IN, USA

ABSTRACT

Capsule Networks (CapsNets) have been proposed as an alternative to Convolutional Neural Networks (CNNs). This paper showcases how CapsNets are more capable than CNNs for autonomous agent exploration of realistic scenarios. In real world navigation, rewards external to agents may be rare. In turn, reinforcement learning algorithms can struggle to form meaningful policy functions. This paper’s approach Capsules Exploration Module (Caps-EM) pairs a CapsNets architecture with an Advantage Actor Critic algorithm. Other approaches for navigating sparse environments require intrinsic reward generators, such as the Intrinsic Curiosity Module (ICM) and Augmented Curiosity Modules (ACM). Caps-EM uses a more compact architecture without need for intrinsic rewards. Tested using ViZDoom, the Caps-EM uses 44% and 83% fewer trainable network parameters than the ICM and Depth-Augmented Curiosity Module (D-ACM), respectively, for 1141% and 437% average time improvement over the ICM and D-ACM, respectively, for converging to a policy function across “My Way Home” scenarios

KEYWORDS

Neural Networks, Autonomous, Navigation, Capsules Networks

Original Source Link : http://www.airccse.org/journal/ijaia/current2020.html

http://aircconline.com/ijaia/V11N1/11120ijaia01.pdf

Click to access 11120ijaia01.pdf

AN INNOVATIVE RESEARCH FRAMEWORK ON INTELLIGENT TEXT DATA CLASSIFICATION SYSTEM USING GENETIC ALGORITHM

Dr. V V R Maheswara Rao¹ , N Silpa² and Dr.Gadiraju Mahesh³

^1,2 Department of CSE, Shri Vishnu Engineering College for Women, Andhra Pradesh, India

³ Department of CSE, S.R.K.R. Engineering College, Andhra Pradesh, India

ABSTRACT

Recent years have witnessed an astronomical growth in the amount of textual information available both on the web and institutional wise document repositories. As a result, text mining has become extremely prevalent and processing of textual information from such repositories got the focus of the current age researchers. Indeed, in the researcher front of text analysis, there are numerous cutting edge applications are available for text mining. More specifically, the classification oriented text mining has been gaining more attention as it concentrates measures like coverage and accuracy. Along with the huge volume of data, the aspirations of the user are growing far higher than the human capacity, thus, an automated and competitive intelligent systems are essential for reliable text analysis. Towards this, the authors in the present paper propose an Intelligent Text Data Classification System (ITDCS) which is designed in the light of biological nature of genetic approach and able to acquire computational intelligence accurately. Initially, ITDCS focusses on preparing structured data from the huge volume of unstructured data with its procedural steps and filter methods. Subsequently, it emphasises on classifying the text data into labelled classes using KNN classification based on the selection of best features derived by genetic algorithm. In this process, it specially concentrates on adding the power of intelligence to the classifier using together with the biological parts namely, encoding strategy, fitness function and operators of genetic algorithm. The integration of all biological components of genetic algorithm in ITDCS significantly improves the accuracy and reduces the misclassification rate in classifying the text data.

KEYWORDS

Text Data, Classification, Genetic Approach, Learning Algorithm, Text Mining, KNN Classification.

Original Source Link : http://aircconline.com/ijaia/V7N6/7616ijaia05.pdf

http://airccse.org/journal/ijaia/current2016.html

CENTROG FEATURE TECHNIQUE FOR VEHICLE TYPE RECOGNITION AT DAY AND NIGHT TIMES

Martins E. Irhebhude, Philip O. Odion and Darius T. Chinyio
Faculty of Science, Department of Computer Science, Nigerian Defence Academy,
Kaduna, Nigeria.

ABSTRACT

This work proposes a feature-based technique to recognize vehicle types within day and night times. Support vector machine (SVM) classifier is applied on image histogram and CENsus Transformed histogRam Oriented Gradient (CENTROG) features in order to classify vehicle types during the day and night. Thermal images were used for the night time experiments. Although thermal images suffer from low image resolution, lack of colour and poor texture information, they offer the advantage of being unaffected by high intensity light sources such as vehicle headlights which tend to render normal images unsuitable for night time image capturing and subsequent analysis. Since contour is useful in shape based categorisation and the most distinctive feature within thermal images, CENTROG is used to capture this feature information and is used within the experiments. The experimental results so obtained were compared with those obtained by employing the CENsus TRansformed hISTogram (CENTRIST). Experimental results revealed that CENTROG offers better recognition accuracies for both day and night times vehicle types recognition.

KEYWORDS

CENTROG, CENTRIST, Vehicle Type Recognition, Day-time Recognition, Night-time Recognition, Classification

Original Source Link : http://aircconline.com/ijaia/V7N6/7616ijaia04.pdf

http://airccse.org/journal/ijaia/current2016.html

STOCHASTIC MODELING TECHNOLOGY FOR GRAIN CROPS STORAGE APPLICATION: REVIEW

Johevajile K. Mazima¹ , Agbinya Johnson², Emmanuel Manasseh³and Shubi Kaijage⁴

^1,4 Department of Communication Science and Engineering, Nelson Mandela African Institution of Science and Technology, Arusha, Tanzania

² School of Information Technology and Engineering, Melbourne Institute of Technology, Melbourne, Australia

³ Tanzania Communications Regulatory Authority, Dar es Salaam, Tanzania

ABSTRACT

Stochastic modeling is a key technique in event prediction and forecasting applications. Recently, stochastic models such as the Artificial Neural Network, Hidden Markov, and Markov Chain have received a significant attention in agricultural application. These techniques are capable of predicting the actions for the better planning and management in various fields. This work comprehensively summarizes and compares their applications such as their processing techniques, performance, as well as their strengths and limitations with regard to event prediction and forecasting. The work ends with recommendations on the appropriate techniques for cereal grain storage application

.KEYWORDS

Grain storage condition, Hidden markov model, Artificial Neural Network, Markov chain & Forecasting.

Original Source Link : http://aircconline.com/ijaia/V7N6/7616ijaia03.pdf

http://airccse.org/journal/ijaia/current2016.html

Department of Computer Engineering, PCE, Mumbai, India.

Arab American University-Jenin, Palestine.

The Ministry of Science, Industry, and Technology, Ankara 6000, Turkey.

1Assistant Professor, Department of Civil Engineering,
Vidya Jyothi Institute of Technology, Hyderabad, India.