Bio-Inspired Computing: Algorithms Review, Deep Analysis, And The Scope Of Applications,
2020
Helwan University
Bio-Inspired Computing: Algorithms Review, Deep Analysis, And The Scope Of Applications, Ashraf Darwish Prof.
Future Computing and Informatics Journal
Bio-inspired computing represents the umbrella of different studies of computer science, mathematics, and biology in the last years. Bioinspired computing optimization algorithms is an emerging approach which is based on the principles and inspiration of the biological evolution of nature to develop new and robust competing techniques. In the last years, the bio-inspired optimization algorithms are recognized in machine learning to address the optimal solutions of complex problems in science and engineering. However, these problems are usually nonlinear and restricted to multiple nonlinear constraints which propose many problems such as time requirements and high dimensionality to find the optimal solution. …
Task Schedul Ing For Cloud Computing Using Multi-Objective Hybrid Bacteria Foraging Algorithm,
2020
Department of Computer Science, IIIT Bhubaneswar, Odisha, India
Task Schedul Ing For Cloud Computing Using Multi-Objective Hybrid Bacteria Foraging Algorithm, Sobhanayak Srichandan, Turuk Ashok Kumar, Sahoo Bibhudatta
Future Computing and Informatics Journal
Cloud computing is the delivery of computing services over the internet. Cloud services allow individuals and other businesses organization to use data that are managed by third parties or another person at remote locations. Most Cloud providers support services under constraints of Service Level Agreement (SLA) definitions. The SLAs are composed of different quality of service (QoS) rules promised by the provider. A cloud environment can be classified into two types: computing clouds and data clouds. In computing cloud, task scheduling plays a vital role in maintaining the quality of service and SLA. Efficient task scheduling is one of the …
An Analysis Of Mapreduce Efficiency In Document Clustering Using Parallel K-Means Algorithm,
2020
[email protected]
An Analysis Of Mapreduce Efficiency In Document Clustering Using Parallel K-Means Algorithm, Tanvir Habib Sardar, Zahid Ansari
Future Computing and Informatics Journal
One of the significant data mining techniques is clustering. Due to expansion and digitalization of each field, large datasets are being generated rapidly. Such large dataset clustering is a challenge for traditional sequential clustering algorithms due to huge processing time. Distributed parallel architectures and algorithms are thus helpful to achieve performance and scalability requirement of clustering large datasets. In this study, we design and experiment a parallel k-means algorithm using MapReduce programming model and compared the result with sequential k-means for clustering varying size of document dataset. The result demonstrates that proposed k-means obtains higher performance and outperformed sequential k-means …
Tga: Team Game Algorithm,
2020
Department of Mechanical Engineering, Sirjan University of Technology, Sirjan, Iran
Tga: Team Game Algorithm, M.J. Mahmoodabadi
Future Computing and Informatics Journal
Lately, there is a growing interest in conducting research on optimization algorithms due to their wide range of engineering applications. One of the optimization algorithms' categories is evolutionary algorithms which are inspired from the natural behavior of animals and humans. Further, each of the evolutionary algorithms has its own advantages and disadvantages in convergence accuracy and computational time. In the present paper, a novel solution search algorithm taken from the team games is introduced. This evolutionary algorithm named Team Game Algorithm (TGA) involves passing a ball, making mistakes and substitution operators. Comparing the TGA's results to the outcomes of other …
Hybrid Energy Aware Clustered Protocol For Iot Heterogeneous Network,
2020
Faculty of Computers and Information, Helwan University, Cairo, Egypt
Hybrid Energy Aware Clustered Protocol For Iot Heterogeneous Network, Rowayda A. Sadek
Future Computing and Informatics Journal
IoT diverse applications face many challenges. The main challenge is to have efficient energy aware communication protocols that utilize the diversity and heterogeneity of the connected things through Internet. Saving energy is a vital requirement in the limited battery energy nodes and also for the outsourced energy nodes for green computing. IoT milieu has many diverse devices that are heterogeneous in their energies, their Internet availability, etc. These devices are usually distributed into regions with different heterogeneity levels; ranging from homogeneous to near homogenous, till reaching to the high heterogeneous regions. Many existed protocols efficiently treated either the homogenous devices …
A Fast Sift Based Method For Copy Move Forgery Detection,
2020
Department of Computer Science, Faculty of Computers and Information, Arab Academy for Science, Technology and Maritime Transport, Cairo, Egypt
A Fast Sift Based Method For Copy Move Forgery Detection, Hesham A. Alberry, Abdelfatah A. Hegazy, Gouda I. Salama
Future Computing and Informatics Journal
Image forensics is an important area of research used to indicate if a particular image is original or subjected to any kind of tampering. Images are essential part of judgment in tribunals. For forensic analysis, image forgery-detection techniques used to identify the forged images. In this paper, an effective algorithm to indicate Copy Move Forgery in digital image presented. The Scale Invariant Feature Transform (SIFT) and Fuzzy C-means (FCM) for clustering are utilized in the proposed algorithm. A number of numerical experiments performed using the MICC-220 dataset. The authors created an additional dataset, which consisted of 353 color images. The …
Withdrawn: Forecasting Of Nonlinear Time Series Using Artificial Neural Network,
2020
Computer Science Department, Faculty of Computers and Information, Cairo University, Giza, Egypt
Withdrawn: Forecasting Of Nonlinear Time Series Using Artificial Neural Network, Amr Badr
Future Computing and Informatics Journal
When forecasting time series, it is important to classify them according to linearity behavior; the linear time series remains at the forefront of academic and applied research. It has often been found that simple linear time series models usually leave certain aspects of economic and financial data unexplained. The dynamic behavior of most of the time series in our real life, with its autoregressive and inherited moving average terms, pose the challenge to forecast nonlinear times series that contain inherited moving average terms using computational intelligence methodologies such as neural networks. It is rare to find studies that concentrate on …
Attribute Selection Using Fuzzy Roughset Based Customized Similarity Measure For Lung Can Cer Microarra Y Gene Expression Data,
2020
Dept. of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India
Attribute Selection Using Fuzzy Roughset Based Customized Similarity Measure For Lung Can Cer Microarra Y Gene Expression Data, C. Arunkumar, S. Ramakrishnan
Future Computing and Informatics Journal
Microarray gene expression data plays a prominent role in feature selection that helps in diagnosis and treatment of a wide variety of diseases. Microarray gene expression data contains redundant feature genes of high dimensionality and smaller training and testing samples. This paper proposes a customized similarity measure using fuzzy rough quick reduct algorithm for attribute selection. Information Gain based entropy is used to reduce the dimensionality in the first stage and the proposed fuzzy rough quick reduct method that defines a customized similarity measure for selecting the minimum number of informative genes and removing the redundant genes is employed at …
Hcidl: Human-Computer Interface Description Language For Multi-Target, Multimodal, Plastic User Interfaces,
2020
Abou Bekr Belkaid University, Tlemcen, Algeria
Hcidl: Human-Computer Interface Description Language For Multi-Target, Multimodal, Plastic User Interfaces, Abdelkrim Benamar, Lamia Gaouar, Olivier Le Goaer, Frédérique Biennier
Future Computing and Informatics Journal
From the human-computer interface perspectives, the challenges to befaced are related to the consideration of new, multiple interactions, and the diversity of devices. The large panel of interactions (touching, shaking, voice dictation, positioning …) and the diversification of interaction devices can be seen as a factor of flexibility albeit introducing incidental complexity. Our work is part of the field of user interface description languages. After an analysis of the scientific context of our work, this paper introduces HCIDL, a modelling language staged in a model-driven engineering approach. Among the properties related to human-computer interface, our proposition is intended for modelling …
A Survey On Opinion Summarization Technique S For Social Media,
2020
Computer Science Dept., Faculty of Computers and Information, Helwan University, Cairo, Egypt
A Survey On Opinion Summarization Technique S For Social Media, Mohammed Elsaid Moussa, Ensaf Hussein Mohamed, Mohamed H. Haggag
Future Computing and Informatics Journal
The volume of data on the social media is huge and even keeps increasing. The need for efficient processing of this extensive information resulted in increasing research interest in knowledge engineering tasks such as Opinion Summarization. This survey shows the current opinion summarization challenges for social media, then the necessary pre-summarization steps like preprocessing, features extraction, noise elimination, and handling of synonym features. Next, it covers the various approaches used in opinion summarization like Visualization, Abstractive, Aspect based, Query-focused, Real Time, Update Summarization, and highlight other Opinion Summarization approaches such as Contrastive, Concept-based, Community Detection, Domain Specific, Bilingual, Social Bookmarking, …
Patient Symptoms Elicitation Process For Breast Cancer Medical Expert Systems: A Semantic Web And Natural Language Parsing Approach,
2020
Department of Computer Science, Faculty of Physical Science, Ahmadu Bello University, Zaria, Nigeria
Patient Symptoms Elicitation Process For Breast Cancer Medical Expert Systems: A Semantic Web And Natural Language Parsing Approach, O.N. Oyelade, A.A. Obiniyi, S.B. Junaidu, S.A. Adewuyi
Future Computing and Informatics Journal
Information gathering from patient by clinicians during diagnostic procedures may sometimes require some skills to adequately collect required information that will be sufficient for the procedure. A situation where this information gathering may proof difficult in when a diagnostic decision making support system (DDSS) will have to gather such information from patient before carrying out the diagnostic procedure. Research has proven that it is more challenging to ensure user or patient inputs, in their raw form, maps into the list of acceptable medical terms for diagnostic tasks. This paper therefore proposes a formalized input generating model that addresses this shortcoming …
Classification Using Deep Learning Neural Networks For Brain Tumors,
2020
Faculty of Computers and Information Technology, Future University in Egypt, New Cairo, Egypt
Classification Using Deep Learning Neural Networks For Brain Tumors, Heba Mohsen
Future Computing and Informatics Journal
Deep Learning is a new machine learning field that gained a lot of interest over the past few years. It was widely applied to several applications and proven to be a powerful machine learning tool for many of the complex problems. In this paper we used Deep Neural Network classifier which is one of the DL architectures for classifying a dataset of 66 brain MRIs into 4 classes e.g. normal, glioblastoma, sarcoma and metastatic bronchogenic carcinoma tumors. The classifier was combined with the discrete wavelet transform (DWT) the powerful feature extraction tool and principal components analysis (PCA) and the evaluation …
Depth-Based Human Activity Recognition: A Comparative Perspective Study On Feature Extraction,
2020
Beni-Suef University, Cairo, Egypt
Depth-Based Human Activity Recognition: A Comparative Perspective Study On Feature Extraction, Heba Hamdy Ali, Hossam M. Moftah, Aliaa A.A. Youssif
Future Computing and Informatics Journal
Depth Maps-based Human Activity Recognition is the process of categorizing depth sequences with a particular activity. In this problem, some applications represent robust solutions in domains such as surveillance system, computer vision applications, and video retrieval systems. The task is challenging due to variations inside one class and distinguishes between activities of various classes and video recording settings. In this study, we introduce a detailed study of current advances in the depth maps-based image representations and feature extraction process. Moreover, we discuss the state of art datasets and subsequent classification procedure. Also, a comparative study of some of the more …
Privacy-Preserving Data Aggregation In Resource-Constrained Sensor Nodes In Internet Of Things: A Review,
2020
COMSATS Institute of Information Technology, Abbottabad 22060, Pakistan
Privacy-Preserving Data Aggregation In Resource-Constrained Sensor Nodes In Internet Of Things: A Review, Inayat Ali
Future Computing and Informatics Journal
Privacy problems are lethal and getting more attention than any other issue with the notion of the Internet of Things (IoT). Since IoT has many application areas including smart home, smart grids, smart healthcare system, smart and intelligent transportation and many more. Most of these applications are fueled by the resource-constrained sensor network, such as Smart healthcare system is powered by Wireless Body Area Network (WBAN) and Smart home and weather monitoring systems are fueled by Wireless Sensor Networks (WSN). In the mentioned application areas sensor node life is a very important aspect of these technologies as it explicitly effects …
Simultaneous Ranking And Selection Of Keystroke Dynamics Features Through A Novel Multi-Objective Binary Bat Algorithm,
2020
Faculty of Computers and Information, Helwan University, Egypt
Simultaneous Ranking And Selection Of Keystroke Dynamics Features Through A Novel Multi-Objective Binary Bat Algorithm, Taha M. Mohamed, Hossam M. Moftah
Future Computing and Informatics Journal
In this paper, we propose a novel multi-objective binary bat algorithm for simultaneous ranking and selection of keystroke dynamics features. The proposed algorithm uses the V shaped binarization function. Simulation results show that, the proposed algorithm can efficiently identify the most important features of the data set. Of the three feature classes, the key down hold time features (H-features) are proofed to be the most dominant features. Using H-features only in classification decreases the mean square error (MSE) by 2% compared to choosing all features in classification. The UD features are the second ranked features. The worst features are the …
Overcoming Business Process Reengineering Obstacles Using Ontology-Based Knowledge Map Methodology,
2020
College of Business, University of Jeddah, Saudi Arabia
Overcoming Business Process Reengineering Obstacles Using Ontology-Based Knowledge Map Methodology, Mahmoud Abdellatif, Marwa Salah Farhan, Naglaa Saeed Shehata
Future Computing and Informatics Journal
Business process reengineering (BPR) is identified as one of the most important solutions for organizational improvements in all performance measures of business processes. However, high failure rates 70% is reported about using it the most important reason that caused the failure is the focus on the process itself; regardless of the surrounding environment, and the knowledge of the organization. The other reasons are due to the lack of tools to determine the causes of the inconsistencies and inefficiencies. This paper proposes Process Reengineering Ontology-based knowledge Map Methodology (PROM) to reduce the failure ratio, solve BPR problems, and overcome their difficulties. …
Pulsar Selection Using Fuzzy Knn Classifier,
2020
Faculty of Computers and Information, Helwan University, Egypt
Pulsar Selection Using Fuzzy Knn Classifier, Taha M. Mohamed
Future Computing and Informatics Journal
Pulsars are rare type of stars that emit radio signals that could be detected from earth. Astronomy scientists give more attention to this type of stars for many reasons. In the near past, the problem of pulsar selection was carried out manually. Recently, neural network techniques are proposed to solve the problem. In this paper, we present a novel technique to efficiently selecting pulsars. The proposed algorithm is based on the fuzzy knn classifier. Results show that, the proposed algorithm outperforms five other classifiers, including neural network classifiers, using three evaluation metrics. The proposed algorithm is evaluated on the recent …
A Robust 3d Mesh Watermarking Algorithm Utilizing Fuzzy C-Means Clustering,
2020
Al-Azhar University, Faculty of Science, Egypt
A Robust 3d Mesh Watermarking Algorithm Utilizing Fuzzy C-Means Clustering, Ola M. El Zein, Lamiaa M. El Bakrawy, Neveen I. Ghali Prof.
Future Computing and Informatics Journal
A new robust 3D watermarking algorithm utilizing Fuzzy C-Means (FCM) clustering technique is presented. FCM clusters 3D mesh vertices into suitable and unsuitable choices to insert the watermark without occasioning visible deformation, and also it is tough for the attacker to determine places of the watermark insertion. Two watermarking processes are offered to insert the watermark into 3D mesh models. The first process utilizes topical statistical measurements like average and standard deviation in order to alter the values of vertices to secret watermark data into 3D mesh models, however, the second process utilizes a jumbled insertion planning to insert the …
An Improved Rank Based Disease Prediction Using Web Navigation Patterns On Bio-Medical Databases,
2020
Department of Computer Science and Engineering, JNTUA, Anantapur, India
An Improved Rank Based Disease Prediction Using Web Navigation Patterns On Bio-Medical Databases, P. Dhanalakshmi, K. Ramani
Future Computing and Informatics Journal
Applying machine learning techniques to on-line biomedical databases is a challenging task, as this data is collected from large number of sources and it is multi-dimensional. Also retrieval of relevant document from large repository such as gene document takes more processing time and an increased false positive rate. Generally, the extraction of biomedical document is based on the stream of prior observations of gene parameters taken at different time periods. Traditional web usage models such as Markov, Bayesian and Clustering models are sensitive to analyze the user navigation patterns and session identification in online biomedical database. Moreover, most of the …
A Framework For Safer Driving In Mauritius,
2020
Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Mauritius, Réduit, Mauritius
A Framework For Safer Driving In Mauritius, V Bassoo, V Hurbungs, V. Ramnarain Seetohul, T.P Fowdur, Y Beeharry
Future Computing and Informatics Journal
According to the National Transport Authority (NTA), there were 493,081 registered vehicles in Mauritius in April 2016, which represents a 1.4% annual increase compared to 2015. Despite the sensitization campaigns and the series of measures setup by the Minister of Public Infrastructure and Land Transport, the number of road accidents continues to rise. The three main elements that contribute to accidents are: road infrastructure, vehicle and driver. The driver has the highest contribution in collisions. If the driver is given the right information (e.g. driving behaviour, accident-prone areas and vehicle status) at the right time, he/she can make better driving …
