Most recent issue published online in the International Journal of Forensic Engineering.
International Journal of Forensic Engineering
http://www.inderscience.com/browse/index.php?journalID=159&year=2021&vol=5&issue=2
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International Journal of Forensic Engineering
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International Journal of Forensic Engineering
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http://www.inderscience.com/browse/index.php?journalID=159&year=2021&vol=5&issue=2
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k-Means clustering-based evolutionary algorithm for solving optimisation problems
http://www.inderscience.com/link.php?id=118911
Environmental adaptation method (EAM) is a newly developed optimisation algorithm for complex problems. Although EAM and its variants converge very fast in lower-dimensional problems, the performance of these algorithms falls drastically in higher-dimensional problems. This paper introduces a novel approach to improve the performance of the algorithm in higher-dimensional problems. In order to explore the whole search space, the problem search space is divided into multiple mutually exclusive clusters, and then parallel exploitation and exploration are achieved that produces better results. The solutions of independent clusters try to adopt a more suitable structure using the direction received from the local/global best and local/global worst solutions. The performance of the suggested algorithm is compared with other existing algorithms using the benchmark function of the COmparing Continuous Optimisers (COCO) framework. The experimental results have demonstrated that the proposed algorithm performs well in many ways.
k-Means clustering-based evolutionary algorithm for solving optimisation problems
Tribhuvan Singh; Krishn Kumar Mishra; Ranvijay
International Journal of Forensic Engineering, Vol. 5, No. 2 (2021) pp. 87 - 101
Environmental adaptation method (EAM) is a newly developed optimisation algorithm for complex problems. Although EAM and its variants converge very fast in lower-dimensional problems, the performance of these algorithms falls drastically in higher-dimensional problems. This paper introduces a novel approach to improve the performance of the algorithm in higher-dimensional problems. In order to explore the whole search space, the problem search space is divided into multiple mutually exclusive clusters, and then parallel exploitation and exploration are achieved that produces better results. The solutions of independent clusters try to adopt a more suitable structure using the direction received from the local/global best and local/global worst solutions. The performance of the suggested algorithm is compared with other existing algorithms using the benchmark function of the COmparing Continuous Optimisers (COCO) framework. The experimental results have demonstrated that the proposed algorithm performs well in many ways.]]>
10.1504/IJFE.2021.118911
International Journal of Forensic Engineering, Vol. 5, No. 2 (2021) pp. 87 - 101
Tribhuvan Singh
Krishn Kumar Mishra
Ranvijay
Department of Computer Science and Engineering, ITER, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, India ' Department of Computer Science and Engineering, MNNIT Allahabad, Prayagraj, India ' Department of Computer Science and Engineering, MNNIT Allahabad, Prayagraj, India
evolutionary algorithms
optimisation problems
EAM
environmental adaptation method
k-Means clustering
parallel exploitation and exploration
2021-11-11T23:20:50-05:00
Copyright © 2021 Inderscience Enterprises Ltd.
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2
87
101
2021-11-11T23:20:50-05:00
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A comparative analysis of SIFT, SURF and ORB on sketch and paint based images
http://www.inderscience.com/link.php?id=118910
Image retrieval has been one of the most interesting and emergent research areas in the field of computer vision. Content-based image retrieval (CBIR) systems are used in order to automatically index, search, retrieve and browse images from the databases. Content-based image retrieval system consider colour and texture features of the image, however those features are different in transformed images even though it is the toughest challenge for the CBIR to understand the image. Human perspective that has been based on input is essential for any retrieval system. Hand drawing images and painting images are considered as query image for this retrieval system. This paper has explored few eminent feature extraction techniques like scale invariant feature transform (SIFT), speeded up robust features (SURF) and oriented FAST and rotated BRIEF (ORB) as well as the performances of these techniques for sketch and paint based images. The suitable extraction technique is identified by this examination, the significance of SIFT, SURF and ORB features are listed.
A comparative analysis of SIFT, SURF and ORB on sketch and paint based images
R. Radha; M. Pushpa
International Journal of Forensic Engineering, Vol. 5, No. 2 (2021) pp. 102 - 110
Image retrieval has been one of the most interesting and emergent research areas in the field of computer vision. Content-based image retrieval (CBIR) systems are used in order to automatically index, search, retrieve and browse images from the databases. Content-based image retrieval system consider colour and texture features of the image, however those features are different in transformed images even though it is the toughest challenge for the CBIR to understand the image. Human perspective that has been based on input is essential for any retrieval system. Hand drawing images and painting images are considered as query image for this retrieval system. This paper has explored few eminent feature extraction techniques like scale invariant feature transform (SIFT), speeded up robust features (SURF) and oriented FAST and rotated BRIEF (ORB) as well as the performances of these techniques for sketch and paint based images. The suitable extraction technique is identified by this examination, the significance of SIFT, SURF and ORB features are listed.]]>
10.1504/IJFE.2021.118910
International Journal of Forensic Engineering, Vol. 5, No. 2 (2021) pp. 102 - 110
R. Radha
M. Pushpa
Department of Computer Science, Quaid-E-Millath Government College for Women (Autonomous), Chennai, 600 002, Tamilnadu, India ' Department of Computer Science, Quaid-E-Millath Government College for Women (Autonomous), Chennai, 600 002, Tamilnadu, India
CBIR
content-based image retrieval
KNN
K nearest neighbour
ORB
oriented FAST and rotated BRIEF
scale invariant feature transform
SIFT
SURF
speeded up robust features
2021-11-11T23:20:50-05:00
Copyright © 2021 Inderscience Enterprises Ltd.
5
2
102
110
2021-11-11T23:20:50-05:00
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Machine learning techniques for autism spectrum disorder (ASD) detection
http://www.inderscience.com/link.php?id=118912
Autism spectrum disorder (ASD) which is termed as ASD is a compound, integrated and lifelong growing incapability which comprises problem that are distinguished by repetition in behaviour, communication (non-verbal), doziness. In recent years, Autism is growing at a massive momentum which needs timely and early diagnosis. Autism can be detected through various tools (screening), but it is very time consuming and costly. In past few year, for prediction of ASD different types of dataset are used like images of autistic and non-autistic children, behavioural feature, genetic dataset etc. These datasets can be processed on different mathematical model's life machine learning, recognition of patterns and so on. The main aim of this paper is to analyse different types of datasets used to predict the autism traits in children by various researcher with the help of techniques like support vector machine (SVM), random forest scan, decision trees, logistic regression etc. and contrast the result in terms of their efficiency and accuracy.
Machine learning techniques for autism spectrum disorder (ASD) detection
Anshu Sharma; Poonam Tanwar
International Journal of Forensic Engineering, Vol. 5, No. 2 (2021) pp. 111 - 125
Autism spectrum disorder (ASD) which is termed as ASD is a compound, integrated and lifelong growing incapability which comprises problem that are distinguished by repetition in behaviour, communication (non-verbal), doziness. In recent years, Autism is growing at a massive momentum which needs timely and early diagnosis. Autism can be detected through various tools (screening), but it is very time consuming and costly. In past few year, for prediction of ASD different types of dataset are used like images of autistic and non-autistic children, behavioural feature, genetic dataset etc. These datasets can be processed on different mathematical model's life machine learning, recognition of patterns and so on. The main aim of this paper is to analyse different types of datasets used to predict the autism traits in children by various researcher with the help of techniques like support vector machine (SVM), random forest scan, decision trees, logistic regression etc. and contrast the result in terms of their efficiency and accuracy.]]>
10.1504/IJFE.2021.118912
International Journal of Forensic Engineering, Vol. 5, No. 2 (2021) pp. 111 - 125
Anshu Sharma
Poonam Tanwar
Department of Computer Science, Manav Rachna International Institute of Research and Studies, Faridabad, 121006, Haryana ' Department of Computer Science, Manav Rachna International Institute of Research and Studies, Faridabad, 121006, Haryana
machine learning
deep learning
classifier
genetic
dataset
behavioural features
2021-11-11T23:20:50-05:00
Copyright © 2021 Inderscience Enterprises Ltd.
5
2
111
125
2021-11-11T23:20:50-05:00
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Intrusion detection in forensics based on machine learning techniques: a review
http://www.inderscience.com/link.php?id=118915
Penetration into various systems, including information, organisations, banks and other systems has become a challenge. Intrusion detection systems (IDS) today have a great impact on detecting attacks and intrusions on many systems including forensics, and a nuclear design that can accurately perform the intrusion detection process is crucial. This paper discusses machine learning techniques of IDS design and implementation in forensics. In general, machine learning is categorised into three general categories: supervised, unsupervised and semi-supervised learning to detect intrusion. In each of these categories, techniques have been put forward that each one with its outstanding capabilities and features can be effective in detecting intrusion. Surveys and analyses show that supervised techniques have higher accuracy and capability to detect intrusions into the IDS.
Intrusion detection in forensics based on machine learning techniques: a review
Fathollah Bistouni; Mohsen Jahanshahi; Kong Fah Tee
International Journal of Forensic Engineering, Vol. 5, No. 2 (2021) pp. 126 - 156
Penetration into various systems, including information, organisations, banks and other systems has become a challenge. Intrusion detection systems (IDS) today have a great impact on detecting attacks and intrusions on many systems including forensics, and a nuclear design that can accurately perform the intrusion detection process is crucial. This paper discusses machine learning techniques of IDS design and implementation in forensics. In general, machine learning is categorised into three general categories: supervised, unsupervised and semi-supervised learning to detect intrusion. In each of these categories, techniques have been put forward that each one with its outstanding capabilities and features can be effective in detecting intrusion. Surveys and analyses show that supervised techniques have higher accuracy and capability to detect intrusions into the IDS.]]>
10.1504/IJFE.2021.118915
International Journal of Forensic Engineering, Vol. 5, No. 2 (2021) pp. 126 - 156
Fathollah Bistouni
Mohsen Jahanshahi
Kong Fah Tee
Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, 13117773591, Iran ' Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, 13117773591, Iran ' School of Engineering, University of Greenwich, Kent, ME4 4TB, UK
intrusion detection
machine learning
forensics
data mining
supervised learning
unsupervised
semi-supervised
2021-11-11T23:20:50-05:00
Copyright © 2021 Inderscience Enterprises Ltd.
5
2
126
156
2021-11-11T23:20:50-05:00
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Assessment of factors affecting construction waste: recycled aggregates and their embodied energy composition
http://www.inderscience.com/link.php?id=118919
The present research focuses on some of the essential aspects of embodied energy of recycled aggregates. One way to lower the embodied energy levels is the utilisation of recycled aggregates. However, aforesaid aggregates also subsequently produce embodied energy, albeit much lower levels than concrete. This research will therefore present an analysis of waste management reduction approach through recycled aggregates, to alleviate the embodied energy levels. The analysis revealed that a key consideration is material choice during the pre-planning stage. Since materials such as timber and masonry have considerably lower embodied energy to produce, they thus use less embodied energy. As a result, such recycled aggregates - from construction to demolition waste, can be used as an alternative to mining virgin aggregate. Such outcome subsequently leads to lower the overall embodied energy required, but also significantly reduces the waste created.
Assessment of factors affecting construction waste: recycled aggregates and their embodied energy composition
Sara Gharehbaghi; Koorosh Gharehbaghi; Kong Fah Tee
International Journal of Forensic Engineering, Vol. 5, No. 2 (2021) pp. 157 - 173
The present research focuses on some of the essential aspects of embodied energy of recycled aggregates. One way to lower the embodied energy levels is the utilisation of recycled aggregates. However, aforesaid aggregates also subsequently produce embodied energy, albeit much lower levels than concrete. This research will therefore present an analysis of waste management reduction approach through recycled aggregates, to alleviate the embodied energy levels. The analysis revealed that a key consideration is material choice during the pre-planning stage. Since materials such as timber and masonry have considerably lower embodied energy to produce, they thus use less embodied energy. As a result, such recycled aggregates - from construction to demolition waste, can be used as an alternative to mining virgin aggregate. Such outcome subsequently leads to lower the overall embodied energy required, but also significantly reduces the waste created.]]>
10.1504/IJFE.2021.118919
International Journal of Forensic Engineering, Vol. 5, No. 2 (2021) pp. 157 - 173
Sara Gharehbaghi
Koorosh Gharehbaghi
Kong Fah Tee
Bechtel Corporation, San Francisco, CA 94105, USA ' School of PCPM, RMIT University, Melbourne, Victoria 3000, Australia ' School of Engineering, University of Greenwich, Kent, ME4 4TB, UK
recycled aggregates
embodied energy
waste minimisation
virgin aggregates
green buildings
GBELS
green building evaluation and labelling system
2021-11-11T23:20:50-05:00
Copyright © 2021 Inderscience Enterprises Ltd.
5
2
157
173
2021-11-11T23:20:50-05:00