JournalTOCs

International Journal of Forensic Engineering
Number of Followers: 2

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1744-9944 - ISSN (Online) 1744-9952
Published by Inderscience Publishers

[439 journals]

k-Means clustering-based evolutionary algorithm for solving
optimisation problems
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  Authors: Tribhuvan Singh, Krishn Kumar Mishra, Ranvijay
  Pages: 87 - 101
  Abstract: 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.
  Keywords: evolutionary algorithms; optimisation problems; EAM; environmental adaptation method; k-Means clustering; parallel exploitation and exploration
  Citation: International Journal of Forensic Engineering, Vol. 5, No. 2 (2021) pp. 87 - 101
  PubDate: 2021-11-11T23:20:50-05:00
  DOI: 10.1504/IJFE.2021.118911
  Issue No: Vol. 5, No. 2 (2021)
A comparative analysis of SIFT, SURF and ORB on sketch and paint based
images
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  Authors: R. Radha, M. Pushpa
  Pages: 102 - 110
  Abstract: 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.
  Keywords: 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
  Citation: International Journal of Forensic Engineering, Vol. 5, No. 2 (2021) pp. 102 - 110
  PubDate: 2021-11-11T23:20:50-05:00
  DOI: 10.1504/IJFE.2021.118910
  Issue No: Vol. 5, No. 2 (2021)
Machine learning techniques for autism spectrum disorder (ASD)
detection
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  Authors: Anshu Sharma, Poonam Tanwar
  Pages: 111 - 125
  Abstract: 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.
  Keywords: machine learning; deep learning; classifier; genetic; dataset; behavioural features
  Citation: International Journal of Forensic Engineering, Vol. 5, No. 2 (2021) pp. 111 - 125
  PubDate: 2021-11-11T23:20:50-05:00
  DOI: 10.1504/IJFE.2021.118912
  Issue No: Vol. 5, No. 2 (2021)
Intrusion detection in forensics based on machine learning techniques:
a review
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  Authors: Fathollah Bistouni, Mohsen Jahanshahi, Kong Fah Tee
  Pages: 126 - 156
  Abstract: 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.
  Keywords: intrusion detection; machine learning; forensics; data mining; supervised learning; unsupervised; semi-supervised
  Citation: International Journal of Forensic Engineering, Vol. 5, No. 2 (2021) pp. 126 - 156
  PubDate: 2021-11-11T23:20:50-05:00
  DOI: 10.1504/IJFE.2021.118915
  Issue No: Vol. 5, No. 2 (2021)
Assessment of factors affecting construction waste: recycled aggregates
and their embodied energy composition
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  Authors: Sara Gharehbaghi, Koorosh Gharehbaghi, Kong Fah Tee
  Pages: 157 - 173
  Abstract: 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.
  Keywords: recycled aggregates; embodied energy; waste minimisation; virgin aggregates; green buildings; GBELS; green building evaluation and labelling system
  Citation: International Journal of Forensic Engineering, Vol. 5, No. 2 (2021) pp. 157 - 173
  PubDate: 2021-11-11T23:20:50-05:00
  DOI: 10.1504/IJFE.2021.118919
  Issue No: Vol. 5, No. 2 (2021)