Muhammad Usman

Azka Rehman, Muhammad Usman, Rabeea Jawaid et al. · stanford

Accurate segmentation of mandibular canals in lower jaws is important in dental implantology. Medical experts determine the implant position and dimensions manually from 3D CT images to avoid damaging the mandibular nerve inside the canal. In this paper, we propose a novel dual-stage deep learning-based scheme for the automatic segmentation of the mandibular canal. Particularly, we first enhance the CBCT scans by employing the novel histogram-based dynamic windowing scheme, which improves the visibility of mandibular canals. After enhancement, we design 3D deeply supervised attention U-Net architecture for localizing the volumes of interest (VOIs), which contain the mandibular canals (i.e., left and right canals). Finally, we employed the multi-scale input residual U-Net architecture (MS-R-UNet) to segment the mandibular canals using VOIs accurately. The proposed method has been rigorously evaluated on 500 scans. The results demonstrate that our technique outperforms the current state-of-the-art segmentation performance and robustness methods.

Muhammad Usman, Nauman bin Ali, Claes Wohlin

Context: Systematic literature reviews (SLRs) have become standard practise as part of software engineering research, although their quality varies. To build on the reviews, both for future research and industry practice, they need to be of high quality. Objective: To assess the quality of SLRs in software engineering, we put forward an appraisal instrument for SLRs. The instrument is intended for use by appraisers of reviews, but authors may also use it as a checklist when designing and documenting their reviews. Method: A well-established appraisal instrument from research in healthcare was used as a starting point to develop a quality assessment instrument. It is adapted to software engineering using guidelines, checklists, and experiences from software engineering. As a validation step, the first version was reviewed by four external experts on SLRs in software engineering and updated based on their feedback. Results: The outcome of the research is an appraisal instrument for the quality assessment of SLRs in software engineering. The instrument intends to support the appraiser in assessing the quality of an SLR. The instrument includes 16 items with different options to capture the quality. The item is assessed on a two or three-grade scale, depending on the item. The instrument also supports consolidating the items into groups, which are then used to assess the overall quality of a systematic literature review. Conclusion: It is concluded that the presented instrument may be helpful support for an appraiser in assessing the quality of SLRs in software engineering.

Nazar Waheed, Xiangjian He, Muhammad Ikram et al.

Security and privacy of the users have become significant concerns due to the involvement of the Internet of things (IoT) devices in numerous applications. Cyber threats are growing at an explosive pace making the existing security and privacy measures inadequate. Hence, everyone on the Internet is a product for hackers. Consequently, Machine Learning (ML) algorithms are used to produce accurate outputs from large complex databases, where the generated outputs can be used to predict and detect vulnerabilities in IoT-based systems. Furthermore, Blockchain (BC) techniques are becoming popular in modern IoT applications to solve security and privacy issues. Several studies have been conducted on either ML algorithms or BC techniques. However, these studies target either security or privacy issues using ML algorithms or BC techniques, thus posing a need for a combined survey on efforts made in recent years addressing both security and privacy issues using ML algorithms and BC techniques. In this paper, we provide a summary of research efforts made in the past few years, starting from 2008 to 2019, addressing security and privacy issues using ML algorithms and BCtechniques in the IoT domain. First, we discuss and categorize various security and privacy threats reported in the past twelve years in the IoT domain. Then, we classify the literature on security and privacy efforts based on ML algorithms and BC techniques in the IoT domain. Finally, we identify and illuminate several challenges and future research directions in using ML algorithms and BC techniques to address security and privacy issues in the IoT domain.

Muhammad Usman, Jeong A Lee

Antifreeze proteins (AFPs) are the sub-set of ice binding proteins indispensable for the species living in extreme cold weather. These proteins bind to the ice crystals, hindering their growth into large ice lattice that could cause physical damage. There are variety of AFPs found in numerous organisms and due to the heterogeneous sequence characteristics, AFPs are found to demonstrate a high degree of diversity, which makes their prediction a challenging task. Herein, we propose a machine learning framework to deal with this vigorous and diverse prediction problem using the manifolding learning through composition of k-spaced amino acid pairs. We propose to use the deep neural network with skipped connection and ReLU non-linearity to learn the non-linear mapping of protein sequence descriptor and class label. The proposed antifreeze protein prediction method called AFP-CKSAAP has shown to outperform the contemporary methods, achieving excellent prediction scores on standard dataset. The main evaluater for the performance of the proposed method in this study is Youden's index whose high value is dependent on both sensitivity and specificity. In particular, AFP-CKSAAP yields a Youden's index value of 0.82 on the independent dataset, which is better than previous methods.