This paper presents a novel technique for segmentation of skin lesion in dermoscopic images based on wavelet transform along with morphological operations. The acquired dermoscopic images may include artifacts inform of gel, dense hairs and water bubble which make accurate segmentation more challenging. We have also embodied an efficient approach for artifacts removal and hair inpainting, to enhance the overall segmentation results. In proposed research, color space is also analyzed and selection of blue channel for lesion segmentation have confirmed better performance than techniques which utilizes gray scale conversion. We tackle the problem by finding the most suitable mother wavelet for skin lesion segmentation. The performance achieved with ‘bior6.8’ Cohen–Daubechies–Feauveau biorthogonal wavelet is found to be superior as compared to other wavelet family. The proposed methodology achieves 93.87 % accuracy on dermoscopic images of PH2 dataset acquired at Dermatology Service of Hospital Pedro Hispano, Matosinhos, Portugal.

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End energy user is dependent on fossil fuel-based main-grid and contributes toward greenhouse gasses (GHG) emissions. Changing its energy source will change the dynamics of the power plant, contribution towards GHG production. This case study aims to highlight the minute but positive role of a single end energy user, invisible to the main grid in GHG mitigations through photovoltaic energy source, selected among Pakistan’s top 10 most populous cities as per census 2017. Quetta is a selected city in Pakistan as the best fit location based on annual average daily solar radiations (AADSR) data retrieved from National Aeronautics and Space Administration (NASA) meteorological data. Helioscope software is used to select −15° tilt and 180° azimuthal angles, which further increased Quetta’s AADSR value from 5.54 kWh/m2/d to 5.93 kWh/m2/d. For research significance, a realistic approach is undertaken by proper selection of solar panel type based on Quetta’s annual average temperature, load categorization, user selection and inputs from a solar energy expert. Finally, initial cost, investment and GHG mitigation analysis are carried out in RETScreen Expert software, which validates the minute but the prominent role of a single, end energy user by mitigating 122 tons of CO2 in 25-year project life span. Further, the proposed project favors end-user financially by recovering its $4501 initial cost in less than four years by effectively meeting its energy demand and saving $1195 per annum.

Brain tumors reduce life expectancy due to the lack of a cure. Moreover, their diagnosis involves complex and costly procedures such as magnetic resonance imaging (MRI) and lengthy, careful examination to determine their severity. However, the timely diagnosis of brain tumors in their early stages may save a patient’s life. Therefore, this work utilizes MRI with a machine learning approach to diagnose brain tumor severity (glioma, meningioma, no tumor, and pituitary) in a timely manner. MRI Gaussian and nonlinear scale features are extracted due to their robustness over rotation, scaling, and noise issues, which are common in image processing features such as texture, local binary patterns, histograms of oriented gradient, etc. For the features, each MRI is broken down into multiple small 8 × 8-pixel MR images to capture small details. To counter memory issues, the strongest features based on variance are selected and segmented into 400 Gaussian and 400 nonlinear scale features, and these features are hybridized against each MRI. Finally, classical machine learning classifiers are utilized to check the performance of the proposed hybrid feature vector. An available online brain MRI image dataset is utilized to validate the proposed approach. The results show that the support vector machine-trained model has the highest classification accuracy of 95.33%, with a low computational time. The results are also compared with the recent literature, which shows that the proposed model can be helpful for clinicians/doctors for the early diagnosis of brain tumors.

Purpose Link prediction in social networks refers toward inferring the new interactions among the users in near future. Citation networks are constructed based on citing each other papers. Reciprocal link prediction in citations networks refers toward inferring about getting a citation from an author, whose work is already cited by you. The paper aims to discuss these issues. Design/methodology/approach In this paper, the authors study the extent to which the information of a two-way citation relationship (called reciprocal) is predictable. The authors propose seven different features based on papers, their authors and citations of each paper to predict reciprocal links. Findings Extensive experiments are performed on CiteSeer data set by using three classification algorithms (decision trees, Naive Bayes, and support vector machines) to analyze the impact of individual, category wise and combination of features. The results reveal that it is likely to precisely predict 96 percent of reciprocal links. The study delivers convincing evidence of presence of the underlying equilibrium amongst reciprocal links. Research limitations/implications It is not a generic method for link prediction which can work for different networks with relevant features and parameters. Practical implications This paper predicts the reciprocal links to show who is citing your work to collaborate with them in future. Social implications The proposed method will be helpful in finding collaborators and developing academic links. Originality/value The proposed method uses reciprocal link prediction for bibliographic networks in a novel way.