International Islamic University, Islamabad

IMPORTANCE: The Global Burden of Diseases, Injuries, and Risk Factors Study 2019 (GBD 2019) provided systematic estimates of incidence, morbidity, and mortality to inform local and international efforts toward reducing cancer burden. OBJECTIVE: To estimate cancer burden and trends globally for 204 countries and territories and by Sociodemographic Index (SDI) quintiles from 2010 to 2019. EVIDENCE REVIEW: The GBD 2019 estimation methods were used to describe cancer incidence, mortality, years lived with disability, years of life lost, and disability-adjusted life years (DALYs) in 2019 and over the past decade. Estimates are also provided by quintiles of the SDI, a composite measure of educational attainment, income per capita, and total fertility rate for those younger than 25 years. Estimates include 95% uncertainty intervals (UIs). FINDINGS: In 2019, there were an estimated 23.6 million (95% UI, 22.2-24.9 million) new cancer cases (17.2 million when excluding nonmelanoma skin cancer) and 10.0 million (95% UI, 9.36-10.6 million) cancer deaths globally, with an estimated 250 million (235-264 million) DALYs due to cancer. Since 2010, these represented a 26.3% (95% UI, 20.3%-32.3%) increase in new cases, a 20.9% (95% UI, 14.2%-27.6%) increase in deaths, and a 16.0% (95% UI, 9.3%-22.8%) increase in DALYs. Among 22 groups of diseases and injuries in the GBD 2019 study, cancer was second only to cardiovascular diseases for the number of deaths, years of life lost, and DALYs globally in 2019. Cancer burden differed across SDI quintiles. The proportion of years lived with disability that contributed to DALYs increased with SDI, ranging from 1.4% (1.1%-1.8%) in the low SDI quintile to 5.7% (4.2%-7.1%) in the high SDI quintile. While the high SDI quintile had the highest number of new cases in 2019, the middle SDI quintile had the highest number of cancer deaths and DALYs. From 2010 to 2019, the largest percentage increase in the numbers of cases and deaths occurred in the low and low-middle SDI quintiles. CONCLUSIONS AND RELEVANCE: The results of this systematic analysis suggest that the global burden of cancer is substantial and growing, with burden differing by SDI. These results provide comprehensive and comparable estimates that can potentially inform efforts toward equitable cancer control around the world.

Development of plant based nanoparticles has many advantages over conventional physico-chemical methods and has various applications in medicine and biology. In present study, zinc oxide (ZnO) nanoparticles (NPs) were synthesized using leaf extracts of two medicinal plants Cassia fistula and Melia azadarach. 0.01 M zinc acetate dihydrate was used as a precursor in leaf extracts of respective plants for NPs synthesis. The structural and optical properties of NPs were investigated by X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, scanning electron microscope (SEM), ultraviolet-visible spectrophotometer (UV-Vis) and dynamic light scattering (DLS). The antibacterial potential of ZnO NPs was examined by paper disc diffusion method against two clinical strains of Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) based on the zone of inhibition and minimal inhibitory indices (MIC). Change in color of the reaction mixture from brown to white indicated the formation of ZnO NPs. UV peaks at 320 nm and 324 nm, and XRD pattern matching that of JCPDS card for ZnO confirmed the presence of pure ZnO NPs. FTIR further confirmed the presence of bioactive functional groups involved in the reduction of bulk zinc acetate to ZnO NPs. SEM analysis displayed the shape of NPs to be spherical whereas DLS showed their size range from 3 to 68 nm. The C. fistula and M. azadarach mediated ZnO NPs showed strong antimicrobial activity against clinical pathogens compared to standard drugs, suggesting that plant based synthesis of NPs can be an excellent strategy to develop versatile and eco-friendly biomedical products.

Purpose The purpose of this article is to examine the influence of ownership structure on corporate social responsibility (CSR) disclosure in Malaysian company annual reports (CARs). Design/methodology/approach The study uses a CSR disclosure checklist to measure the extent of CSR disclosure in annual reports and a multiple regression analysis to examine the association between ownership structure and the extent of CSR disclosure in annual reports. Findings The paper finds that, even among the larger and actively traded stocks in Malaysia, there is considerable variability in the amount of social activities disclosed in corporate annual reports. Results from multiple regression analysis show that, consistent with expectations, companies in which the directors hold a higher proportion of equity shares (owner‐managed companies) disclosed significantly less CSR information, while companies in which the government is a substantial shareholder disclosed significantly more CSR information in their annual reports. Research limitations/implications The sample for this study comes from larger and actively traded stocks on the Bursa Malaysia. Thus, the results may not be generalizable to smaller and less actively traded stocks. Practical implications The findings appear to suggest that the level of CSR disclosure in annual reports of companies depends on the extent of “public pressure” faced by each company. The results also raise the question of whether corporate involvement in social activities should be made a mandatory disclosure in annual reports to better assess the extent of “corporate citizenship” of Malaysian companies. Originality/value The study finds that ownership structure, which had been ignored in prior studies on factors influencing CSR disclosure, has an impact on CSR disclosure.

The explosion of social media allowed individuals to spread information without cost, with little investigation and fewer filters than before. This amplified the old problem of fake news, which became a major concern nowadays due to the negative impact it brings to the communities. In order to tackle the rise and spreading of fake news, automatic detection techniques have been researched building on artificial intelligence and machine learning. The recent achievements of deep learning techniques in complex natural language processing tasks, make them a promising solution for fake news detection too. This work proposes a novel hybrid deep learning model that combines convolutional and recurrent neural networks for fake news classification. The model was successfully validated on two fake news datasets (ISO and FA-KES), achieving detection results that are significantly better than other non-hybrid baseline methods. Further experiments on the generalization of the proposed model across different datasets, had promising results.

Purpose – Muslims living in multi-religious societies are considered more conscious about the permissibility ( Halal ) of products and thus the majority of Halal research in the non-financial sector was conducted in multi-ethnic societies. Nonetheless, the global trade is changing the way we perceive the origin of products and brands and their permissibility under Islamic Sharia laws. This apparently has serious implications for international companies operating in food, cosmetics and pharmaceutical products. The purpose of this paper is to investigate the role of Muslim attitude towards Halal products, their subjective norms and religiosity in predicting intention to choose Halal products. Design/methodology/approach – A structured question was designed to elicit consumer attitude, subjective norms, intention to choose Halal products and degree of inter and intra personal religiosity. Data were collected from 180 adult respondents using a convenience sampling method. Only 150 responses were deemed suitable for further analysis, yielding a response rate of 83 per cent. Stepwise regression analysis was used to test the proposed model. Findings – The results indicated that theory of reasoned action (TRA) is a valid model in predicting intention to choose Halal products. The results further indicate that subjective norms ( β =0.455, p , 0.001), attitude towards the Halal products ( β =0.265, p , 0.001) and intra personal religiosity ( β =0.167, p , 0.001) positively influence attitude towards the Halal products. Interestingly, subjective norm appears to be the strongest of all the predictors for choosing Halal products. Research limitations/implications – The data collected for the current study investigate global attitude towards Halal products. It would be interesting if future researchers examine consumers ' attitude towards specific Halal products for specific product categories. Practical implications – It is argued in this research that the presence of strong attitude towards Halal products in Muslim consumers might play an important role in exclusion or inclusion of brands, based on their conformance to Halal requirements. Originality/value – The paper extends the applicability of the theory of reasoned action model by investigating the role of inter-personal and intra-personal religiosity in intention to choose Halal products.

The key objective of the present proposed work in this paper is introduced a new version of picture fuzzy set so called spherical fuzzy sets (SFS). spherical fuzzy set is a new extension of picture fuzzy sets and Pythagorean fuzzy sets. In spherical fuzzy sets, membership degrees are gratifying the condition 0 ⩽ P2 (x) + I2 (x) + N2 (x) ⩽1 instead of 0 ⩽ P (x) + I (x) + N (x) ⩽1 as is in picture fuzzy sets. In this paper, we investigate the basic operations of spherical fuzzy sets and discuss some related results. We extend operational laws to aggregation operators and introduce weighted averaging and weighted geometric aggregation operators based on spherical fuzzy number’s. Further a multi attribute decision making method is developed and these aggregation operators are utilized. Finally, we constructed a numerical approach for implementation of proposed technique.

The concept of complex fuzzy set (CFS) and complex intuitionistic fuzzy set (CIFS) is two recent developments in the field of fuzzy set (FS) theory. The significance of these concepts lies in the fact that these concepts assigned membership grades from unit circle in plane, i.e., in the form of a complex number instead from [0, 1] interval. CFS cannot deal with information of yes and no type, while CIFS works only for a limited range of values. To deal with these kinds of problems, in this article, the concept of complex Pythagorean fuzzy set (CPFS) is developed. The novelty of CPFS lies in its larger range comparative to CFS and CIFS which is demonstrated numerically. It is discussed how a CFS and CIFS could be CPFS but not conversely. We investigated the very basic concepts of CPFSs and studied their properties. Furthermore, some distance measures for CPFSs are developed and their characteristics are studied. The viability of the proposed new distance measures in a building material recognition problem is also discussed. Finally, a comparative study of the proposed new work is established with pre-existing study and some advantages of CPFS are discussed over CFS and CIFS.

This study examined the influence of pyrolysis temperature on biochar characteristics and evaluated its suitability for carbon capture and energy production. Biochar was produced from corn stover using slow pyrolysis at 300, 400 and 500°C and 2 hrs holding time. The experimental biochars were characterized by elemental analysis, BET, FTIR, TGA/DTA, NMR (C-13). Higher heating value (HHV) of feedstock and biochars was measured using bomb calorimeter. Results show that carbon content of corn stover biochar increased from 45.5% to 64.5%, with increasing pyrolysis temperatures. A decrease in H:C and O:C ratios as well as volatile matter, coupled with increase in the concentration of aromatic carbon in the biochar as determined by FTIR and NMR (C-13) demonstrates a higher biochar carbon stability at 500°C. It was estimated that corn stover pyrolysed at 500°C could provide of 10.12 MJ/kg thermal energy. Pyrolysis is therefore a potential technology with its carbon-negative, energy positive and soil amendment benefits thus creating win- win scenario.

Human’s quest for innovation, finding solutions of problems, and upgrading the industrial yield with energy efficient and cost-effective materials has opened the avenues of nanotechnology. Among a variety of nanoparticles, zinc oxide nanoparticles (ZnO) have advantages because of the extraordinary physical and chemical properties. It is one of the cheap materials in cosmetic industry, nanofertilizers, and electrical devices and also a suitable agent for bioimaging and targeted drug and gene delivery and an excellent sensor for detecting ecological pollutants and environmental remediation. Despite inherent toxicity of nanoparticles, synthetic routes are making use of large amount of chemical and stringent reactions conditions that are contributing as environmental contaminants in the form of high energy consumption, heat generation, water consumption, and chemical waste. Further, it is also adding to the innate toxicity of nanoparticles (NPs) that is either entirely ignored or poorly investigated. The current review illustrates a comparison between pollutants and hazards spawned from chemical, physical, and biological methods used for the synthesis of ZnO. Further, the emphasis is on devising eco-friendly techniques for the synthesis of ZnO especially biological methods which are comparatively less hazardous and need to be optimized by controlling the reaction conditions in order to get desired yield and characteristics.

Purpose This paper aims to investigate the relationship between religiosity and new product adoption (NPA) among Muslim consumers. Design/methodology/approach A total of 300 questionnaires were distributed to university students. Religiosity represented the independent variable and was measured using five dimensions: ideological, ritualistic, intellectual, consequential and experimental dimensions. NPA represented the dependent variable. Findings Religiosity affects NPA among Muslim consumers; their beliefs influence how and what products they adopt. Originality/value This is the first paper to investigate the relationship between religiosity and NPA, not only in Pakistan but also in the entire Islamic market. Moreover, this is a relatively new issue that remains largely undiscovered by researchers worldwide. This paper will help to emphasize its importance and implications to business decisions.

Modern research has revealed that dietary consumption of flavonoids and flavonoids-rich foods significantly improve cognitive capabilities, inhibit or delay the senescence process and related neurodegenerative disorders including Alzheimer's disease (AD). The flavonoids rich foods such as green tea, cocoa, blue berry and other foods improve the various states of cognitive dysfunction, AD and dementia-like pathological alterations in different animal models. The mechanisms of flavonoids have been shown to be mediated through the inhibition of cholinesterases including acetylcholinesterase (AChE), and butyrylcholinesterase (BChE), β-secretase (BACE1), free radicals and modulation of signaling pathways, that are implicated in cognitive and neuroprotective functions. Flavonoids interact with various signaling protein pathways like ERK and PI3-kinase/Akt and modulate their actions, thereby leading to beneficial neuroprotective effects. Moreover, they enhance vascular blood flow and instigate neurogenesis particularly in the hippocampus. Flavonoids also hamper the progression of pathological symptoms of neurodegenerative diseases by inhibiting neuronal apoptosis induced by neurotoxic substances including free radicals and β-amyloid proteins (Aβ). All these protective mechanisms contribute to the maintenance of number, quality of neurons and their synaptic connectivity in the brain. Thus flavonoids can thwart the progression of age-related disorders and can be a potential source for the design and development of new drugs effective in cognitive disorders.

In the era of globalisation and dynamic market, firms look for competitive advantage and survival using different sources and resources. Prior studies have indicated that Business Model Innovation (BMI) is a core driver for firm’s survival and superior performance especially in growing industries. However, the role of BMI has been discussed theoretically and exploratory while empirical studies are still lacking. Hence, this study examines the importance of BMI in SME performance and the mediating role of competitive advantage. Data were collected through structured questionnaires using a sample size of 303 manufacturing SMEs operating in the emerging market of Pakistan. Hypotheses were tested through Structural Equation Modelling (SEM) using AMOS.21. The results indicate that BMI has a significant positive impact on competitive advantage and SME performance. Competitive advantage partially mediates the relationship between BMI and SME performance. Firms are required to create an effective business model to acquire competitive advantage and superior financial performance. Implications for practice have been discussed.

Psychological differences between women and men, far from being invariant as a biological explanation would suggest, fluctuate in magnitude across cultures. Moreover, contrary to the implications of some theoretical perspectives, gender differences in personality, values, and emotions are not smaller, but larger, in American and European cultures, in which greater progress has been made toward gender equality. This research on gender differences in self-construals involving 950 participants from 5 nations/cultures (France, Belgium, the Netherlands, the United States, and Malaysia) illustrates how variations in social comparison processes across cultures can explain why gender differences are stronger in Western cultures. Gender differences in the self are a product of self-stereotyping, which occurs when between-gender social comparisons are made. These social comparisons are more likely, and exert a greater impact, in Western nations. Both correlational and experimental evidence supports this explanation.

BACKGROUND: For more than three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has provided a framework to quantify health loss due to diseases, injuries, and associated risk factors. This paper presents GBD 2023 findings on disease and injury burden and risk-attributable health loss, offering a global audit of the state of world health to inform public health priorities. This work captures the evolving landscape of health metrics across age groups, sexes, and locations, while reflecting on the remaining post-COVID-19 challenges to achieving our collective global health ambitions. METHODS: The GBD 2023 combined analysis estimated years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) for 375 diseases and injuries, and risk-attributable burden associated with 88 modifiable risk factors. Of the more than 310 000 total data sources used for all GBD 2023 (about 30% of which were new to this estimation round), more than 120 000 sources were used for estimation of disease and injury burden and 59 000 for risk factor estimation, and included vital registration systems, surveys, disease registries, and published scientific literature. Data were analysed using previously established modelling approaches, such as disease modelling meta-regression version 2.1 (DisMod-MR 2.1) and comparative risk assessment methods. Diseases and injuries were categorised into four levels on the basis of the established GBD cause hierarchy, as were risk factors using the GBD risk hierarchy. Estimates stratified by age, sex, location, and year from 1990 to 2023 were focused on disease-specific time trends over the 2010-23 period and presented as counts (to three significant figures) and age-standardised rates per 100 000 person-years (to one decimal place). For each measure, 95% uncertainty intervals [UIs] were calculated with the 2·5th and 97·5th percentile ordered values from a 250-draw distribution. FINDINGS: Total numbers of global DALYs grew 6·1% (95% UI 4·0-8·1), from 2·64 billion (2·46-2·86) in 2010 to 2·80 billion (2·57-3·08) in 2023, but age-standardised DALY rates, which account for population growth and ageing, decreased by 12·6% (11·0-14·1), revealing large long-term health improvements. Non-communicable diseases (NCDs) contributed 1·45 billion (1·31-1·61) global DALYs in 2010, increasing to 1·80 billion (1·63-2·03) in 2023, alongside a concurrent 4·1% (1·9-6·3) reduction in age-standardised rates. Based on DALY counts, the leading level 3 NCDs in 2023 were ischaemic heart disease (193 million [176-209] DALYs), stroke (157 million [141-172]), and diabetes (90·2 million [75·2-107]), with the largest increases in age-standardised rates since 2010 occurring for anxiety disorders (62·8% [34·0-107·5]), depressive disorders (26·3% [11·6-42·9]), and diabetes (14·9% [7·5-25·6]). Remarkable health gains were made for communicable, maternal, neonatal, and nutritional (CMNN) diseases, with DALYs falling from 874 million (837-917) in 2010 to 681 million (642-736) in 2023, and a 25·8% (22·6-28·7) reduction in age-standardised DALY rates. During the COVID-19 pandemic, DALYs due to CMNN diseases rose but returned to pre-pandemic levels by 2023. From 2010 to 2023, decreases in age-standardised rates for CMNN diseases were led by rate decreases of 49·1% (32·7-61·0) for diarrhoeal diseases, 42·9% (38·0-48·0) for HIV/AIDS, and 42·2% (23·6-56·6) for tuberculosis. Neonatal disorders and lower respiratory infections remained the leading level 3 CMNN causes globally in 2023, although both showed notable rate decreases from 2010, declining by 16·5% (10·6-22·0) and 24·8% (7·4-36·7), respectively. Injury-related age-standardised DALY rates decreased by 15·6% (10·7-19·8) over the same period. Differences in burden due to NCDs, CMNN diseases, and injuries persisted across age, sex, time, and location. Based on our risk analysis, nearly 50% (1·27 billion [1·18-1·38]) of the roughly 2·80 billion total global DALYs in 2023 were attributable to the 88 risk factors analysed in GBD. Globally, the five level 3 risk factors contributing the highest proportion of risk-attributable DALYs were high systolic blood pressure (SBP), particulate matter pollution, high fasting plasma glucose (FPG), smoking, and low birthweight and short gestation-with high SBP accounting for 8·4% (6·9-10·0) of total DALYs. Of the three overarching level 1 GBD risk factor categories-behavioural, metabolic, and environmental and occupational-risk-attributable DALYs rose between 2010 and 2023 only for metabolic risks, increasing by 30·7% (24·8-37·3); however, age-standardised DALY rates attributable to metabolic risks decreased by 6·7% (2·0-11·0) over the same period. For all but three of the 25 leading level 3 risk factors, age-standardised rates dropped between 2010 and 2023-eg, declining by 54·4% (38·7-65·3) for unsafe sanitation, 50·5% (33·3-63·1) for unsafe water source, and 45·2% (25·6-72·0) for no access to handwashing facility, and by 44·9% (37·3-53·5) for child growth failure. The three leading level 3 risk factors for which age-standardised attributable DALY rates rose were high BMI (10·5% [0·1 to 20·9]), drug use (8·4% [2·6 to 15·3]), and high FPG (6·2% [-2·7 to 15·6]; non-significant). INTERPRETATION: Our findings underscore the complex and dynamic nature of global health challenges. Since 2010, there have been large decreases in burden due to CMNN diseases and many environmental and behavioural risk factors, juxtaposed with sizeable increases in DALYs attributable to metabolic risk factors and NCDs in growing and ageing populations. This long-observed consequence of the global epidemiological transition was only temporarily interrupted by the COVID-19 pandemic. The substantially decreasing CMNN disease burden, despite the 2008 global financial crisis and pandemic-related disruptions, is one of the greatest collective public health successes known. However, these achievements are at risk of being reversed due to major cuts to development assistance for health globally, the effects of which will hit low-income countries with high burden the hardest. Without sustained investment in evidence-based interventions and policies, progress could stall or reverse, leading to widespread human costs and geopolitical instability. Moreover, the rising NCD burden necessitates intensified efforts to mitigate exposure to leading risk factors-eg, air pollution, smoking, and metabolic risks, such as high SBP, BMI, and FPG-including policies that promote food security, healthier diets, physical activity, and equitable and expanded access to potential treatments, such as GLP-1 receptor agonists. Decisive, coordinated action is needed to address long-standing yet growing health challenges, including depressive and anxiety disorders. Yet this can be only part of the solution. Our response to the NCD syndemic-the complex interaction of multiple health risks, social determinants, and systemic challenges-will define the future landscape of global health. To ensure human wellbeing, economic stability, and social equity, global action to sustain and advance health gains must prioritise reducing disparities by addressing socioeconomic and demographic determinants, ensuring equitable health-care access, tackling malnutrition, strengthening health systems, and improving vaccination coverage. We live in times of great opportunity. FUNDING: Gates Foundation and Bloomberg Philanthropies.

With the rise of new technologies, such as the Internet of Things, raising the productivity of agricultural and farming activities is critical to improving yields and cost-effectiveness. IoT, in particular, can improve the efficiency of agriculture and farming processes by eliminating human intervention through automation. The fast rise of Internet of Things (IoT)-based tools has changed nearly all life sectors, including business, agriculture, surveillance, etc. These radical developments are upending traditional agricultural practices and presenting new options in the face of various obstacles. IoT aids in collecting data that is useful in the farming sector, such as changes in climatic conditions, soil fertility, amount of water required for crops, irrigation, insect and pest detection, bug location disruption of creatures to the sphere, and horticulture. IoT enables farmers to effectively use technology to monitor their forms remotely round the clock. Several sensors, including distributed WSNs (wireless sensor networks), are utilized for agricultural inspection and control, which is very important due to their exact output and utilization. In addition, cameras are utilized to keep an eye on the field from afar. The goal of this research is to evaluate smart agriculture using IoT approaches in depth. The paper demonstrates IoT applications, benefits, current obstacles, and potential solutions in smart agriculture. This smart agricultural system aims to find existing techniques that may be used to boost crop yield and save time, such as water, pesticides, irrigation, crop, and fertilizer management.

Deteriorating soil quality and decrease in vegetation abundance are grave consequences of open waste dumping which have resulted in growing public concern. The focus of this study is to assess the contribution of open waste dumping in soil contamination and its effect on plant diversity in one of the renowned green cities of Pakistan. Surface soil samples (n = 12 + 12) were collected from both the open waste dumping areas allocated by Capital Development Authority (CDA) and sub- sectors of H-belt of Islamabad city (representative of control site). The diversity of vegetation was studied at both sampling sites. Significant modifications were observed in the soil properties of the dumping sites. Soils at the disposal sites showed high pH, TDS and EC regime in comparison to control sites. Various heavy metal concentrations i.e., Lead (Pb), Copper (Cu), Nickel (Ni), Chromium (Cr) and Zinc (Zn) were also found to be higher at the dumping sites except for Cadmium (Cd) which had a higher value in control site. A similar trend was observed in plant diversity. Control sites showed diversified variety of plants i.e., 44 plant species while this number reduced to only 32 plant species at the disposal sites. This is attributed to changes in soil characteristics at disposal sites and in its vicinity areas.

A brain tumor is an uncontrolled development of brain cells in brain cancer if not detected at an early stage. Early brain tumor diagnosis plays a crucial role in treatment planning and patients' survival rate. There are distinct forms, properties, and therapies of brain tumors. Therefore, manual brain tumor detection is complicated, time-consuming, and vulnerable to error. Hence, automated computer-assisted diagnosis at high precision is currently in demand. This article presents segmentation through Unet architecture with ResNet50 as a backbone on the Figshare data set and achieved a level of 0.9504 of the intersection over union (IoU). The preprocessing and data augmentation concept were introduced to enhance the classification rate. The multi-classification of brain tumors is performed using evolutionary algorithms and reinforcement learning through transfer learning. Other deep learning methods such as ResNet50, DenseNet201, MobileNet V2, and InceptionV3 are also applied. Results thus obtained exhibited that the proposed research framework performed better than reported in state of the art. Different CNN, models applied for tumor classification such as MobileNet V2, Inception V3, ResNet50, DenseNet201, NASNet and attained accuracy 91.8, 92.8, 92.9, 93.1, 99.6%, respectively. However, NASNet exhibited the highest accuracy.

Melanoma is considered a fatal type of skin cancer. However, it is sometimes hard to distinguish it from nevus due to their identical visual appearance and symptoms. The mortality rate because of this disease is higher than all other skin-related consolidated malignancies. The number of cases is growing among young people, but if it is diagnosed at an earlier stage, then the survival rates become very high. The cost and time required for the doctors to diagnose all patients for melanoma are very high. In this paper, we propose an intelligent system to detect and distinguish melanoma from nevus by using the state-of-the-art image processing techniques. At first, the Gaussian filter is used for removing noise from the skin lesion of the acquired images followed by the use of improved K-mean clustering to segment out the lesion. A distinctive hybrid superfeature vector is formed by the extraction of textural and color features from the lesion. Support vector machine (SVM) is utilized for the classification of skin cancer into melanoma and nevus. Our aim is to test the effectiveness of the proposed segmentation technique, extract the most suitable features, and compare the classification results with the other techniques present in the literature. The proposed methodology is tested on the DERMIS dataset having a total number of 397 skin cancer images: 146 are melanoma and 251 are nevus skin lesions. Our proposed methodology archives encouraging results having 96% accuracy.

This work is concerned with the viscous flow due to a curved stretching sheet. The similarity solution of the problem is obtained numerically by a shooting method using the Runge–Kutta algorithm. The physical quantities of interest like the fluid velocity and skin friction coefficient are obtained and discussed under the influence of dimensionless curvature. It is evident from the results that dimensionless curvature causes an increase in boundary layer thickness and a decrease in the skin friction coefficient.

Melanoma is considered the most serious type of skin cancer. All over the world, the mortality rate is much high for melanoma in contrast with other cancer. There are various computer-aided solutions proposed to correctly identify melanoma cancer. However, the difficult visual appearance of the nevus makes it very difficult to design a reliable Computer-Aided Diagnosis (CAD) system for accurate melanoma detection. Existing systems either uses traditional machine learning models and focus on handpicked suitable features or uses deep learning-based methods that use complete images for feature learning. The automatic and most discriminative feature extraction for skin cancer remains an important research problem that can further be used to better deep learning training. Furthermore, the availability of the limited available images also creates a problem for deep learning models. From this line of research, we propose an intelligent Region of Interest (ROI) based system to identify and discriminate melanoma with nevus cancer by using the transfer learning approach. An improved k-mean algorithm is used to extract ROIs from the images. These ROI based approach helps to identify discriminative features as the images containing only melanoma cells are used to train system. We further use a Convolutional Neural Network (CNN) based transfer learning model with data augmentation for ROI images of DermIS and DermQuest datasets. The proposed system gives 97.9% and 97.4% accuracy for DermIS and DermQuest respectively. The proposed ROI based transfer learning approach outperforms existing methods that use complete images for classification.