Background: LBP is a condition with a high prevalence and recurrence rate. This condition has the potential to create a major impact on the individual over extended periods of time. Numerous reasons and factors for lower back pain have been suggested; including age, gender, body mass index (BMI) and physical activity of the patient. There are different treatments and techniques being implemented, however their effects are minimal. Students at University level are at high risk of LBA due to prolonged sitting and standing hours.Methods: Research was conducted on 190 students of ISRA University, Karachi Campus. The duration of the study was 6 months, the participants were randomly selected, who were studying in ISRA University, Karachi Campus and self-administered questionnaires with consent forms were distributed to all the participants. Participants were asked to complete the questionnaire and return it to the concerned person after one week.Results: 71.6% had history of low back pain whereas 28.4% did not have history of low back pain. 84.7% students used computer whereas 15.3% did not use computer. 65.3% left the class room due to low back pain whereas 34.7% did not leave the class room. 61.6% students had prevented normal work from 1-7days due to low back pain during last 12 months, 26.8% prevented normal work from 0 days whereas 11.6% students had prevented normal work from 8-30days due to low back pain during last 12 months. Conclusion:The overall aim of this study was to analyze the prevalence of low back pain among the undergraduate students of ISRA University, Karachi Campus. The study provided a detailed awareness about the level of prevalence of lower back pain among the undergraduate students. The results of this study showed that most of the students were experiencing lower back pain, and it was also found that it is directly related to their work.
Print media plays a key role in raising awareness among people and in the public understanding of various issues. This study discusses the role the print media during COVID-19 pandemic how the print media raises awareness among people in the contagion. Using a quantitative method, the researchers analysed the contents of two leading newspapers of Khyber Pakhtunkhwa, daily Mashriq and Daily Aaj. The study then achieved the results regarding how they reported the stories in various dimensions such as awareness, negative, positive aspects, etc.
The Android mobile platform is the most popular and dominates the cell phone market. With the increasing use of Android, malware developers have become active in circumventing security measures by using various obfuscation techniques. The obfuscation techniques are used to hide the malicious code in the Android applications to evade detection by anti-malware tools. Some attackers use the obfuscation techniques in isolation, while some attackers use a mixed approach (i.e., employing multiple obfuscation techniques simultaneously). Therefore, it is crucial to analyze the impact of the different obfuscation techniques, both when they are used in isolation and when they are combined as hybrid techniques. Several studies have suggested that the obfuscation techniques may be more effective when used in a mixed pattern. However, in most of the related works, the obfuscation techniques used for analysis are either based on individual or a combination of primitive obfuscation techniques. In this work, we provide a comprehensive evaluation of anti-malware tools to gauge the impact of complex hybrid code-obfuscations techniques on malware detection capabilities of the prominent anti-malware tools. The evaluation results show that the inter-category-wise hybridized code obfuscation results in more evasion as compared to the individual or simple hybridized code obfuscations (using multiple and similar code obfuscations) which most of the existing related work employed for the evaluation. Obfuscation techniques significantly impact the detection rate of any anti-malware tool. The remarkable result i.e., almost 100% best detection rate is observed for the seven out of 10 tools when analyzed using the individual obfuscation techniques, four out of 10 tools on category-wise obfuscation, and not a single anti-malware tool attained full detection (i.e., 100%) for inter-category obfuscations.
In the Agriculture sector, control of plant leaf diseases is crucial as it influences the quality and production of plant species with an impact on the economy of any country. Therefore, automated identification and classification of plant leaf disease at an early stage is essential to reduce economic loss and to conserve the specific species. Previously, to detect and classify plant leaf disease, various Machine Learning models have been proposed; however, they lack usability due to hardware incompatibility, limited scalability and inefficiency in practical usage. Our proposed DeepLens Classification and Detection Model (DCDM) approach deal with such limitations by introducing automated detection and classification of the leaf diseases in fruits (apple, grapes, peach and strawberry) and vegetables (potato and tomato) via scalable transfer learning on A.W.S. SageMaker and importing it on A.W.S. DeepLens for real-time practical usability. Cloud integration provides scalability and ubiquitous access to our approach. Our experiments on extensive image data set of healthy and unhealthy leaves of fruits and vegetables showed an accuracy of 98.78% with a real-time diagnosis of plant leaves diseases. We used forty thousand images for the training of deep learning model and then evaluated it on ten thousand images. The process of testing an image for disease diagnosis and classification using A.W.S. DeepLens on average took 0.349s, providing disease information to the user in less than a second.
In the Agriculture sector, control of plant leaf diseases is crucial as it influences the quality and production of plant species with an impact on the economy of any country. Therefore, automated identification and classification of plant leaf disease at an early stage is essential to reduce economic loss and to conserve the specific species. Previously, to detect and classify plant leaf disease, various Machine Learning models have been proposed; however, they lack usability due to hardware incompatibility, limited scalability and inefficiency in practical usage. Our proposed DeepLens Classification and Detection Model (D.C.D.M.) approach deal with such limitations by introducing automated detection and classification of the leaf diseases in fruits (apple, grapes, peach and strawberry) and vegetables (potato and tomato) via scalable transfer learning on A.W.S. SageMaker and importing it on A.W.S. DeepLens for real-time practical usability. Cloud integration provides scalability and ubiquitous access to our approach. Our experiments on extensive image data set of healthy and unhealthy leaves of fruits and vegetables showed an accuracy of 98.78% with a real-time diagnosis of plant leaves diseases. We used forty thousand images for the training of deep learning model and then evaluated it on ten thousand images. The process of testing an image for disease diagnosis and classification using A.W.S. DeepLens on average took 0.349s, providing disease information to the user in less than a second.
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