International Journal of Innovations in Science & Technology
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Role of Agile Methodologies for Ensuring Quality in Complex Systems: A Systematic Literature Review
Get PDFIn software development, the selection of a software process model set the base for the success of a software product. An inappropriate selection may lead to a delay in project release, introduce defects and make the project difficult to update. This lack of quality characteristics may lead to the risk of losing customer expectations as well as the failure of the project itself. In the case of complex systems, the problems become more severe. To meet such expectations, agile methodologies are used to ensure quality in software and meet customers’ expectations. There is currently no literature that gives insights into the role of agile methodologies in ensuring quality in complex systems. The purpose of this paper is to evaluate the effectiveness and the impact of agile methodology in achieving the quality of complex systems.
For this, we perform a Systematic Literature Review (SLR) and define a review protocol. By performing a thorough search and screening, we selected 39 papers related to agile methods and complex systems. Our analysis shows that complex systems have various requirements of quality attributes some of the complex systems mainly focus on security, reliability, and efficiency whereas other emphasizes safety, response time, and maintainability. Our analysis also shows that agile methodologies are widely used for the development of complex systems because ensuring the quality requirements of complex systems is not possible with the use of traditional methods of software development
Enterprise Network Infrastructure Malicious Activity Analysis: Malicious Activity Analysis
No full textInter and intra-network connectivity have become a useful resource for accessibility and flexibility of data for different organizations. Online services are increasing day by day, everything is available online, it generates a huge amount of data, that require cyber security revolves for ensuring secure interconnectivity between devices. Because of an exponential increase in internet users and cyber-attacks, the data security and credibility of various organizations is on stake. In the continued development of the threat environment, cyber security experts deal with numerous threats on daily basis. As multiple attacks on computer networks and systems are becoming stronger each day therefore current security tools are often inadequate to resolve issues relating to unauthorized users, reliability, and reliable network security. To maintain a safe environment, Intrusion-Detection Mechanisms (IDS) enabled to control device functions and detect intrusions should typically be used to supplement with other protection strategies; for which conventional security methods are inadequate. Actual users expect their requested information to be processed in real-time, while malicious traffic needs to be mitigated just as quickly as possible. As traffic increases, this problem becomes more complex. This paper contributes a detailed analysis of network packets to find anomaly detection based on the UNSW NB 15 dataset and investigate the the difference between IP packet behavior for both malicious and legitimate packets. Besides we acquaint with new methodologies to illuminate and appraise the network attack in a very proficient way using different machine learning algorithms which will accomplish locating the malicious traffic in the least execution time with precision
Diastolic Dysfunction Prediction with Symptoms Using Machine Learning Approach
Get PDFCardiac disease is the major cause of deaths all over the world, with 17.9 million deaths annually, as per World Health Organization reports. The purpose of this study is to enable a cardiologist to early predict the patient’s condition before performing the echocardiography test. This study aims to find out whether diastolic function or diastolic dysfunction using symptoms through machine learning. We used the unexplored dataset of diastolic dysfunction disease in this study and checked the symptoms with cardiologist to be enough to predict the disease. For this study, the records of 1285 patients were used, out of which 524 patients had diastolic function and the other 761 patients had diastolic dysfunction. The input parameters considered in this detection include patient age, gender, BP systolic, BP diastolic, BSA, BMI, hypertension, obesity, and Shortness of Breath (SOB). Various machine learning algorithms were used for this detection including Random Forest, J.48, Logistic Regression, and Support Vector Machine algorithms. As a result, with an accuracy of 85.45%, Logistic Regression provided promising results and proved efficient for early prediction of cardiac disease. Other algorithms had an accuracy as follow, J.48 (85.21%), Random Forest (84.94%), and SVM (84.94%). Using a machine learning tool and a patient’s dataset of diastolic dysfunction, we can declare either a patient has cardiac disease or not
Realization of Presentation layer information of Legacy Java Enterprise Applications Through Design Pattern’s Recovery
Get PDFThe presentation layer is the outermost layer of an application that providesuser interface and communication services. This layer is responsible for session management, controlling client access, and validations within data from the client.In legacy enterprise applications like Java Enterprise Edition Platform (Java EE),thedesign considerations of the presentation layer are spread over different design patterns and cross-language constructs. Resultantly, the analysis of such applications becomes quite challenging due to their heterogeneity, essentially requiredforthe extraction of design-level information and furthermodernization. In this research,a flexible technique is presented to extract presentation tier information based on customizable feature types by recovering instances of presentation tier patterns of the Java Enterprise EditionPlatform.The proposed approach is evaluated on well-operative open-source Enterprise Applications. The validation resultsdemonstratethe extraction of presentation tier information through Design Pattern’s recovery.This prototype is validated on the repository of source code of Java applications as well on open source java applications.
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Melanoma Detection Using a Deep Learning Approach
Get PDFMelanoma is a skin lesion disease; it is a skin cancer that is caused by uncontrolled growth in melanocytic tissues. Damaged cells can cause damage to nearby cells and consequently spreads cancer in other parts of the body. The aim of this research is the early detection of Melanoma disease, many researchers have already struggled and achieved success in detecting melanoma with different values for their evaluation parameters, they used different machine learning as well as deep learning approaches, and we applied deep learning approach for Melanoma detection, we used publicly available dataset for experimentation purpose. We applied deep learning algorithms ResNet50 and VGG16 for Melanoma detection; the accuracy, precision, recall, Jaccard index, and dice co-efficient of our proposed model are 92.3%, 93.3%, 90%, 9.98%, and 97.7%, respectively. Our proposed algorithm can be used to increase chances of survival for patients and can save the money which is used for diagnosis and treatment of Melanoma every year.
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Quack Finder: A Probabilistic Approach
Get PDFQuackery is profoundly rooted in rural areas, , but cities are also considerably affected. Countries, whether developed or developing, are experiencing this curse. Quackery is one of the primary reasons for the recent AIDS epidemic among children in Pakistan. In this research, we have conducted two separate surveys to study the causes of quackery. The sample sizes for the public and physicians were 157 and 58, respectively. The first questionnaire was handed to the respondents based in Pakistan to reveal the reasons behind the quackery and its widespread existence. The second questionnaire was designed to examine physicians\u27 perceptions of quackery. The data were analyzed and we physicians having permit granted by the PMDC (Pakistan Medical and Dental Council) should be placed in doctor\u27s clinics to help people distinguish between quacks and physicians. According to the conducted survey government may be held accountable behind the spread of quackery whereas lack of awareness from the media and high illiteracy rates are also the lead causes. Moreover, according to doctor’s perspective, awareness campaigns will surely help us to overcome this menace. It is not simple for common people to differentiate a quack from a doctor. To assist the common person, we have created a prototype android app called "Quack Finder"\u27 based on our survey outcomes to predict whether an individual is a quack or a doctor.
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Detecting Aphid Concentration in Wheat Leaf Using Remote Sensing and GIS
Get PDFWheat lies among the most prominent cereal crop of Pakistan which has a significant role in the stability of Pakistan’s economy. Certain biotic and a biotic factors including agro-climatic conditions, rainfall, lack of irrigation infrastructure and conventional agricultural methods are raising the risk of aphid attacks. The current study utilized satellite imagery for obtaining thermal datasets of complete wheat growth. Results revealed that rainfall is a significant parameter for the determination of aphid growth on wheat plant. A region receiving 0-10 mm rainfall, supported the growth of aphid. Moreover, the aphid survival was highly supported at a moderate temperature ranging between 20-25 oC with relative humidity ranging from 70-75 %. The study also revealed the production of weed in wheat crop acted as a moistrizing agent which consequently provided favorable conditions to the aphid population for growth. Inapropriate usage of fertilizers increased the nitrogen content in soil which turned to be favourable for the aphid attack. Thus, the study concludes that agro-climatic conditions must be considered before the aplication of pesticides.
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Spatial Patterns of LRTI among Children in Lahore
Get PDFLower Respiratory Tract Infection (LRTI) is the leading global cause of morbidity and mortality in children of 1 month in developing countries. The aim of this research was to examine the spatial patterns of children under LRTI in Lahore, Pakistan. The records of all patients of LRTIs among children <5 years, admitted in the four different public sector hospitals of Lahore from 2017-2021 were analyzed. The collected data was processed and analyzed in SPSS 22.0 for the chi-square test (P<0.0.5), Multiple linear regression and ANOVA were calculated to assess the association of these variables. Town-wise distribution of diseases was mapped in ArcGIS 10.5. There were 2,609 pediatrics patients admitted and major cases in the year 2021. All the patients were distributed in four age groups, <2m, 2-12m, 13-24m, 25-60m. The most common diagnosis was Bronchopneumonia with (77.50%), Bronchiolitis (11.84%), Pneumonia (6.86%), and Bronchitis (3.79%). A significant increasing trend was found in Bronchopneumonia. In town-wise analysis, out of 2,609 patients, 977 patients were observed in Allama Iqbal Town. The peak season of the disease was seen in winter Dec-Feb. LRTI is a leading cause of childhood hospitalization in Lahore, Pakistan. These results may guide health authorities to determine where and when to effectively allocate resources for the prevention and control of LRTI.
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Multirate Adaptive Equalization
Get PDFFinite Impulse Response (FIR) filter model emulates the Inter Symbol Interference (ISI) in a wireless communication channel. An equalizer, typically an Infinite Impulse Response (IIR) filter, behaves as an inverse filter to the FIR filter to remove the effects of the ISI. IIR filters are generally avoided due to tractability issues, and an FIR filter, with an adaptive signal processing algorithm to minimize the error due to the ISI, is deployed at the receiver. However, the filter is observed to quickly reach a steady state where further iterations do not yield a reduction in the error. This can be attributed to relatively slow variations in the steady state error which prevent further reduction of the errors. This work focuses on converting the low frequency error variations to high frequency variations by the use of multirate signal processing. As such, the steady state error can be damped as well, providing further reduction in the error and an enhanced adaptive filter performance.
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Asphalt Pavement Potholes Localization and Segmentation using Deep Retina Net and Conditional Random Fields
Get PDFThe main aspect of maintaining the roads and highways\u27 durability and long life is to detect potholes and restore them. A huge number of accidents occur on the roads and highways due to the pothole. It also causes financial loss to vehicle owners by damaging the wheel and flat tire. For the strategies of the road management system and ITS (Intelligent Transportation System) service, it is one of the major tasks to quickly and precisely detect the potholes. To solve this problem, we have proposed a deep learning methodology to automatically detect and segment the pothole region within the asphalt pavement images. The detection of the pothole is a challenging task because of the arbitrary shape and complex structure of the pothole. In our proposed methodology, to accurately detect the pothole region, we used RetinaNet that creates the bounding box around the multiple regions. For the segmentation we used Conditional Random Field that segments the detected pothole regions obtained from RetinaNet. There are three steps in our methodology, image preprocessing, Pothole region localization, and Pothole segmentation. Our proposed methodology results show that potholes in the images were correctly localized with the best accuracy of 93.04%. Conditional Random Fields (CRF) also show good results.
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