Machine Learning with Abstention for Automated Liver Disease Diagnosis

This paper presents a novel approach for detection of liver abnormalities in an automated manner using ultrasound images. For this purpose, we have implemented a machine learning model that can not only generate labels (normal and abnormal) for a given ultrasound image but it can also detect when its prediction is likely to be incorrect. The proposed model abstains from generating the label of a test example if it is not confident about its prediction. Such behavior is commonly practiced by medical doctors who, when given insufficient information or a difficult case, can chose to carry out further clinical or diagnostic tests before generating a diagnosis. However, existing machine learning models are designed in a way to always generate a label for a given example even when the confidence of their prediction is low. We have proposed a novel stochastic gradient based solver for the learning with abstention paradigm and use it to make a practical, state of the art method for liver disease classification. The proposed method has been benchmarked on a data set of approximately 100 patients from MINAR, Multan, Pakistan and our results show that the proposed scheme offers state of the art classification performance.Comment: Preprint version before submission for publication. complete version published in proc. 15th International Conference on Frontiers of Information Technology (FIT 2017), December 18-20, 2017, Islamabad, Pakistan. http://ieeexplore.ieee.org/document/8261064

A New Weighting Scheme in Weighted Markov Model for Predicting the Probability of Drought Episodes

Drought is a complex stochastic natural hazard caused by prolonged shortage of rainfall. Several environmental factors are involved in determining drought classes at the specific monitoring station. Therefore, efficient sequence processing techniques are required to explore and predict the periodic information about the various episodes of drought classes. In this study, we proposed a new weighting scheme to predict the probability of various drought classes under Weighted Markov Chain (WMC) model. We provide a standardized scheme of weights for ordinal sequences of drought classifications by normalizing squared weighted Cohen Kappa. Illustrations of the proposed scheme are given by including temporal ordinal data on drought classes determined by the standardized precipitation temperature index (SPTI). Experimental results show that the proposed weighting scheme for WMC model is sufficiently flexible to address actual changes in drought classifications by restructuring the transient behavior of a Markov chain. In summary, this paper proposes a new weighting scheme to improve the accuracy of the WMC, specifically in the field of hydrology

Prospects of microalgal biodiesel production in Pakistan – a review

Biodiesel is an alternative, renewable, biodegradable and environmentally friendly fuel for transportation, with properties like petroleum-derived diesel, and can be used directly in a compression ignition engine without any modifications. The world's fossil fuel and crude oil reserves are going to dry up in the next few decades, but, contrariwise, an attractive, high quality, readily available and economically extractable oil from microalgae is a substitute feedstock to produce alternative biodiesel fuel for the transportation sector in the future. Microalgae have a higher biomass productivity (tons/hectare/year) and lipid yield (kg/kg of algal biomass) as compared to vegetable oil crops. To overcome the problem of energy deficiency in developing countries, like Pakistan, and boost their economic growth, alternative fuels are proving very important for environment-friendly and sustainable development, especially in the last few decades. Different research studies on microalgae cultivation, characterization of microalgae oil (lipids), and evaluations of its socio-economic feasibility to produce renewable biodiesel have been conducted in the past in Pakistan for its future prospects. This review paper includes the overall summary and compilation of the microalgae research conducted in Pakistan on biodiesel production and includes the algal biodiesel production cost analysis. The studies showed promising results for harnessing microalgae and using its lipids to produce biodiesel with favourable properties that were comparable to the conventional diesel in Pakistan. The information related to the microalgae research will help stakeholders and governmental organisations working in the renewable energy sector to consider its cultivation on a large scale, using waste water as a feedstock to produce biodiesel to meet the target set by the Government of Pakistan of using 10% blended biodiesel by the year 2025 in Pakistan

Irrigation Water Quality Assessment and Salinity Management Strategies in Bahawalpur Division, Pakistan

Economy of Pakistan is agricultural based and it mainly dependent on canal supplies. Due to rapid population growth, there has been a dramatic increase in the intensity of ground water exploitation leading to decline water table and deteriorate ground water quality. Tube well water is one of the most common resources to support the irrigation in situation of canal water scarcity. Considering the importance of tube well water, present study was conducted for the quality assessment of tube well water to provide guidelines to farmer and researches for better crop production by adopting water management strategies. Total 1400 water samples were collected from Bahawalpur division during the year 2017. These samples were analyzed and categorized according to suitability criteria of water quality evaluation. 38.64 percent water samples were found fit, 7.65 percent were marginally fit and 53.7 percent water samples were found unfit for irrigation purpose. Majority of water samples were found hazardous for irrigation purpose. There is need to analyze the existing water resources and recommending comprehensive conservation and management strategies in view of catering the planning requirements for the future. Keywords: Salinity, Sodicity, Ground water, Bahawalpur, Pakistan

Viable forecasting monthly weather data using time series methods

The main object of the research was to assess the forecast values of the weather parameters by using three-time series methods such as Decomposition of time series, Autoregressive (AR) model with seasonal dummies and Autoregressive moving average (ARMA) /Autoregressive Integrated moving average (ARIMA) model. A recent phenomenon in weather changing has disturbed the world in general and Pakistan in particular. In Pakistan due to climate change, flood and heat stroke have taken many lives. Stationarity was measured through the Augmented Dickey-Fuller test; results showed that some variables are I(0) and some are I(1). The reliability of the forecast results was examined through the goodness of fit test. For finding the best fit model, the performance measures of various models: Root Mean Squire Error, Mean Absolute Error and Mean Absolute Percentage Error were considered. The model in which the above statistics are the minimum was chosen as the appropriate model. After model analysis and validation, it was observed that AR-model with seasonal dummies was found to be the best fit model between the three models. Meanwhile, the forecasting for the period Jan.2018 to Dec.2018 was made based on the best fit model. Given the future forecasting results, the temperature will be normal at selected stations. The wind and rainfall will also be present. Overall, it was suggested that the obtained findings of meteorological stations' weather might be normal for the coming few months over there, and no chance of heatstroke and flood might be expected. Future studies must be carried out to provide the awareness to well-being regarding ecological hazardous to minimize their economic loss through mass media

Feeling Safe in Urban Estates: Learning from Riverwood, Sydney

Feeling safe is a necessity for quality of life. Conversely, feeling unsafe has a substantial impact on residents’ quality of life. How does design impact on the perception of safety, and moreover, how can design reduce incidences of crime? Safety is influenced by many social, economic, and wellbeing factors that affect residents’ experiences of their built environments. Neighbourhood and urban design – which are liable to be affected by the perceived quality of local spaces – are likely to be significant factors influencing broader residents’ feelings of safety. To these ends, this paper reviews recent literature on how design processes have influenced perceived and actual safety in public spaces. This paper focuses on different aspects of urban safety, including planning, management, and design in a mix-tenure neighbourhood. The paper selected Riverwood, a social housing renewal neighbourhood located in southwest Sydney, as the study area. Data collection methods used by the author for this paper include direct-observation and a cross-sectional survey of 62 households, aimed at shedding light on what are residents' preferences to improve safety perception in public spaces. The paper finds that, for greater safety of neighbourhoods in urban estates, design approaches need toconsider both physical and social-cultural factors; and that to achieve this, practical and realistic mechanisms are required to improve existing estates and to design future estates better. The findings of the study reveal that, addressing the concerns revolving around the trust-deficit in the community, will be the cornerstone to promote residents feeling of safety

Applying the Land Administration Domain Model (LADM) for Integrated, Standardized, and Sustainable Development of Cadastre Country Profile for Pakistan

Rapid urban growth necessitates focused attention regarding its policy and governance to ensure affordable housing, transparent and efficient real-world systems, reduce social inequalities, and promote sustainable development. This study delves into the semantics and ontology for developing a Land Administration Domain Model (LADM) profile in the context of Pakistan’s Land Administration Systems (LASs), which currently face issues due to manual record-keeping, lack of transparency, frauds, and disintegration. Establishing a baseline through Record of Rights (RoR) and Property Information Report (PIR), alongside surveying and mapping procedures defined by laws and rules, forms the foundation for LADM profile development. This study explores the transition from manual LAS to 2D/3D representation, using LADM as a conceptual guideline. The LADM profile’s three key packages—PK_Party, PK_Administrative, and PK_SpatialUnit—a sub-package, and external classes are examined, with proposals for digitalisation and modernisation. Additionally, the study includes expert consultation, and highlights the significant support that the LADM implementation offers to achieve Sustainable Development Goals (SDGs) in Pakistan. In conclusion, the study underscores the need for a comprehensive and inclusive approach to address organisational overlaps and ambiguities within LAS, positioning PK LADM as a transformative force for sustainable urban LAS in Pakistan, aligning with broader SDGs. Recommendations include exploring realistic land valuation, integrated ownership and location verification systems, addressing historical survey data challenges, and promoting wider stakeholder adoption for sustainable 2D/3D urban LAS using LADM and its edition II as a way forward towards the creation of a smart city and digital twin