Showing papers by "Muhammad Ramzan published in 2019"

A Survey on State-of-the-Art Drowsiness Detection Techniques

Abstract: Drowsiness or fatigue is a major cause of road accidents and has significant implications for road safety. Several deadly accidents can be prevented if the drowsy drivers are warned in time. A variety of drowsiness detection methods exist that monitor the drivers' drowsiness state while driving and alarm the drivers if they are not concentrating on driving. The relevant features can be extracted from facial expressions such as yawning, eye closure, and head movements for inferring the level of drowsiness. The biological condition of the drivers' body, as well as vehicle behavior, is analyzed for driver drowsiness detection. This paper presents a comprehensive analysis of the existing methods of driver drowsiness detection and presents a detailed analysis of widely used classification techniques in this regard. First, in this paper, we classify the existing techniques into three categories: behavioral, vehicular, and physiological parameters-based techniques. Second, top supervised learning techniques used for drowsiness detection are reviewed. Third, the pros and cons and comparative study of the diverse method are discussed. In addition, the research frameworks are elaborated in diagrams for better understanding. In the end, overall research findings based on the extensive survey are concluded which will help young researchers for finding potential future work in the relevant field.

A Review on State-of-the-Art Violence Detection Techniques

Abstract: With the rapid growth of surveillance cameras to monitor the human activity demands such system which recognize the violence and suspicious events automatically. Abnormal and violence action detection has become an active research area of computer vision and image processing to attract new researchers. The relevant literature presents different techniques for detection of such activities from the video proposed in the recent years. This paper reviews various state-of-the-art techniques of violence detection. In this paper, the methods of detection are divided into three categories that is based on classification techniques used: violence detection using traditional machine learning, using support vector machine (SVM), and using deep learning. The feature extraction techniques and object detection techniques of the each single method are also presented. Moreover, datasets and video features that used in the techniques, which play a vital role in recognition process are also discussed. For better understanding, the steps of the research approaches have been presented in an architecture diagram. The overall research findings have been discussed which may be helpful for finding the potential future work in this research domain.

A note on some perfect fluid Kantowski–Sachs and Bianchi type III spacetimes and their conformal vector fields in f(R) theory of gravity

Abstract: The purpose of this paper is to find conformal vector fields of some perfect fluid Kantowski–Sachs and Bianchi type III spacetimes in the f(R) theory of gravity using direct integration technique. ...

Classification of static cylindrically symmetric spacetimes in f(R) theory of gravity by conformal motions with perfect fluid matter

Abstract: Assuming the source of energy momentum tensor as perfect fluid, a classification of static cylindrically symmetric spacetimes in f(R) theory of gravity by conformal vector fields (CVFs) is presented. For the classification purpose, we put different conditions on metric coefficients to obtain solutions in f(R) theory of gravity. By means of some algebraic and direct integration techniques, it is shown that the dimension of CVFs for the considered spacetimes turns out to be 4, 5, or 15.

Classification of vacuum classes of plane fronted gravitational waves via proper conformal vector fields in f(R) gravity

Abstract: In this paper, we have studied proper conformal vector fields of pp-wave space-times in the f(R) theory of gravity using algebraic and direct integration techniques. From this study, we found that ...

A note on classification of spatially homogeneous rotating space-times in f(R) theory of gravity according to their proper conformal vector fields

Abstract: In this paper, we present a study of proper conformal vector fields of spatially homogeneous rotating space-times in the f(R) theory of gravity. A total of six cases have been discussed in detail. ...

A note on some perfect fluid Kantowski-Sachs and Bianchi type III space-times and their conformal vector fields in f(R) theory of gravity

Abstract: The purpose of this paper is to find conformal vector fields of some perfect fluid Kantowski-Sachs and Bianchi type III space-times in the f(R) theory of gravity using direct integration technique. In this study there exists only eight cases. Studying each case in detail, we found that in two cases proper conformal vector fields exist while in the rest of six cases conformal vector fields become Killing vector fields. The dimension of conformal vector fields is either 4 or 6.

A note on some Bianchi type II spacetimes and their conformal vector fields in f(R) theory of gravity

Abstract: The aim of this paper is to find proper conformal vector fields of some Bianchi type II spacetimes in the f(R) theory of gravity using direct integration technique. In this study, seven cases have ...

Energy Management in Smart Sectors Using Fog Based Environment and Meta-Heuristic Algorithms

Abstract: Smart Grid (SG) plays vital role in modern electricity grid. The data is increasing with the drastic increase in number of users. An efficient technology is required to handle this dramatic growth of data. Cloud computing is then used to store the data and to provide numerous services to the consumers. There are various cloud Data Centers (DC), which deal with the requests coming from consumers. However, there is a chance of delay due to the large geographical area between cloud and consumer. So, a concept of fog computing is presented to minimize the delay and to maximize the efficiency. However, the issue of load balancing is raising; as the number of consumers and services provided by fog grow. So, an enhanced mechanism is required to balance the load of fog. In this paper, a three-layered architecture comprising of cloud, fog and consumer layers is proposed. A meta-heuristic algorithm: Improved Particle Swarm Optimization with Levy Walk (IPSOLW) is proposed to balance the load of fog. Consumers send request to the fog servers, which then provide services. Further, cloud is deployed to save the records of all consumers and to provide the services to the consumers, if fog layer is failed. The proposed algorithm is then compared with existing algorithms: genetic algorithm, particle swarm optimization, binary PSO, cuckoo with levy walk and BAT. Further, service broker policies are used for efficient selection of DC. The service broker policies used in this paper are: closest data center, optimize response time, reconfigure dynamically with load and new advance service broker policy. Moreover, response time and processing time are minimized. The IPSOLW has outperformed to its counterpart algorithms with almost 4.89% better results.

An assessment of SMS fraud in Pakistan

Abstract: SMS fraud has become a growing concern for those working toward financial inclusion, however, it is often unclear how widespread such threats are in practice. This multi-method study investigates SMS fraud in Pakistan through identification and categorization of fraudulent messages as well as the impact on those who receive such messages. We collect fraudulent SMS messages by various means, including byway of a custom-built Android smartphone application. To complement this, we interview people exposed to SMS fraud and representatives of mobile network operators. Based on our analysis, lottery type fraud schemes dominate SMS fraud in Pakistan, and these schemes have the greatest impact on vulnerable low-income, rural populations. We offer a simple heuristic for fraud detection that has a high accuracy rate and is adaptable to evolving fraud schemes, and conclude with a recommendation for a fraud mitigation strategy to target fraudster call back numbers.

Electric Vehicles Privacy Preserving Using Blockchain in Smart Community

Abstract: During the process of charging, electric vehicle’s location is usually revealed when making payment. This brings about the potential risk to privacy of electric vehicle. We observe that the trade information recorded on blockchain may raise privacy concern and therefore, we propose a blockchain oriented approach to resolve the privacy issue without restricting trading activities through \((\epsilon , \delta )\)-differential privacy. The proposed scheme does not only preserve the electric vehicle’s location; however, prevents semantic, linking and data mining based attacks. Simulation results show that as the privacy level increases, the risk revealing decreases as well.

A note on proper homothetic vector fields in plane symmetric perfect fluid static spacetimes in f(R, T) theory of gravity

Abstract: The purpose of this paper is to find proper homothetic vector fields in plane symmetric perfect fluid static spacetimes in the f(R,T) theory of gravity by using direct integration technique. In thi...

Internet of medical things for smart D3S to enable road safety

Abstract: It has been seen that most of the accidents occur due to driver’s fatigue. Drowsiness is a state of mind before the driver falls asleep, which means the driver could not accomplish his actions, suc...

Frequency of common factors leading to primary postpartum haemorrhage in patients presenting at Nishtar Hospital Multan.

Abstract: Objectives: The objective was to determine the frequency of common factors leading to Primary postpartum hemorrhage. Study Design: Cross-sectional study. Setting: Study was conducted in obstetrics and Gynaecology ward of Nishtar Hospital Multan. Period: From 15-9-2017 to 14-03-2018. Material and Methods: Total 2119 deliveries were done in hospital during study period. Patients with loss of blood greater than 500 ml in vaginal delivery and 1000 ml in abdominal delivery were included. Main variables of study were age, paity, duration of delivery, mode of delivery. analysis was done by using SPSS for Window (version 10.0) Mean and stranded deviation was calculated for numerical data like ages of the patients and parity, while frequencies and percentages was calculated for qualitative data like mode of delivery. Results: 61.07% of the patients delivered vaginally and duration of labour was prolonged in 27.51% of cases. The single most frequent factor leading to primary postpartum haemorrhage was uterine atony i.e. in 48.99% of cases. Conclusion: Uterine atony was the most significant risk factors of primary postpartum haemorrhage. Our findings confirm the importance of previously recognized factors. Timely identification & management of these factors can help in reducing the incidence of maternal deaths.

Quantifying Effects of Photoperiod, Temperature and Humidity on Flowering Initiation in Basmati Rice Lines

Abstract: The results suggested that all the pure Basmati lines or genotypes initiate flowering within a specific range of day length duration and temperature. Delaying the transplanting date did not significantly affect the flowering time of studied Basmati lines. Maximum threshold of daily minimum temperature (MT) was 24°C during both the years. Likewise, maximum threshold of daily day-length (DL) durations was 12 hours and 18 minutes during both the study years. Results showed that the flowering was maximum when the MT and DL values were optimum as depicted by peak of the trend lines, where maximum lines flowered. Maximum flowering initiations took place when DL was about 11 hours and 15 minutes indicating Basmati as short day plant which flowers maximum when day lengths are shorter and dark or night duration is longer. During 2013, 1st date trial showed strong relationship between flowering initiation with dates of flowering during the flowering period as depicted by higher regression (R2 = 0.837) values. In 2nd date trial during the same year, the value is lower (R2 = 0.513) depicting less relationship of flowering initiation with the dates of flowering. The same trend was observed in 2014 trial. In 1st date trial, strong relationship between flowering initiation with dates of flowering during the flowering period as depicted by higher regression (R2 = 0.864) values, was observed. In 2nd date trial during the same year, the value is lower (R2 = 0.1789) depicting very poor relationship of flowering initiation with the dates of flowering, and the same trend (R2 = 0.0544) in 3rd date of trial.

Identifying Attractive Research Fields for New Scientists

Abstract: The selection of the research area is very vital for new researchers. One of the major issues for researchers is the selection of the domain of research on which he/she can carry out research. This case is very vital on the grounds that it decides the future of the researchers in that research area. Finding hot and attractive research areas is not considered in the relevant literature of Scientometrics. In this regard, the correct decision of the selection of the research domain helps the researchers to show better performance as well gain a good academic career. The main aim of this research study is to figure out the attractive research areas for the researchers, especially who are at the starting stage of their research life. To the best of our knowledge, this research area is still very limited due to limited work done in this area. So in order to distinguish the attractive research field for the new researchers, new rising fields are identified by applying the well-known g-index, which is widely used for finding the top authors in academic networks. In addition, we compute diverse, relevant features of the research fields which help us to identify top research area. The results demonstrate that the proposed methodology is capable to recommend the attractive research fields for potential future research work. An extensive empirical analysis has been carried out using the widely used academic database of DBLP.

Pesticides; acute poisoning, suicides and disability in humans

Abstract: Pakistan is a place where only pesticides is considered as an effective source for control of pests and diseases, and we are using these deliberately by involving wide range of pesticides. Majority of chemicals are used in agriculture as data suggests that about 49399 million metric tons of pesticides had been used only in agriculture sector in 2010 in the country. Use of pesticides in other fields such as public health, fisheries, forestry and food industry is not included in the data. Though pesticides are useful in managing pest populations but these have adverse effects on humans, animals and ecosystems. The current work exhibits the effects of pesticides on human health in the country. Acute poisoning is one of the key issues resulted by unsafe handling and storage of pesticides in on-farm and off-farm premises in developing economies. From various areas in the country, pesticides are reported to be used as tool for suicide by teen agers due to easy access. According to department of Plant Protection (Karachi), total of 200 workers get affected showing low enzyme activity in their blood. In Multan, an adverse situation was observed as elevated levels (87.5 %) of acetylcholinesteras (AchE) activity was recorded in female cotton pickers as compared to males which have low (36 %) AchE activity because cotton is picked mostly by women in Pakistan. The aim of this work is to sum-up the weaknesses in practicing / implementing current policies and legislations for safe handling, storage and judicious use of pesticides. The acquired knowledge will later be used in making and improving the existing regulations for the subject