Causes of anemia in pregnant women of the state of azad kashmir: A cross-sectional survey

Background: Epidemic of anemia is considered to be a significant threat to pregnant women or women in child bearing age. Anemia is one of the major nutritional health disorders affecting significant proportion of population not only in developing countries but also in developed countries. This threat is more alarming in developing countries where poverty, illiteracy may contribute to high risk for causes of anemia. Objective: The purpose of the current study was to investigate the main causes of anemia in pregnant women in the State of Azad Kashmir, Muzaffarabad and to investigate the relationship between education and anemia. Methods: A descriptive cross sectional study was conducted over a sample of 433 pregnant women. The Chi- square test has been used to assess the statistical significance of different risk factors with Hb% (Heamoglobin) of the respondent. The multiple logistic regression model was used to get the most significant risk factors of anemia. Results: The study shows that the most dominant risk factors of the anemia were age at the time of marriage at different age categories that are 16 - 20 (OR = 3.945) (OR Odds ratios) with 95% C-I (confidence interval) (0.294 to 52.985), 21 - 25 (OR = 2.316) with 95% C-I (0.192 to 27.932) and 26 - 30 (OR = 4.179) with 95% C-I (0.347 to 50.320). Education at different education levels that is illiterate (OR = 1.191) with 95% C-I (0.005 to 87.279) and primary (OR = 1.179) with 95% C-I (0.009 to 156.200). Hb% at different levels 3 - 4 g/dl (OR = 1.220) with 95% C-I (0.299 to 4.984), 5 - 6 g/dl (OR = 2.221) with 95% C-I (0.679 to7.263) and 7 - 10 g/dl (OR = 1.384) with 95% C-I (0.408 to 4.689). Monthly

Study of Similarity Measures with Linear Discriminant Analysis for Face Recognition

Face recognition systems have been in the active research in the area of image processing for quite a long time. Evaluating the face recognition system was carried out with various types of algorithms used for extracting the features, their classification and matching. Similarity measure or distance measure is also an important factor in assessing the quality of a face recognition system. There are various distance measures in literature which are widely used in this area. In this work, a new class of similarity measure based on the Lp metric between fuzzy sets is proposed which gives better results when compared to the existing distance measures in the area with Linear Discriminant Analysis (LDA). The result points to a positive direction that with the existing feature extraction methods itself the results can be improved if the similarity measure in the matching part is efficient.

Summary study of data-driven photometric stereo methods

Background A photometric stereo method aims to recover the surface normal of a 3D object observed under varying light directions.It is an ill-defined problem because the general reflectance properties of the surface are unknown.Methods This paper reviews existing data-driven methods,with a focus on their technical insights into the photometric stereo problem.We divide these methods into two categories,per-pixel and all-pixel,according to how they process an image.We discuss the differences and relationships between these methods from the perspective of inputs,networks,and data,which are key factors in designing a deep learning approach.Results We demonstrate the performance of the models using a popular benchmark dataset.Conclusions Data-driven photometric stereo methods have shown that they possess a superior performance advantage over traditional methods.However,these methods suffer from various limitations,such as limited generalization capability.Finally,this study suggests directions for future research.

Deep Learning Method to Detect the Road Cracks and Potholes for Smart Cities

The increasing global population at a rapid pace makes road trafficdense;managing such massive traffic is challenging. In developing countrieslike Pakistan, road traffic accidents (RTA) have the highest mortality percentageamong other Asian countries. The main reasons for RTAs are roadcracks and potholes. Understanding the need for an automated system forthe detection of cracks and potholes, this study proposes a decision supportsystem (DSS) for an autonomous road information system for smart citydevelopment with the use of deep learning. The proposed DSS works in layerswhere initially the image of roads is captured and coordinates attached to theimage with the help of global positioning system (GPS), communicated tothe decision layer to find about the cracks and potholes in the roads, andeventually, that information is passed to the road management informationsystem, which gives information to drivers and the maintenance department.For the decision layer, we projected a CNN-based model for pothole crackdetection (PCD). Aimed at training, a K-fold cross-validation strategy wasused where the value of K was set to 10. The training of PCD was completedwith a self-collected dataset consisting of 6000 images from Pakistani roads.The proposed PCD achieved 98% of precision, 97% recall, and accuracy whiletesting on unseen images. The results produced by our model are higher thanthe existing model in terms of performance and computational cost, whichproves its significance.

Real-Time Multiple Guava Leaf Disease Detection from a Single Leaf Using Hybrid Deep Learning Technique

The guava plant has achieved viable significance in subtropics and tropics owing to its flexibility to climatic environments,soil conditions and higher human consumption.It is cultivated in vast areas of Asian and Non-Asian countries,including Pakistan.The guava plant is vulnerable to diseases,specifically the leaves and fruit,which result in massive crop and profitability losses.The existing plant leaf disease detection techniques can detect only one disease from a leaf.However,a single leaf may contain symptoms of multiple diseases.This study has proposed a hybrid deep learning-based framework for the real-time detection of multiple diseases from a single guava leaf in several steps.Firstly,Guava Infected Patches Modified MobileNetV2 and U-Net(GIP-MU-NET)has been proposed to segment the infected guava patches.The proposed model consists of modified MobileNetv2 as an encoder,and the U-Net model’s up-sampling layers are used as a decoder part.Secondly,the Guava Leaf SegmentationModel(GLSM)is proposed to segment the healthy and infected leaves.In the final step,the Guava Multiple Leaf Diseases Detection(GMLDD)model based on the YOLOv5 model detects various diseases from a guava leaf.Two self-collected datasets(the Guava Patches Dataset and the Guava Leaf Diseases Dataset)are used for training and validation.The proposed method detected the various defects,including five distinct classes,i.e.,anthracnose,insect attack,nutrition deficiency,wilt,and healthy.On average,the GIP-MU-Net model achieved 92.41%accuracy,the GLSM gained 83.40%accuracy,whereas the proposed GMLDD technique achieved 73.3%precision,73.1%recall,71.0%mAP@0.5 and 50.3 mAP@0.5:0.95 scores for all the aforesaid classes.

A Hybrid Deep Learning Approach to Classify the Plant Leaf Species

Many plant species have a startling degree of morphological similarity,making it difficult to split and categorize them reliably.Unknown plant species can be challenging to classify and segment using deep learning.While using deep learning architectures has helped improve classification accuracy,the resulting models often need to be more flexible and require a large dataset to train.For the sake of taxonomy,this research proposes a hybrid method for categorizing guava,potato,and java plumleaves.Two new approaches are used to formthe hybridmodel suggested here.The guava,potato,and java plum plant species have been successfully segmented using the first model built on the MobileNetV2-UNET architecture.As a second model,we use a Plant Species Detection Stacking Ensemble Deep Learning Model(PSD-SE-DLM)to identify potatoes,java plums,and guava.The proposed models were trained using data collected in Punjab,Pakistan,consisting of images of healthy and sick leaves from guava,java plum,and potatoes.These datasets are known as PLSD and PLSSD.Accuracy levels of 99.84%and 96.38%were achieved for the suggested PSD-SE-DLM and MobileNetV2-UNET models,respectively.

An Efficient Scheme for Multiparty Multi-particle State Sharing

We present an efficient scheme for sharing an arbitrary m-qubit state with n agents.In our scheme,the sender Alice first shares m Bell states with the agent Bob,who is designated to recover the original m-qubit state.Furthermore,Alice introduces n-1 auxiliary particles in the initial state |0>,applies Hadamard (H) gate and Controlled-Not(CNOT) gate operations on the particles,which make them entangled with one of m particle pairs in Bell states,and then sends them to the controllers (i.e.,other n-1 agents),where each controller only holds one particle in hand.After Alice performing m Bell-basis measurements and each controller a single-particle measurement,the recover Bobcan obtain the original unknown quantum state by applying the corresponding local unitary operations on his particles.Its intrinsic efficiency for qubits approaches 100%,and the total efficiency really approaches the maximal value.

Non-ambiguity of blind watermarking:a revisit with analytical resolution

Resistance to ambiguity attack is an important requirement for a secure digital rights management （DRM） system. In this paper, we revisit the non-ambiguity of a blind watermarking based on the computational indistinguishability between pseudo random sequence generator （PRSG） sequence ensemble and truly random sequence ensemble. Ambiguity attacker on a watermarking scheme, which uses a PRSG sequence as watermark, is viewed as an attacker who tries to attack a noisy PRSG sequence. We propose and prove the security theorem for binary noisy PRSG sequence and security theorem for general noisy PRSG sequence. It is shown that with the proper choice of the detection threshold Th = α n （a is a normalized detection threshold; n is the length of a PRSG sequence） and n i≥ 1.39 × m/α^2 （m is the key length）, the success probability of an ambiguity attack and the missed detection probability can both be made negligibly small thus non-ambiguity and robustness can be achieved simultaneously for both practical quantization-based and blind spread spectrum （SS） watermarking schemes. These analytical resolutions may be used in designing practical non-invertible watermarking schemes and measuring the non-ambiguity of the schemes.