The seeds of twenty-five varieties/cultivars of mungbean were evaluated in respect of proximate assays, chemical composition, and nutritional values with the aim to ensure the suitability of the foodstuff for human consumption. The average fiber content of the tested varieties/cultivars was 4.85±0.04 % while potassium (K) ranges from 250 to 938 and phosphorus (P) from 371 to 464 mg 100g−1. The tested cultivars contained micronutrients including Zn, Fe and Cu with average value of 30.70, 104.00 and 3.84 mg kg−1, respectively. Alpha (α-), Gamma (γ-) and Sigma (δ-) tocopherols were also present in mungbean seeds with average value of 25, 1962 and 670 mg kg−1. Among toxic heavy metals, average Cd, Cr and Pb were present at 0.22, 3.23 and 1.64 mg kg−1, respectively, indicating some values above the maximum permissible limit set under various rules and regulations. The content of phytic acid varied from 0.45% to 1.22% in these varieties and it was observed that the phytates were existed in negative correlation with Zn content. Aflatoxins AFB1 and AFB2 were 48% and 16% above the maximum permissible limits, respectively, while AFG1 and AFG2 were found to be below the permissible limits in all the tested varieties/cultivars. The results of the study showed the essential minerals and protein, nutritional and anti-nutritional characters of mungbean varieties/cultivars, which are potential health determinants and can be used to characterize food quality in human healthcare. 

Nutritional evaluation, proximate and chemical composition of mungbean varieties/cultivars pertaining to food quality characterization

Urdbean or blackgram is one of the important multi‐season pulse crops grown in India. Improved seed quality and resistance to Mungbean yellow mosaic India virus (MYMIV) are the important objectives of the urdbean breeding program. For this, knowledge of inheritance for seed quality traits and MYMIV resistance is essentially required. Therefore, an attempt has been made to understand the inheritance of seed traits like seed luster, seed coat color, mosaic on seed surface, and MYMIV reaction using intra‐ and inter‐specific bi‐parental populations. The inheritance results revealed that in DPU88‐31 × LBG‐685 population, the shining seed registered dominance over the dull trait, while the green seed coat color exhibited dominance over the brown seed coat color. However, the inter‐specific F2 population derived from IPU11‐02 × Pant M 6 deviated significantly from Mendelian ratios and supported severe segregation distortion for seed traits, while the mosaic on seed coat or mottling character studied in interspecific population demonstrated a dominant digenic inheritance pattern. In five inter‐ and intra‐specific F2 populations, MYMIV resistance demonstrated monogenic dominant inheritance, which was also validated in F3 generations. Furthermore, linkage analysis exhibited an association between seed luster and seed coat color, while no association was noticed between seed traits and MYMIV resistance. The normality tests revealed that seed width and 100 seeds weight were controlled by a few major genes, while other quantitative traits were governed by many genes with small additive effects. The skewness suggested that complementary gene interactions were present among genes controlling 100 seed weight in the intra‐specific cross, while duplicate gene interactions were involved in the inter‐specific cross. The identified monogenic traits and linked morphological marker after further enrichment of the linkage group could be used as an important tool in the regular breeding program.

Genetics for seed traits and Mungbean yellow mosaic India virus reaction in urdbean (Vigna mungo L. Hepper)

In Nigeria, there are many different security concerns and thus crimes have increased despite the fact that there are stringent laws and punishments in place to deter them, making it appear as though the authorities are unable to stop it. In order to identify criminals and conduct investigations, it is imperative that a facial recognition system be connected to a constantly updated digital library. The focus of this paper is to develop an automatic criminal investigation system that can identify criminals based on their faces and produce real-time digital archives about them. However, as an object detection method and facial recognition model, the new system is built on the Haar Cascades Classifier technique in the OpenCV package. Additionally, appropriate programming languages that may provide the needed results were investigated. Python 3.6 was used with the Django 4.2 framework, OpenCV-Python, and Dlib for language execution. Due to Django's ORM, support for numerous databases, and usage of the SQLite3 database, a straightforward database was employed for lightweight applications. The 12 factor app idea was used to construct the DICA-FR system's essential skills. Face detection was applied to the image using the Haar method during processing, and during post-processing, the discovered face was compared with well-known criminal face encodings for matching purposes. Results demonstrated that DICA-FRS could effectively replace human systems since it can recover faces from the furthest distances, display the name of the offender, and sound an alert on the DICA web app's output screen. The DICA system is a working prototype of a system that might be used in the criminal investigative process in Nigeria.

Digital Criminal Biometric Archives (DICA) and Public Facial Recognition System (FRS) for Nigerian criminal investigation using HAAR cascades classifier technique

Abstract The vehicular ad-hoc network is a technology that enables vehicles to interact with each other and the surrounding infrastructure, aiming to enhance road safety and driver comfort. However, it is susceptible to various security attacks. Among these attacks, the position falsification attack is regarded as one of the most serious, in which the malicious nodes tamper with their transmitted location. Thus, developing effective misbehavior detection schemes capable of detecting such attacks is crucial. Many of these schemes employ machine learning techniques to detect misbehavior based on the features of the exchanged messages. However, the studies that identify the impact of feature engineering on schemes’ performance and highlight the most efficient features and algorithms are limited. This paper conducts a comprehensive literature survey to identify the key features and algorithms used in the literature that lead to the best-performing models. Then, a comparative study using the VeReMi dataset, which is publicly available, is performed to assess six models implemented using three different machine learning algorithms and two feature sets: one comprising selected and derived features and the other including all message features. The findings show that two of the suggested models that employ feature engineering perform almost equally to existing studies in identifying two types of position falsification attacks while exhibiting performance improvements in detecting other types. Furthermore, the results of evaluating the proposed models using another simulation exhibit a substantial improvement achieved by employing feature engineering techniques, where the average accuracy of the models is increased by 6.31–47%, depending on the algorithm used. 

Feature engineering impact on position falsification attacks detection in vehicular ad-hoc network

Abstract Carbohydrates are significant components of legumes, and their profiling can provide information about their nutritional value and potential health benefits. This study explores the proximate composition and carbohydrate profiles of accessions of three underutilized legumes, namely, Vigna radiata , Vigna mungo , and Macrotyloma uniflorum . The total starch and soluble sugar are determined. Starch granule morphology and diameter are determined using scanning electron microscopy and chain length distribution of amylopectin is analyzed using Capillary Electrophoresis. All accessions of the legumes vary both in their proximate composition and carbohydrate components. Accessions of M. uniflorum (PI‐658594‐01‐SD) have the highest carbohydrate component (7.6%) while V. radiata (TVR‐42) has the least (4.2%). Macrotyloma uniflorum accensions (PI‐658594‐01‐SD, and PI‐180437‐01‐SD) have the highest starch and sucrose contents, while V. mungo accensions (TVM‐13, and TVM‐11) have the lowest. The glucose and fructose contents are lower than sucrose in all the accessions of the legumes. Macrotyloma uniflorum accensions exhibit larger granules while accessions of Vigna species have the smallest granules. All species accessions exhibit similar amylopectin chain length distribution profiles although accessions of V. mungo slightly differ in their proportion of long and short glucan chains. The three species of underutilized legumes exhibit unique characteristics which make them suitable for consumption and may be exploited as a source of nutraceuticals and pharmaceuticals. 

Carbohydrate Profiling of Different Accessions of Three Underutilized Legumes: A Potential Source of Valuable Nutraceuticals and Pharmaceuticals

Feature selection has become essential in classification problems with numerous features. This process involves removing redundant, noisy, and negatively impacting features from the dataset to enhance the classifier’s performance. Some features are less useful than others or do not correlate with the system’s evaluation, and their removal does not affect the system’s performance. In most cases, removing features with a monotonically decreasing impact on the system’s performance increases accuracy. Therefore, this research aims to propose a dimensionality reduction method using a feature selection technique to enhance accuracy. This paper proposes a novel feature-selection approach that combines filter and wrapper techniques to select optimal features using Mutual Information with the Sequential Forward Method and 10-fold cross-validation. Results show that the proposed algorithm can reduce features by more than 75% in datasets with large features and achieve a maximum accuracy of 97%. The algorithm outperforms or performs similarly to existing ones. The proposed algorithm could be a better option for classification problems with minimized features.

/pdf/efficient-multiclass-classification-using-feature-selection-1tl16qpq.pdf

Efficient Multiclass Classification Using Feature Selection in High-Dimensional Datasets

Effect of growing environments on the minerals and proximate composition of urdbeans (Vigna mungo L. Hepper)

Face detection is the process of identifying a face in an image with the help of facial characteristics. Detection of the face with other facial characteristics such as eyes, nose, and smile can help in solving many real-world problems such as Product rating, patient monitoring in hospitals as well as image Capturing, conference videos, etc. The application of real-time smile detection could also be integrated with facial recognition to make it more viable and effective for use. To detect a face and its components the system uses Haar features. Haar features have a resemblance to the convolution kernel, which is used to detect the presence of a particular feature in an image. The Difference Between the values present under the Dark region and the pixels present under the White region results in a single Value. The value is then compared to the threshold, If the value is Greater than the threshold then a human face is detected. Here, The integral image also plays a very important role in detecting a face and its features, it is used to calculate the sum of all pixels inside a rectangle by just using the corner values of a rectangle. Adaboost is also used to remove irrelevant features from a given set of features, Leaving behind only the relevant set of features. At last cascading is used to group the strong classifiers into stages. So, Each stage consists of a set of features that are very crucial in detecting whether a given image is a face or not. If Not, the image is discarded immediately. The Proposed Model detects a face by Capturing the Frames in real-time from a Webcam Feed, Converts the Captured Frames into Grayscale as it Requires Less Processing to be Done. The Haar Cascade Xml Files are used which Consist of a Necessary set of Haar Features required for face detection in a frame. There are Separate Xml Files that Consist of a Necessary set of features that Could be used for the detection of Other Body Parts. To Detect Facial Components such as Eyes and Nose or even a Smile, Haar Cascade Xml Files are used. A complete Facial Detection System with Real-Time Smile Detection Has Numerous Applications in Medical Sectors such as Emotion Detection, Sentiment Analysis, Etc., It can be Even used in the Improvement of a Business Model Enhancing the Feedback System which in turn Improves the Overall Customer Experience, Leading to Growth in Business.

Real-Time Facial Components Detection Using Haar Classifiers

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Efficient Multiclass Classification Using Feature Selection in High-Dimensional Datasets

Effect of growing environments on the minerals and proximate composition of urdbeans (Vigna mungo L. Hepper)

Real-Time Facial Components Detection Using Haar Classifiers