A Study On The Factors Influencing Employees Intention To Use E- Licensing

Teknologi telah mengubah cara penyampaian perkhidmatan kerajaan kepada orang ramai dan perniagaan. Technology has changed the delivery of government services to the public and businesses

Smart Farm-Care using a Deep Learning Model on Mobile Phones

Deep learning and its models have provided exciting solutions in various image processing applications like image segmentation, classification, labeling, etc., which paved the way to apply these models in agriculture to identify diseases in agricultural plants. The most visible symptoms of the disease initially appear on the leaves. To identify diseases found in leaf images, an accurate classification system with less size and complexity is developed using smartphones. A labeled dataset consisting of 3171 apple leaf images belonging to 4 different classes of diseases, including the healthy ones, is used for classification. In this work, four variants of MobileNet models - pre-trained on the ImageNet database, are retrained to diagnose diseases. The model’s variants differ based on their depth and resolution multiplier. The results show that the proposed model with 0.5 depth and 224 resolution performs well - achieving an accuracy of 99.6%. Later, the K-means algorithm is used to extract additional features, which helps improve the accuracy to 99.7% and also measures the number of pixels forming diseased spots, which helps in severity prediction. Doi: 10.28991/ESJ-2023-07-02-013 Full Text: PD

The Eye: A Light Weight Mobile Application for Visually Challenged People Using Improved YOLOv5l Algorithm

The eye is an essential sensory organ that allows us to perceive our surroundings at a glance. Losing this sense can result in numerous challenges in daily life. However, society is designed for the majority, which can create even more difficulties for visually impaired individuals. Therefore, empowering them and promoting self-reliance are crucial. To address this need, we propose a new Android application called “The Eye” that utilizes Machine Learning (ML)-based object detection techniques to recognize objects in real-time using a smartphone camera or a camera attached to a stick. The article proposed an improved YOLOv5l algorithm to improve object detection in visual applications. YOLOv5l has a larger model size and captures more complex features and details, leading to enhanced object detection accuracy compared to smaller variants like YOLOv5s and YOLOv5m. The primary enhancement in the improved YOLOv5l algorithm is integrating L1 and L2 regularization techniques. These techniques prevent overfitting and improve generalization by adding a regularization term to the loss function during training. Our approach combines image processing and text-to-speech conversion modules to produce reliable results. The Android text-to-speech module is then used to convert the object recognition results into an audio output. According to the experimental results, the improved YOLOv5l has higher detection accuracy than the original YOLOv5 and can detect small, multiple, and overlapped targets with higher accuracy. This study contributes to the advancement of technology to help visually impaired individuals become more self-sufficient and confident. Doi: 10.28991/ESJ-2023-07-05-011 Full Text: PD

Ovicidal Activity of Couroupita guianensis (Aubl.) against Spodoptera litura (Fab.)

Hexane, chloroform, and ethyl acetate extracts of Couroupita guianensis leaves were studied for ovicidal activity against S. litura. All the extracts showed ovicidal activity against S. litura. Maximum activity was noticed in hexane extract and it showed the least LC50 and LC90 values; the regression equation was also higher than the other extracts. All the analyzed values showed homogeneity variance. The active hexane extract was fractionated and eight fractions were isolated. The fractions were studied at different concentrations. Among the fractions, fraction 8 showed maximum ovicidal activity with least LC50 and LC90 values. Fraction 8 differed statistically from the other fractions; the regression equation value was higher than the other fractions. All the P values obtained from regression analysis were significant. The results of the present investigation clearly suggest that the active fraction could be purified to isolate active compound(s) and could be used to develop an insecticidal formulation to control economically important agricultural pests

Evaluation of fractions and 5,7-dihydroxy-4',6-dimethoxy-flavone fromClerodendrum phlomidis Linn. F. against Helicoverpa armigera Hub.

Twelve fractions from chloroform extract of Clerodendrum phlomidis and 5,7-dihydroxy- 4',6-dimethoxy-flavone (pectolinaringenin) were evaluated against Helicoverpa armigera. Maximum antifeedant (89.41%), larvicidal (83.77%) and ovicidal (69.25%) activities were observed in fraction 5. The least LC50 value for antifeedant (178.09 ppm) and larvicidal (198.23 ppm) were observed in fraction 5. No adult emergence was recorded in fractions 4-6 at 1000 ppm. The oviposition deterrent activity was 100% in fraction 5 at all the concentrations. Pectolinaringenin recorded maximum antifeedant (74.68%) and larvicidal (81.11%) activities at 100 ppm; it completely prevented the adult emergence of H. armigera at 100 ppm. Maximum ovicidal activity at 100 ppm concentration was 67.95%. The oviposition deterrent activity was 100% in 100 and 50 ppm concentrations. C. phlomidis could be effectively used to develop a new formulation to control the economically important pests

PDHS: Pattern-Based Deep Hate Speech Detection With Improved Tweet Representation

Automatic hate speech identification in unstructured Twitter is significantly more difficult to analyze, posing a significant challenge. Existing models heavily depend on feature engineering, which increases the time complexity of detecting hate speech. This work aims to classify and detect hate speech using a linguistic pattern-based approach as pre-trained transformer language models. As a result, a novel Pattern-based Deep Hate Speech (PDHS) detection model was proposed to detect the presence of hate speech using a cross-attention encoder with a dual-level attention mechanism. Instead of concatenating the features, our model computes dot product attention for better representation by reducing the irrelevant features. The first level of Attention is extracting aspect terms using predefined parts-of-speech tagging. The second level of Attention is extracting the sentiment polarity to form a pattern. Our proposed model trains the extracted patterns with term frequency, parts-of-speech tag, and Sentiment Scores. The experimental results on Twitter Dataset can learn effective features to enhance the performance with minimum training time and attained 88%F1Score

Bioefficacy of pectolinaringenin from Clerodendrum phlomidis Linn. F. against Anopheles stephensi and bhendi fruit borer, Earias vittella fab.

Larvicidal activity of pectolinaringenin from Clerodendrum phlomidiswas evaluated against Anopheles stephensi and antifeedant, larvicidal and growth inhibitory activities were evaluated against Earias vittella. Pectolinaringenin exhibited larvicidal activity of 100 and 98.24% against 2nd and 4th instar larvae of Anopheles stephensi at 5ppm concentration. It exhibited LC50 values of 0.35 and 0.55 ppm for 2nd and 4th instar larvae, respectively. At 100 ppm concentration, pectolinaringenin exhibited maximum antifeedant activity of 74.00% and larvicidal activity of 89.98%. The LC50 values were 36.2 and 10.23 ppm for antifeedant and larvicidal, respectively. The compound completely prevented the adult emergence at 50 and 100 ppm concentrations. This is the first report of pectolinaringenin from C. phlomidis evaluated against An. stephensi and E. vittella. The results suggested that the pectolinaringenin from C. philomidis could be used to develop a new botanical formulation to manage vector mosquitoes and agricultural pests

Intrusion detection technique in wireless sensor network using grid search random forest with Boruta feature selection algorithm

Attacks in wireless sensor networks (WSNs) aim to prevent or eradicate the network's ability to perform its anticipated functions. Intrusion detection is a defense used in wireless sensor networks that can detect unknown attacks. Due to the incredible development in computer-related applications and massive Internet usage, it is indispensable to provide host and network security. The development of hacking technology tries to compromise computer security through intrusion. Intrusion detection system (IDS) was employed with the help of machine learning (ML) Algorithms to detect intrusions in the network. Classic ML algorithms like support vector machine (SVM), K-nearest neighbour (KNN), and filter-based feature selection often led to poor accuracy and misclassification of intrusions. This article proposes a novel framework for IDS that can be enabled by Boruta feature selection with grid search random forest (BFS-GSRF) algorithm to overcome these issues. The performance of BFS-GSRF is compared with ML algorithms like linear discriminant analysis (LDA) and classification and regression tree (CART) etc. The proposed work was implemented and tested on network security laboratory — knowledge on discovery dataset (NSL-KDD). The experimental results show that the proposed model BFS-GSRF yields higher accuracy (i.e., 99%) in detecting attacks, and it is superior to LDA, CART, and other existing algorithms

Bioefficacy of flindersine against Helicoverpa armigera Hübner, Spodoptera litura Fabricius, Anopheles stephensis Liston. and Culex quinquefasciatus Say.

Flindersine, an alkaloid isolated from Toddalia asiatica, was evaluated for their antifeedant, larvicidal and growth inhibitory activities against Helicoverpa armigera, Spodoptera litura and larvicidal activity against vector mosquitoes Anopheles stephensi and Culex quinquefasciatus. For this, leaf disc no choice method was used for agricultural pests; larvicidal activity was tested on second and fourth instar larvae for mosquitoes at different concentrations. Flindersine showed antifeedant, larvicidal and growth inhibitory activities against H. armigera and S. litura and larvicidal activity against vector mosquitoes An. stephensi and Cx. quinquefasciatus. It showed high regression (R2) values of 0.91 and 0.87 against H. armigera and S. litura, respectively for antifeedant activity. Flindersine exhibited more than 65% larvicidal activity against both the pests with LC50 values of 443.04 and 568.88 ppm and R2 values of 0.87 and 0.90 against H. armigera and S. litura, respectively. The larval and pupal duration of tested insects increased to more than five days at 1000 ppm when compared with the control. The adult emergence was reduced when the concentration of flindersine was increased. At 1000 ppm, no adult emergence was observed in both the pests. Flindersine exhibited 100% larvicidal activity against both the tested mosquitoes at 20 ppm concentration, which showed LC50 values of 2.90, 4.19, 1.68 and 2.71 ppm for 2nd and 4th instar larvae of Cx. quinquefasciatus and An. Stephensi, respectively. High regression values were observed for antifeedant, larvicidal and growth inhibitory activities. Flindersine could be used to develop an ecofriendly pesticide formulation to control the agricultural pests and vector mosquitoes