Optimizing agricultural data security: harnessing IoT and AI with Latency Aware Accuracy Index (LAAI).

The integration of Internet of Things (IoT) and artificial intelligence (AI) technologies into modern agriculture has profound implications on data collection, management, and decision-making processes. However, ensuring the security of agricultural data has consistently posed a significant challenge. This study presents a novel evaluation metric titled Latency Aware Accuracy Index (LAAI) for the purpose of optimizing data security in the agricultural sector.

An enhanced pattern detection and segmentation of brain tumors in MRI images using deep learning technique.

Neuroscience is a swiftly progressing discipline that aims to unravel the intricate workings of the human brain and mind. Brain tumors, ranging from non-cancerous to malignant forms, pose a significant diagnostic challenge due to the presence of more than 100 distinct types. Effective treatment hinges on the precise detection and segmentation of these tumors early.

A video summarization framework based on activity attention modeling using deep features for smart campus surveillance system.

Like other business domains, digital monitoring has now become an integral part of almost every academic institution. These surveillance systems cover all the routine activities happening on the campus while producing a massive volume of video data. Selection and searching the desired video segment in such a vast video repository is highly time-consuming.

Playing-Dumb Behavior of Trainers During Online Streaming and Trainee's Burnout Behavior: Mediating Role of Psychological Disengagement.

A trainer's behavior is a crucial factor, and it can affect the cognitive engagement of trainees in parts of training and development programs; thus, playing-dumb behavior by a trainer can cause lower attachment and less interested trainees during courses. This study was planned to investigate the impact of trainers' playing-dumb behavior on trainees' burnout behavior under the mediating role of psychological disengagement in online broadcasting. This study followed a convenience sampling technique under a cross-sectional research design, and data are collected from 371 trainees through a questionnaire.

Abnormal Activity Recognition from Surveillance Videos Using Convolutional Neural Network.

Unlabelled: Background and motivation: Every year, millions of Muslims worldwide come to Mecca to perform the Hajj. In order to maintain the security of the pilgrims, the Saudi government has installed about 5000 closed circuit television (CCTV) cameras to monitor crowd activity efficiently.

Problem: As a result, these cameras generate an enormous amount of visual data through manual or offline monitoring, requiring numerous human resources for efficient tracking.

CapPlant: a capsule network based framework for plant disease classification.

Accurate disease classification in plants is important for a profound understanding of their growth and health. Recognizing diseases in plants from images is one of the critical and challenging problem in agriculture. In this research, a deep learning architecture model (CapPlant) is proposed that utilizes plant images to predict whether it is healthy or contain some disease.

Introspective analysis of convolutional neural networks for improving discrimination performance and feature visualisation.

Deep neural networks have been widely explored and utilised as a useful tool for feature extraction in computer vision and machine learning. It is often observed that the last fully connected (FC) layers of convolutional neural network possess higher discrimination power as compared to the convolutional and maxpooling layers whose goal is to preserve local and low-level information of the input image and down sample it to avoid overfitting. Inspired from the functionality of local binary pattern (LBP) operator, this paper proposes to induce discrimination into the mid layers of convolutional neural network by introducing a discriminatively boosted alternative to pooling (DBAP) layer that has shown to serve as a favourable replacement of early maxpooling layer in a convolutional neural network (CNN).

Solutions of nonlinear real world problems by a new analytical technique.

Here a new analytical scheme is presented to solve nonlinear boundary value problems (BVPs) of higher order occurring in nonlinear phenomena. This method is called second alternative of Optimal Homotopy Asymptotic Method. It converts a complex nonlinear problem into zeroth order and first order problem.