Citrus diseases detection using innovative deep learning approach and Hybrid Meta-Heuristic.

Citrus farming is one of the major agricultural sectors of Pakistan and currently represents almost 30% of total fruit production, with its highest concentration in Punjab. Although economically important, citrus crops like sweet orange, grapefruit, lemon, and mandarins face various diseases like canker, scab, and black spot, which lower fruit quality and yield. Traditional manual disease diagnosis is not only slow, less accurate, and expensive but also relies heavily on expert intervention.

Novel deep neural network architecture fusion to simultaneously predict short-term and long-term energy consumption.

Energy is integral to the socio-economic development of every country. This development leads to a rapid increase in the demand for energy consumption. However, due to the constraints and costs associated with energy generation resources, it has become crucial for both energy generation companies and consumers to predict energy consumption well in advance.

An innovative artificial neural network model for smart crop prediction using sensory network based soil data.

A thriving agricultural system is the cornerstone of an expanding economy of agricultural countries. Farmers' crop productivity is significantly reduced when they choose the crop without considering environmental factors and soil characteristics. Crop prediction enables farmers to select crops that maximize crop yield and earnings.

Musculoskeletal morbidity in adults with spinal cord injuries: A nationwide cohort study.

Background: An increase in the demand for quality of life following spinal cord injuries (SCIs) is associated with an increase in musculoskeletal (MSK) pain, highlighting the need for preventive measure research.

Objective: This study aimed to evaluate the incidence and hazards of MSK morbidities among Korean adults with SCIs, as well as the influence of SCI location on MSK morbidities.

Methods: Patient populations were selected from Korean National Health Insurance Service data (n = 276).

Environmental determinants and risk behaviour in the case of indigenous malaria in Muara Enim Regency, Indonesia: A case-control design.

Introduction: Malaria is a significant public health concern in Indonesia. Muara Enim Regency is one of the districts in South Sumatra with the most important number of indigenous malaria cases in the last three years (2018-2020). Therefore, this study aimed to identify determinants of indigenous malaria in the Muara Enim Regency.

Characterization of microplastics in the water and sediment of Baram River estuary, Borneo Island.

The Baram River is one of the largest rivers in Sarawak, where many large industries, such as plywood, sawmills, shipyards, interisland ports, and other wood-based industries are located along the river. Microplastic contamination has become a widespread and growing concern worldwide because of the small sizes of microplastics and their presence in seafood such as fish, squid, scallop, crabs, shrimp, and mussels. In this study, microplastics were found in all sampling stations.

Pesticides in Drinking Water-A Review.

The ubiquitous problem of pesticide in aquatic environment are receiving worldwide concern as pesticide tends to accumulate in the body of the aquatic organism and sediment soil, posing health risks to the human. Many pesticide formulations had introduced due to the rapid growth in the global pesticide market result from the wide use of pesticides in agricultural and non-agricultural sectors. The occurrence of pesticides in the water body is derived by the runoff from the agricultural field and industrial wastewater.

Waste valorization using solid-phase microbial fuel cells (SMFCs): Recent trends and status.

This review article discusses the use of solid waste processed in solid-phase microbial fuel cells (SMFCs) as a source of electrical energy. Microbial Fuel Cells (MFCs) are typically operated in the liquid phase because the ion transfer process is efficient in liquid media. Nevertheless, some researchers have considered the potential for MFCs in solid phases (particularly for treating solid waste).

Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing.

With the increase in the amount of data captured during the manufacturing process, monitoring systems are becoming important factors in decision making for management. Current technologies such as Internet of Things (IoT)-based sensors can be considered a solution to provide efficient monitoring of the manufacturing process. In this study, a real-time monitoring system that utilizes IoT-based sensors, big data processing, and a hybrid prediction model is proposed.

A Personalized Healthcare Monitoring System for Diabetic Patients by Utilizing BLE-Based Sensors and Real-Time Data Processing.

Current technology provides an efficient way of monitoring the personal health of individuals. Bluetooth Low Energy (BLE)-based sensors can be considered as a solution for monitoring personal vital signs data. In this study, we propose a personalized healthcare monitoring system by utilizing a BLE-based sensor device, real-time data processing, and machine learning-based algorithms to help diabetic patients to better self-manage their chronic condition.