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Heavy metals (HMs) are metallic substances. They enter biotic and abiotic systems through natural and human activities. These HMs have an impact on the atmosphere, soil, and groundwater, and they also affect all living things, especially humans, when they enter the food chain. Therefore, monitoring and removing HMs from the environment and humans are crucial for maintaining HMs-based toxicity. The detection of HMs from environmental and human samples has been performed by techniques such as atomic adsorption spectrometry (AAS) and inductively coupled plasma mass spectrometry (ICP-MS). With the advancement of AI-based technology, HMs are now detected and removed from the environment and human systems. This review discusses the impact of HMs on the environment and human health, their detection and removal techniques, and the integration of recent advancements in AI-based technology for the detection and removal of HMs from environmental and human samples.

Agriculture is the primary source of income in developing countries like India. Agriculture accounts for 17 percent of India’s total GDP, with almost 60 percent of the people directly or indirectly employed. While researchers and planters focus on a variety of elements to boost productivity, crop loss due to disease is one of the most serious issues they confront. Crop growth monitoring and early detection of pest infestations are still a problem. With the expansion of cultivation to wider fields, manual intervention to monitor and diagnose insect and pest infestations is becoming increasingly difficult. Failure to apply on time fertilizers and pesticides results in more crop loss and so lower output. Farmers are putting in greater effort to conserve crops, but they are failing most of the time because they are unable to adequately monitor the crops when they are infected by pests and insects. Pest infestation is also difficult to predict because it is not evenly distributed. In the recent past, modern equipment, tools, and approaches have been used to replace manual involvement. Unmanned aerial vehicles serve a critical role in crop disease surveillance and early detection in this setting. This research attempts to give a review of the most successful techniques to have precision-based crop monitoring and pest management in agriculture fields utilizing unmanned aerial vehicles (UAVs) or unmanned aircraft. The researchers’ reports on the various types of UAVs and their applications to early detection of agricultural diseases are rigorously assessed and compared. This paper also discusses the deployment of aerial, satellite, and other remote sensing technologies for disease detection, as well as their Quality of Service (QoS).

Signal processing and data analysis are widely used methods in a biomedical research. In recent years, detection of cardiovascular abnormalities in patients can be achieved by using electrocardiogram (ECG) recording. In this paper, a fuzzy-based multi-objective algorithm using Fast Fourier Transform (FFT) is proposed. Initially, an effective FFT is used to extract the feature points in ECG signals, such as PQRST wave's amplitude and wave function and then the proposed multi-objective genetic algorithm is used to classify the abnormality of heart patient. Basically, the ECG behaviour depends on various factors such as age, physical condition of patients and the surrounding environment. The efficient detection of abnormalities (e.g. arrhythmia and myocardial abstraction) can be achieved by initializing the above-mentioned factors and maintaining a database containing previously attributed signals, such MIT-BIH arrhythmia. The present study provides efficiency of around 98.7% in detection of abnormalities in patients. [ABSTRACT FROM PUBLISHER]