Artificial intelligence (AI) has demonstrated remarkable potential in transforming medical diagnostics across various healthcare domains. This paper explores AI applications in cancer detection, dental medicine, brain tumor database management, and personalized treatment planning. AI technologies such as machine learning and deep learning have enhanced diagnostic accuracy, improved data management, and facilitated personalized treatment strategies. In cancer detection, AI-driven imaging analysis aids in early diagnosis and precise treatment decisions. In dental healthcare, AI applications improve oral disease detection, treatment planning, and workflow efficiency. AI-powered brain tumor databases streamline medical data management, enhancing diagnostic precision and research outcomes. Personalized treatment planning benefits from AI algorithms that analyze genetic, clinical, and lifestyle data to recommend tailored interventions. Despite these advancements, AI integration faces challenges related to data privacy, algorithm bias, and regulatory concerns. Addressing these issues requires improved data governance, ethical frameworks, and interdisciplinary collaboration among healthcare professionals, researchers, and policymakers. Through comprehensive validation, educational initiatives, and standardized protocols, AI adoption in healthcare can enhance patient outcomes and optimize clinical decision-making, advancing the future of precision medicine and personalized care.

Electrical energy can be harvested from the living plants as a new potential renewable energy source. Characterization of the electrical signal is needed to enable an optimum energy harvesting setup condition. In the present paper, an investigation is conducted to analyze the characteristic of Aloe Barbadensis Miller (Aloe Vera) leaves in terms of electrical energy generation under specific experimental setups. The experimental results show that 1111.55uW electrical power can be harvested from the Aloe Vera with 24 pairs of electrodes and this energy is capable to be stored in a capacitor. This energy has a high potential to be used to power up a low power consumption device.

The evolution of the Internet of Things (IoT) has ushered in innovative approaches facilitated by breakthrough technologies. This paper presents a comprehensive review of recent advancements in smart weather stations, focusing on IoT-enabled solutions. The integration of Internet of Things (IoT) technologies has revolutionized traditional weather monitoring systems, enabling seamless data collection, analysis, and dissemination. Commercially available automated weather stations offer cost-effective solutions for comprehensive meteorological data collection. However, challenges such as limited local deployment and reliance on expensive options persist, hindering comprehensive monitoring efforts. The critical need for improved data collection methods is underscored to enhance the accuracy of weather forecasts and address evolving climatic conditions. Climate change impacts, including shifts in weather patterns and rising temperatures, highlight the importance of effective weather monitoring for agriculture, infrastructure, and national security. Additionally, the dependence on non-renewable energy sources for electricity generation emphasizes the environmental and economic implications of energy production. In response to these challenges, numerous IoT based smart weather station systems have been proposed by earlier researchers. The introduction of IoT-enabled smart weather stations represents a significant advancement in weather monitoring technology. These stations leverage IoT technologies to collect, analyse, and visualize meteorological data in real time. In order to identify the challenges and prospects in this area of technology, the purpose of this study is to present a thorough analysis of the suggested designs as well as to compare, evaluate, and assess the outcomes, contributing to the development of robust and efficient weather monitoring systems.

Electrical energy is often wasted through human negligence when people do not switch off electrical appliances such as lighting after leaving a place. Such a scenario often happens in a classroom when the last person leaves the class and forgets to switch off the electrical appliances. Such wastage may not be able to be afforded by schools that are limited financially. Therefore, this research proposed a simple and cost-effective system that can analyze whether there is or is not a human presence in the classroom by applying a counter to count the total number of people entering and leaving the classroom based on the sensing signals of a set of dual PIR sensors only and then correlating this to automatically turn on or off the electrical appliances mentioned. The total number of people identified in the classroom is also displayed on an LCD screen. A TRIZ approach is used to support the ideation of the system. The system can switch on several electrical output loads simultaneously when the presence of people is detected and switch them off when there are no people in the classroom. The proposed system can be expanded to be used in homes, offices, and buildings to prevent the high cost of electricity consumption caused by the negligence of people. This enables smarter control of electricity consumption.

Water irrigation remain as a challenge to supply adequate amount of water to sustain the growth of plant and crops yield along the year in certain part of the world which are heavily affected by climate change. This scenario creates a huge risk toward the world food supply chain. Hence, the application of smart farming system is crucially important now to pave the way for a better the agriculture monitoring system to replace traditional manual monitoring labour by farmers. The smart farming system are usually equipped with environmental stimuli sensing system such as temperature, humidity, soil moisture, light intensity sensing sensors coupled with automation actuators to control the water irrigation rate for the crops in order to save water and at the same time provide adequate water supply for plant growth. The aim of using such smart farming system is to enable higher crops production and less human labour at the same time optimising resources available to minimize cost of farming. Hence, this paper aims to introduce a novel approach of a Raspberry Pi powered IoT smart farming system (ISFS) which can incorporate autonomous monitoring of plant irrigation, temperature, humidity, soil moisture and light intensity, to design a smartphone app that allows users to monitor plantation-related conditions in a user-friendly manner, and to enable automatic control of a drip irrigation system for plants based on data obtained on soil moisture, temperature and sunlight intensity. The proposed prototype with the functionality mentioned is aim to resolve the existing problem and to meet the demand of smart farming application in current era.