Unmanned aerial vehicles (UAVs), commonly known as drones, have found extensive applications across diverse sectors, such as agriculture, delivery, surveillance, and military. In recent times, drone swarming has emerged as a novel field of research, which involves multiple drones working in collaboration towards a shared objective. This innovation holds immense potential in transforming the way we undertake tasks, including military operations, environmental monitoring, and search and rescue missions. However, the emergence of drone swarms also brings new security challenges, as they can be susceptible to hacking and intrusion. To address these concerns, we propose utilizing a timed probabilistic automata (TPA)-based intrusion detection system (IDS) to model the normal behavior of drone swarms and identify any deviations that may indicate an intrusion. This IDS system is particularly efficient and adaptable in detecting different types of attacks in drone swarming. Its ability to adapt to evolving attack patterns and identify zero-day attacks makes it an invaluable tool in protecting drone swarms from malicious attacks.

Optical Character Recognition (OCR) is a predominant aspect to transmute scanned images and other visuals into text. Computer vision technology is extrapolated onto the system to enhance the text inside the digitized image. This preliminary provisional setup holds the invoice's information and converts it into JSON and CSV configurations. This model can be helpful in divination based on knowledge engineering and qualitative analysis in the nearing future. The existing system contains data extraction and nothing more. In a paramount manner, image pre-processing techniques like black and white, inverted, noise removal, grayscale, thick font, and canny are applied to escalate the quality of the picture. With the enhanced image, more OpenCV procedures are carried through. In the very next step, three different OCRs are used: Keras OCR, Easy OCR, and Tesseract OCR, out of which Tesseract OCR gives the precise result. After the initial steps, the undesirable symbols (/t, /n) are cleared to get the escalated text as an output. Eventually, a unique work that is highly accurate in giving JSON and CSV formats is developed.Impact statement— In our protrude, a front-end android app is developed which takes input from the user and stores the output onto the database. The JSON and CSV files can be viewed through an app by the end.

In the current scenario, getting good drinking water or getting good quality water for domestic purpose is highly essential to maintain good health. By exploiting the water scarcity problem many private tanker water suppliers are providing water for very high cost. Even though the quality of water becomes questionable, many people are availing only this facility to fill their tanks in India, as there is no alternative. The proposed system uses a PH (Potential of Hydrogen) sensor and a temperature sensor to assess the water quality; a relay driver and solenoid valve to communicate with central controller ESP32 (ESP represents the company Espressif Systems) about the water quality and water level in tank; the collected data is sent through the cloud (analysing the PH levels of the collected data) to the mobile number of the user. The sensor setup can be controlled by the android application. All the components used are very simple reactive machines category, coming under type II of Artificial Intelligence. This system applies a very simple logic as intelligence to detect the PH level and water level in residential flats.

Energy consumption in metropolitan cities is increasing day by day. In every city, considerable amount of electricity is being used for the purpose of street lighting system. Some areas of the city may have low frequency of passerby, but it's observed that the amount of energy the street lights consume in these areas is same as that of areas with high frequency passerby. As a result enormous amount of energy is wasted without being used. In the proposed system, high intensity discharge lamps are replaced by LED's which can alter its intensity based on the need. Movement of vehicles is sensed using LDR (Light Dependent Resistor) and the intensity of the street light is reduced when not in use. The system also detects fault in the system and indicates it to the base station using GSM (Global System for Mobile communication) technology by sending SMS (short message service).

In recent times very sophisticated and complicated malware are being produced on a regular basis and it has grown into one of the stealthiest and lethal attack techniques used against critical information technology infrastructures. In recent years, android is now the most expected smartphone operating system which is open source. However, Android's fast progress is impeded by the rising threat of Android malware, which uses Smartphones to carry out harmful acts. Malware uses a number of techniques to evade detection systems, posing new hurdles for reliable detection. The two main classifications for current Android malware detection approaches are signature-based detection and machine-learning-based detection. To address the limits of signature-based detection, researchers and antimalware firms have turned to a machine-learning-based detection approach. This review study disseminates present machine-learning-based Android malware detection techniques and compares them parametrically. As a result, the purpose of this review is to look at a variety of machine-learning-based detection methods as well as future possibilities in this domain.