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The most difficult medium for data communication is the underwater medium. It is due to its characteristics. The various existing mode of the communication in water medium are electromagnetic waves, acoustic waves and optical signal. But all these have its own drawbacks. The loss will be huge for Electro-Magnetic (EM) waves, so it is limited to the short range communication, optical waves has line of sight issues. To overcome these, in this paper, a Li-Fi technique is used for data transmission in water medium. It uses Light Emitting Diode (LED) sources for transmission of data. This will ensure the maximum transmission rate and it is more efficient and cheaper than the other existing methods. In this paper, automation via Bluetooth, IoT and it has GPS tracking capabilities are also incorporated.

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The fast advancement of biomedical research technology has expanded and enhanced the spectrum of diagnostic instruments. Various research groups have found optical imaging, ultrasonic imaging, and magnetic resonance imaging to create multifunctional devices that are critical for biomedical activities. Multispectral photoacoustic imaging that integrates the ideas of optical and ultrasonic technologies is one of the most essential instruments. At the same time, early cancer identification is becoming increasingly important in order to minimize fatality. Deep learning (DL) techniques have recently advanced to the point where they can be used to diagnose and classify cancer using biological images. This paper describes a hybrid optimization method that combines in-depth transfer learning-based cancer detection with multispectral photoacoustic imaging. The goal of the PS-ACO-RNN approach is to use ultrasound images to detect and classify the presence of cancer. Bilateral filtration (BF) is often used as a noise removal approach in image processing. In addition, lightweight LEDNet models are used to separate the biological images. A feature extractor with particle swarm with ant colony optimization (PS-ACO) paradigm can also be used. Finally, biological images assign appropriate class labels using a recurrent neural network (RNN) model. The effectiveness of the PS-ACO-RNN technique is verified using a benchmark database, and test results show that the PS-ACO-RNN approach works better than current approaches.