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When individuals are in the market for a new home, they often display a more cautious approach when it comes to their budgets and overall market strategies. However, the current system for determining house prices typically lacks the essential element of predicting future market trends and potential price increases. The existing system Give the functionality for buyers, allowing them to search for houses by features or address. Machine Learning has significantly contributed to various areas such as natural language processing, product recommendations, healthcare, customer service, and automotive safety. Its widespread adoption in these fields highlights its trendiness and its potential for transformative impact. In light of this, we are incorporating Machine Learning into our project. Predicting the selling price of houses in cities such as Bengaluru remains a complex and intricate task. Numerous interrelated factors influence the property prices in these cities. Essential elements that impact the price comprise the property's size, its location, and its characteristics, including the number of rooms and bathrooms.

COVID-19, the disease caused by the novel corona virus, can cause lung complications such as pneumonia and, in the most severe cases, acute respiratory distress syndrome, or ARDS. Another possible complication of COVID-19 is sepsis, which can cause long-term damage to the lungs and other organs. COVID-19 virus is primarily transmitted through droplets produced when an infected person coughs, sneezes, or exhales. These droplets are too heavy to float in the air and quickly fall to the ground or other surfaces. As everyone is aware, the corona virus disease 2019 (COVID-19) spread throughout the world in early 2020, causing the world to face an existential health crisis. Thus, automating the detection of lung infections from computed tomography (CT) images has the potential to supplement the traditional healthcare strategy for combating COVID-19. However, segmenting infected regions from CT slices is difficult due to high variation in infection characteristics and low-intensity contrast between infections and normal tissues. Furthermore, collecting a large amount of data in a short period of time is impractical. Our proposed solution will analyse a CT image of the lung and detect the infected portion of the lung, as well as the percentage of the affected portion. The system will identify the infection severity and will help patients to take essential measures. Keywords: Preprocessing, Segmentation, Feature Ex-traction, Classification, CT Images, Image Preprocessing.

In this work, automatic bird species recognition systems were developed, and their identification methods were investigated. Automatically identifying bird calls without physical intervention has been a large and tedious endeavor for major studies in various subfields of taxonomy and other ornithology. This task uses a two-step identification process. In the first phase, an ideal dataset was created containing all recordings of different bird species. Next, the sound clip was subjected to various sound pre-processing techniques such as pre-emphasis, framing, silence removal, and reconstruction. A spectrogram was generated for each reconstructed sound clip. The second step used a neural network given with the spectrogram as input. A convolutional neural network (CNN) classifies sound clips and recognizes bird species based on input characteristics. In the above system, a real-time implementation model was also designed and executed. Keywords: Bird species identification, bird sound, sound pre-processing techniques, Convolutional Neural Network, Spectrograms

Neural Cryptography is a new thread that integrates cryptography and neural networks for cryptanalysis and encryption applications. We show that Neural Networks can execute symmetric encryption in an adversarial context in this paper, and we build on the existing literature on the subject. Cryptography's purpose is to make it difficult to decipher a cypher and recreate the plain text without the associated key. Your messages are encrypted with excellent cryptography in such a way that brute force attacks against the algorithm or key are nearly impossible. Good cryptography is secure because it employs extremely long keys and encryption techniques that are resistant to various types of attack. The neural net application is the next step in the evolution of good cryptography. This paper discusses the use of neural networks in cryptography, including how to create neural networks that can be utilized in cryptography. Keywords: Cryptography key, encryption system, encryption algorithm, artificial neural network,chaos maps, logistic encryption.