In the current era, many fake videos and images are created with the help of various software and new AI (Artificial Intelligence) technologies, which leave a few hints of manipulation. There are many unethical ways videos can be used to threaten, fight, or create panic among people. It is important to ensure that such methods are not used to create fake videos. An AI-based technique for the synthesis of human images is called Deep Fake. They are created by combining and superimposing existing videos onto the source videos. In this paper, a system is developed that uses a hybrid Convolutional Neural Network (CNN) consisting of InceptionResnet v2 and Xception to extract frame-level features. Experimental analysis is performed using the DFDC deep fake detection challenge on Kaggle. These deep learning-based methods are optimized to increase accuracy and decrease training time by using this dataset for training and testing. We achieved a precision of 0.985, a recall of 0.96, an f1-score of 0.98, and support of 0.968.

Smart cities are the current buzz phrase between infrastructure developments. With a gradually increasing inflow on populations into cities then a continuously thriving necessity to better deal with resources, countless cities kind of San Francisco, united states, Singapore, Portugal, England is experimenting together with upcoming state-of-the-art technologies after fulfill their cities smarter. Among these current trending technologies is the Internet of Things (IoT), Big Data and Artificial Intelligence (AI) which has revolutionized the way we analyze patterns yet traits between human behaviors. With Big Data, current fragmented and remoted data sets do stand well-acquainted beside an overarching point of view in accordance with provide high quality solutions in accordance with frequent issues up to expectation have an effect on rapidly growing cities today. Here are 5 ways within which Big Data could show fundamental in smart cities about the future. A lot of governments are thinking about adopting the smart city thought between theirs urban areas at that point executing impressive records services up to expectation assist smart city components in accordance with attain the required stage concerning supportability and improve the living norms. Smart cities take advantage of more than one technology in conformity with get better the concert about healthiness, transportation, power, education, and cloud applications lead after greater stages about remedy about their citizens. In addition, it attempts in accordance with pick out the necessities as assist the implementation on substantial data purposes for smart city services. The criticism displays as numerous possibilities are accessible because of making use of big data in smart cities; conversely, so are nevertheless various concerns and disputes in conformity with stay addressed to attain higher utilization about this technology.

The advancement and innovations in the field of science and technology paved way for various advanced treatments in the field of medicine. They are implemented using sensors, and computer-aided designs with artificial intelligence techniques. This helps in the detection of serious health constraints at an earlier stage with appropriate treatments using decision-making techniques. One of the important health concerns that are increasing rapidly is cardiovascular disorders. This includes Arrhythmia and Myocardial Infarction. Earlier prediction and classification can protect them from serious constraints. They are diagnosed using the Electrocardiogram (ECG). To obtain accurate results, artificial intelligence techniques are implemented to extract the optimum output. The proposed system includes the detection and classification using deep learning techniques with the Internet of Things (IoT). The existing heartbeat detection system is overcome using a deep convolutional neural network. This helps in the implementation of automatic heartbeat detection and identification of abnormalities. The ECG signals are pre-processed with segmentation and feature extraction techniques. The classification and identification of constraints in the functioning of the heart are identified using optimization algorithms. The proposed system is trained, tested, and evaluated using the MIT-BIH arrhythmia database. The accuracy and efficiency of the proposed system are 99.98% using the MIT-BIH dataset.

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