In today's internet-driven world, a multitude of attacks occurs daily, propelled by a vast user base. The effective detection of these numerous attacks is a growing area of research, primarily accomplished through intrusion detection systems (IDS). IDS are vital for monitoring network traffic to identify malicious activities, such as Denial of Service, Probe, Remote-to-Local, and User-to-Root attacks. Our research focused on evaluating different auto-encoders for enhancing network intrusion detection. The proposed method sparse deep denoising auto-encoder approach produces the dimensionality reduction used to predict and classify attacks in datasets. With the most records among the datasets by training the auto-encoder on normal network data, this utilized reconstruction error as an indicator of anomalies. We tested our approach using standard datasets like KDDCup99, NSL-KDD, UNSW-NB15, and NMITIDS. Remarkably, our sparse deep denoising auto-encoder achieved an accuracy of over 96% based solely on reconstruction error. The primary aim of this work is to improve intrusion detection by achieving higher detection accuracy compared to existing methods.

“Alzheimer’s disease” (AD) is a neurodegenerative disorder in which the memory shrinks and neurons die. “Dementia” is described as a gradual decline in mental, psychological, and interpersonal qualities that hinders a person’s ability to function autonomously. AD is the most common degenerative brain disease. Among the first signs of AD are missing recent incidents or conversations. “Deep learning” (DL) is a type of “machine learning” (ML) that allows computers to learn by doing, much like people do. DL techniques can attain cutting-edge precision, beating individuals in certain cases. A large quantity of tagged information with multi-layered “neural network” architectures is used to perform analysis. Because significant advancements in computed tomography have resulted in sizable heterogeneous brain signals, the use of DL for the timely identification as well as automatic classification of AD has piqued attention lately. With these considerations in mind, this paper provides an in-depth examination of the various DL approaches and their implementations for the identification and diagnosis of AD. Diverse research challenges are also explored, as well as current methods in the field.

"Network security" is currently among the crucial areas of computer science. Due to the proliferation of IoT tools and "peer-to-peer" systems, the necessity to mitigate safety concerns is prominent. Intrusion detection in network systems is employed to analyse network packets for malicious activity. Different kinds of attacks, such as "Denial of Service", "Probe", "Remote-to-Local", and "User-to-Root" are instances of unexpected behaviour. Once attacks are spotted, customized alerts can be sent to the personnel responsible. In numerous application fields of information security, intrusion detection is a necessity in the modern era. In this study, we examined the usefulness of several autoencoder types in identifying network intrusions. In this research, a system for network intrusion detection based on Sparse Deep Denoising Autoencoder for dimension reduction was established. If the autoencoder is trained on regular network information, reconstruction error (the dissimilarity between the original and recreated data) is used to identify anomalies. Intrusion Detection System Development utilized standard datasets such as KDDCup99, NSL-KDD, UNSW-NB15, and NMITIDS. The effectiveness of four distinct autoencoders was analysed to identify network attacks. We were able to achieve over 96% accuracy with only reconstruction error by using a sparse deep denoising autoencoder. The primary research aim was to enhance the performance of the network by achieving high intrusion detection precision.