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Brain signal data are recordings of the electrical activity of the brain made using the electroencephalography (EEG). EEG is considered the future of neuroscience, as it has emerged as the latest gold standard for detecting neurological disorders such as dementia, mild cognitive impairment (MCI), Alzheimer's disease (AD), Parkinson's disease, schizophrenia, epilepsy, and so on. Due to its cost-effectiveness and portability, EEG is becoming the first choice when it comes to analyses and the detection of neurodegenerative disorders. In addition, it has been widely accepted that analyzing EEG data is the better method for solving the challenge of learning about the brain's dynamics. EEG measures the electrical activity of the brain in real-time and can provide valuable information about brain function and dysfunction. The ability to analyze EEG data is crucial for improving our knowledge of cognitive processes and aiding in the identification of brain illnesses. To discover the most recent trends and gaps in the EEG research, a lot of scholarly articles have been studied. However, the following research problems are still existing: • Poor classification performances due to old and shallow traditional machine learning (TML) algorithms. • Lack of efficient noise removal algorithms being used. • Computationally expensive models are being proposed, which consume more power and time to diagnose brain diseases. • Limit the diagnosis to detect one disorder, even though there may be more than one brain disorder with similar symptoms. • Failed to accommodate a huge volume of EEG data. In this dissertation, all five of the aforementioned research challenges have been answered. To address the aforementioned research issues, this dissertation focuses on the detection of AD and MCI among 600 neuro-diseases. The main objective of this dissertation is to develop computer-aided diagnostic methods for effectively detecting both AD and MCI handling a large volume of EEG data with improved performance and cost-effective deep learning (DL) models.

Being overweight and obese are risk factors for multiple chronic diseases, including hypertension, type II diabetes mellitus, and cardiovascular diseases. These factors drive the development of new treatments to control obesity. The regulation of food appetite is a critical element of energy balance in obese people. Hypothalamic neurons of the brain region play a crucial role in balancing hunger and satiety for an individual. The hypothalamus's arcuate nucleus (ARC) contains Proopiomelanocortin (POMC) neurons that mediate food satiety signalling. They express one of the serotonin receptors called 5HT2cR, which binds to the ligand serotonin to activate satiety signalling through the anorexigenic pathway. Hence, the 5HT2cR is proposed as one of the potential targets for regulating obesity and overeating disorders. Several agonists have been developed to increase the functional activity of 5HT2cR and further enhance satiety signalling. This satiety signalling is dysregulated in overeating diseases like Prader-Willi syndrome and Bardet–Biedl syndrome. Studies have shown that a succulent plant, Caralluma fimbriata extract (CFE), can reduce body weight and waist circumference. In addition, preliminary data on modified fibroblasts (3T3 cells) showed that CFE increased expression of the full-length 5HT2cR mRNA compared to the truncated/nonfunctional variant. However, the mechanism through which CFE reduces obesity remains undefined. The FDA approved the 5HT2cR-specific agonist Lorcaserin to treat obesity in 2012. However, the drug Lorcaserin has limited use for the long-term treatment of obesity due to concerns about side effects and it was withdrawn from sale in the USA in 2020. The current study aims to investigate whether the combination of CFE and a lower dose of lorcaserin can increase 5HT2cR activation and reduce diet-induced obesity. The CFE is predicted to increase 5HT2cR expression, thereby increasing satiety signalling when activated by the specific agonist lorcaserin. The study comprises three phases; the first phase involved human SHSY5Y neuroblast cell culture as a model to study 5HT2cR expression studies in response to treatment with CFE. The treatment was carried out for 48h and 96h duration with different concentrations of CFE. In addition, these SHSY5Y cells can be differentiated from fibroblasts to mature neurons. These differentiated neurons retain the property of expressing 5HT2cR. Therefore, these neurons were treated with CFE for 48h and 96h with different concentrations (ranging from 0.1 to 50 µg /mL) of CFE. The treated cells were analysed for differential gene expression studies, including full-length (functional) and truncated 5HT2cR expression by quantitative PCR and pilot-scale next-generation sequencing (NGS) for further metabolic analysis. The results showed a significant two-fold increase in the expression level of the 5HT2cR functional receptor in CFE-treated neurons at 25 µg /mL (1.87-fold at 48h and 2.41-fold at 96h of CFE treatments with p value 0.01 and 0.03 respectively) and at 50 µg /mL (2.84-fold at 48h and 2.54-fold at 96h of treatments with p values