Alzheimer’s disease (AD) may cause damage to the memory cells permanently, which results in the form of dementia. The diagnosis of Alzheimer’s disease at an early stage is a problematic task for researchers. For this, machine learning and deep convolutional neural network (CNN) based approaches are readily available to solve various problems related to brain image data analysis. In clinical research, magnetic resonance imaging (MRI) is used to diagnose AD. For accurate classification of dementia stages, we need highly discriminative features obtained from MRI images. Recently advanced deep CNN-based models successfully proved their accuracy. However, due to a smaller number of image samples available in the datasets, there exist problems of over-fitting hindering the performance of deep learning approaches. In this research, we developed a Siamese convolutional neural network (SCNN) model inspired by VGG-16 (also called Oxford Net) to classify dementia stages. In our approach, we extend the insufficient and imbalanced data by using augmentation approaches. Experiments are performed on a publicly available dataset open access series of imaging studies (OASIS), by using the proposed approach, an excellent test accuracy of 99.05% is achieved for the classification of dementia stages. We compared our model with the state-of-the-art models and discovered that the proposed model outperformed the state-of-the-art models in terms of performance, efficiency, and accuracy.

Rainfall plays a pivotal role in regulating water levels in reservoirs, which can lead to overflow or drought, depending on the unpredictability of rainfall patterns. In 2020 and 2022, Sindh experienced seven episodes of normal to heavy rains, causing flooding and disrupting major highways such as Gwadar-Karachi. This study evaluates daily and cumulative rainfall data in Karachi for the months of June, July, and August from 2016 to 2023. The rainfall data is divided, focusing on the monsoon rains over the eight-year period. Among these years, the highest recorded rainfall of 93.099mm occurred on August 11, 2019, while the lowest rainfall of 0.001mm was noted on July 26, 2016, and June 18, 2017. The yearly (2016-2023) cumulative rainfall for the study period was 114.6mm, 187.0mm, 34.9mm, 310.5mm, 347.9mm, 285.3mm, 761.4mm, and 167.6mm respectively. Notably, the cumulative rainfall and the frequency of rain events were highest in August 2020 and 2022. The monthly data revealed that Karachi experienced exceptionally heavy rainfall in August 2020 and 2022, resulting in significant disruption and chaos in the city. Moreover, when considering the data across the years, it becomes evident that Karachi faced unprecedented rainfall in 2020 compared to the preceding years.This research represents the first comprehensive analysis of the intense rainfall events in August 2020 and 2022 in Karachi. It identifies trends in rainfall patterns that led to flood-like conditions in the city. This study provides a detailed and quantitative understanding of rainfall occurrences during the monsoon season. Such insights are invaluable for assessing flood risks in Karachi, Pakistan.

This paper aims to examine the trends of the maximum temperature (Tmax) and minimum temperature (Tmin) in Lahore over a period of 42 years (1988 to 2029). The study employs the Mann-Kendall statistical test to analyze the linear trends of both Tmin and Tmax annually and seasonally. To determine the linear trends in temperature extremes (Te), a linear curve fitting method was employed. In modeling Tmax and Tmin, a sine function was utilized. The results showed that Tmin exhibited an increasing trend both annually and seasonally, except for winter, where no significant trend was observed. Conversely, Tmax showed a decreasing trend both annually and seasonally, except for the monsoon and pre-monsoon periods, where no significant trends were found. Furthermore, the study divided the Te data from 1988 to 2019 into twotime series: from 1988 to 2003 and from 2004 to 2019. The findings indicated that Tmin had no significant trend, while Tmax demonstrated an increasing trend for the first time series. In contrast, both Tmin and Tmax exhibited increasing trends for the second time series. Moreover, when the time series was divided into six parts for trend analysis, mixed trends, whether increasing or decreasing, were observed. To investigate the periodicity of Te, the sine function was applied, and the results showed that Tmin had no periodicity. However, Tmax exhibited periodicity, and it was observed that the peak pattern repeated in reverse after 2004. Based on the proposed sine function model, the study predicted the future pattern of the maximum temperature variation in Lahore for the next ten years (i.e., 2020 to 2029).