There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Cognitive routing protocol for disaster-inspired Internet of Things

The next section surveys some typical systems that have been developed to aid decision makers: from these a classification of decision support systems (DSS) is developed. The systems studied include a project management (PERT) package, a statistical package and a corporate planning system. The authors introduce three aspects for study in a DSS:a knowledge system, a language system and a problem processing system.

Foundations of Decision Support Systems

Encyclopedia of E‐Business Development and Management in the Global Economy

Floods are among the most destructive natural hazards that cost lives and disrupt the socioeconomic activities of residents, especially in the rapidly growing cities of developing countries. Jeddah, a coastal city situated in Saudi Arabia, has experienced severe flash flood events in recent years. With intense rainfall, extensive coastal developments, and sensitive ecosystems, the city is susceptible to severe flash flood risks. The objective of this article is to apply an Analytic Hierarchy Process (AHP) model to explore the impacts of flash flood hazards and identify the most effective approaches to reducing the flash flood impacts in Jeddah using expert’s opinions. The study utilizes experts’ judgments and employs the AHP for data analyses and modeling. The results indicated that property loss has the highest probability of occurrence in the events of a flash flood with a priority level of 42%, followed by productivity loss (28%). Injuries and death were rated the least priority of 18% and 12%, respectively. Concerning flood impact reduction alternatives, river management (41%) and early warning system (38%) are the most favorable options. The findings could assist the government to design appropriate measures to safeguard the lives and properties of the residents. The study concludes by underscoring the significance of incorporating experts’ judgments in assessing flash flood impacts.

https://www.mdpi.com/2306-5338/7/1/10/pdf

Flash Flood Impact Assessment in Jeddah City: An Analytic Hierarchy Process Approach

Efficient Short-Term Electricity Load Forecasting for Effective Energy Management

Face recognition based on statistical features and SVM classifier

Alzheimer’s disease (AD), the most familiar type of dementia, is a severe concern in modern healthcare. Around 5.5 million people aged 65 and above have AD, and it is the sixth leading cause of mortality in the US. AD is an irreversible, degenerative brain disorder characterized by a loss of cognitive function and has no proven cure. Deep learning techniques have gained popularity in recent years, particularly in the domains of natural language processing and computer vision. Since 2014, these techniques have begun to achieve substantial consideration in AD diagnosis research, and the number of papers published in this arena is rising drastically. Deep learning techniques have been reported to be more accurate for AD diagnosis in comparison to conventional machine learning models. Motivated to explore the potential of deep learning in AD diagnosis, this study reviews the current state-of-the-art in AD diagnosis using deep learning. We summarize the most recent trends and findings using a thorough literature review. The study also explores the different biomarkers and datasets for AD diagnosis. Even though deep learning has shown promise in AD diagnosis, there are still several challenges that need to be addressed.

/pdf/deep-learning-based-diagnosis-of-alzheimers-disease-7vkjin6j.pdf

Deep Learning-Based Diagnosis of Alzheimer’s Disease

Surveillance systems regularly create massive video data in the modern technological era, making their analysis challenging for security specialists. Finding anomalous activities manually in these enormous video recordings is a tedious task, as they infrequently occur in the real world. We proposed a minimal complex deep learning-based model named EADN for anomaly detection that can operate in a surveillance system. At the model’s input, the video is segmented into salient shots using a shot boundary detection algorithm. Next, the selected sequence of frames is given to a Convolutional Neural Network (CNN) that consists of time-distributed 2D layers for extracting salient spatiotemporal features. The extracted features are enriched with valuable information that is very helpful in capturing abnormal events. Lastly, Long Short-Term Memory (LSTM) cells are employed to learn spatiotemporal features from a sequence of frames per sample of each abnormal event for anomaly detection. Comprehensive experiments are performed on benchmark datasets. Additionally, the quantitative results are compared with state-of-the-art methods, and a substantial improvement is achieved, showing our model’s effectiveness.

/pdf/eadn-an-efficient-deep-learning-model-for-anomaly-detection-2wx64coo.pdf

Mohammad Hijji

Papers

Efficient Short-Term Electricity Load Forecasting for Effective Energy Management

Face recognition based on statistical features and SVM classifier

Deep Learning-Based Diagnosis of Alzheimer’s Disease

EADN: An Efficient Deep Learning Model for Anomaly Detection in Videos

Face recognition based on statistical features and SVM classifier