Understanding the complex dynamics of landslides is crucial for disaster planners to make timely ... more Understanding the complex dynamics of landslides is crucial for disaster planners to make timely and effective decision that saves lives and reduces the economic impact on society. Using the landslide inventory of Chittagong Metropolitan Area (CMA), we created a new Artificial Intelligence (AI) based insight system for the town planners and senior disaster recovery strategists of Chittagong, Bangladesh. Our system generates dynamic AI-based insights for a range of complex scenarios created from 7 different landslide feature attributes. The users of our system can select a particular kind of scenario out of the exhaustive list of 1.054X10 41 possible scenario sets and our AI-based system will immediately predict how many casualties are likely to occur based on the selected kind of scenario. Moreover, an AI-based system shows how landslide attributes (e.g., rainfall, area of mass, elevation, etc.) correlate with landslide casualty by drawing detailed trend lines performing both linear and logistic regressions. According to literature and the best of our knowledge, our CMA scenariobased AI insight system is the first of its kind providing the most comprehensive understanding of landslide scenarios and associated deaths and damages in CMA. The system was deployed on a wide range of platforms including Android, iOS, and Windows systems so that it could be easily adapted to strategic disaster planners. The deployed solutions were handed down to 12 landslide strategists and disaster planners for evaluations whereby 91.67% of users found the solution easy to use, effective and self-explanatory while using via mobile. 1 Introduction Landslides are natural phenomenon that have an adverse effect on human life as well as economy (Rabby and Li, 2020). For the purpose of reducing the negative impact of landslide and to have an increased level of disaster preparedness (Alam, 2020), it is crucial to have a multi-dimensional understanding landslide attributes. The complex nature of landslide dynamics makes it extremely difficult to understand the impact of a particular type of landslide. Bangladesh is susceptible to a variety of natural and human-induced hazards including tropical cyclones, floods, droughts, earthquakes, tsunamis, and landslides (Alam, 2020). Particularly, landslides become recurrent phenomena in the Southeast Bangladesh in recent decades. Therefore, the Government of Bangladesh (GoB) and its coastal residents have been in reducing resultant deaths from tropical cyclones but
Utilizing social media data is imperative in comprehending critical insights on the Russia–Ukrain... more Utilizing social media data is imperative in comprehending critical insights on the Russia–Ukraine cyber conflict due to their unparalleled capacity to provide real-time information dissemination, thereby enabling the timely tracking and analysis of cyber incidents. The vast array of user-generated content on these platforms, ranging from eyewitness accounts to multimedia evidence, serves as invaluable resources for corroborating and contextualizing cyber attacks, facilitating the attribution of malicious actors. Furthermore, social media data afford unique access to public sentiment, the propagation of propaganda, and emerging narratives, offering profound insights into the effectiveness of information operations and shaping counter-messaging strategies. However, there have been hardly any studies reported on the Russia–Ukraine cyber war harnessing social media analytics. This paper presents a comprehensive analysis of the crucial role of social-media-based cyber intelligence in un...
The surge in cybercrime has emerged as a pressing concern in contemporary society due to its far-... more The surge in cybercrime has emerged as a pressing concern in contemporary society due to its far-reaching financial, social, and psychological repercussions on individuals. Beyond inflicting monetary losses, cyber-attacks exert adverse effects on the social fabric and psychological well-being of the affected individuals. In order to mitigate the deleterious consequences of cyber threats, adoption of an intelligent agent-based solution to enhance the speed and comprehensiveness of cyber intelligence is advocated. In this paper, a novel cyber intelligence solution is proposed, employing four semantic agents that interact autonomously to acquire crucial cyber intelligence pertaining to any given country. The solution leverages a combination of techniques, including a convolutional neural network (CNN), sentiment analysis, exponential smoothing, latent Dirichlet allocation (LDA), term frequency-inverse document frequency (TF-IDF), Porter stemming, and others, to analyse data from both s...
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Understanding the complex dynamics of landslides is crucial for disaster planners to make timely ... more Understanding the complex dynamics of landslides is crucial for disaster planners to make timely and effective decisions that save lives and reduce the economic impact on society. Using the landslide inventory of the Chittagong Metropolitan Area (CMA), we have created a new artificial intelligence (AI)-based insight system for the town planners and senior disaster recovery strategists of Chittagong, Bangladesh. Our system generates dynamic AI-based insights for a range of complex scenarios created from 7 different landslide feature attributes. The users of our system can select a particular kind of scenario out of the exhaustive list of 1.054 × 1041 possible scenario sets, and our AI-based system will immediately predict how many casualties are likely to occur based on the selected kind of scenario. Moreover, an AI-based system shows how landslide attributes (e.g., rainfall, area of mass, elevation, etc.) correlate with landslide casualty by drawing detailed trend lines by performin...
Negative events are prevalent all over the globe round the clock. People demonstrate psychologica... more Negative events are prevalent all over the globe round the clock. People demonstrate psychological affinity to negative events, and they incline to stay away from troubled locations. This paper proposes an automated geospatial imagery application that would allow a user to remotely extract knowledge of troubled locations. The autonomous application uses thousands of connected news sensors to obtain real-time news pertaining to all global troubles. From the captured news, the proposed application uses artificial intelligence-based services and algorithms like sentiment analysis, entity detection, geolocation decoder, news fidelity analysis, and decomposition tree analysis to reconstruct global threat maps representing troubled locations interactively. The fully deployed system was evaluated for full three months of summer 2021, during which the autonomous system processed above 22 k news from 2397 connected news sources involving BBC, CNN, NY Times, Government websites of 192 countri...
Tropical cyclones take precious lives, damage critical infrastructure, and cause economic losses ... more Tropical cyclones take precious lives, damage critical infrastructure, and cause economic losses worth billions of dollars in Australia. To reduce the detrimental effect of cyclones, a comprehensive understanding of cyclones using artificial intelligence (AI) is crucial. Although event records on Australian tropical cyclones have been documented over the last 4 decades, deep learning studies on these events have not been reported. This paper presents automated AI-based regression, anomaly detection, and clustering techniques on the largest available cyclone repository covering 28,713 records with almost 80 cyclone-related parameters from 17 January 1907 to 11 May 2022. Experimentation with both linear and logistic regression on this dataset resulted in 33 critical insights on factors influencing the central pressure of cyclones. Moreover, automated clustering determined four different clusters highlighting the conditions for low central pressure. Anomaly detection at 70% sensitivity...
Social media platforms such as Twitter have been used by political leaders, heads of states, poli... more Social media platforms such as Twitter have been used by political leaders, heads of states, political parties, and their supporters to strategically influence public opinions. Leaders can post about a location, a state, a country, or even a region in their social media accounts, and the posts can immediately be viewed and reacted to by millions of their followers. The effect of social media posts by political leaders could be automatically measured by extracting, analyzing, and producing real-time geospatial intelligence for social scientists and researchers. This paper proposed a novel approach in automatically processing real-time social media messages of political leaders with artificial intelligence (AI)-based language detection, translation, sentiment analysis, and named entity recognition (NER). This method automatically generates geospatial and location intelligence on both ESRI ArcGIS Maps and Microsoft Bing Maps. The proposed system was deployed from 1 January 2020 to 6 Fe...
Tropical cyclones devastate large areas, take numerous lives and damage extensive property in Ban... more Tropical cyclones devastate large areas, take numerous lives and damage extensive property in Bangladesh. Research on landfalling tropical cyclones affecting Bangladesh has primarily focused on events occurring since AD1960 with limited work examining earlier historical records. We rectify this gap by developing a new tornado catalogue that include present and past records of tornados across Bangladesh maximizing use of available sources. Within this new tornado database, 119 records were captured starting from 1838 till 2020 causing 8,735 deaths and 97,868 injuries leaving more than 1,02,776 people affected in total. Moreover, using this new tornado data, we developed an end-to-end system that allows a user to explore and analyze the full range of tornado data on multiple scenarios. The user of this new system can select a date range or search a particular location, and then, all the tornado information along with Artificial Intelligence (AI) based insights within that selected sco...
In this review, we have summarized the influence of inflammation-related pathological mechanisms ... more In this review, we have summarized the influence of inflammation-related pathological mechanisms on the development of ulcerative colitis (UC) to colorectal cancer (CRC). Background: UC is a chronic inflammatory bowel disease (IBD) of unknown etiology that affects the colon and rectum. Long-term inflammation of UC may greatly increase the risk of CRC, and the secretion of inflammatory factors and sustained inflammation may be key drivers of UC-associated CRC progression. Compared with the general population, the risk of CRC in patients with UC is 2.4 times higher, and the mortality rate of patients with UC is higher than that of those with sporadic CRC. The use of non-steroidal anti-inflammatory drugs can reduce the probability of UC transforming into CRC. Methods: Literatures about inflammation and UC were extensively reviewed to analyze and discuss. Conclusions: We believe that the mechanism of continuous inflammation that promotes cancer in UC may be the result of the mutual influence of intestinal microbes, inflammatory signals, and tissue remodeling. The invasion of intestinal microorganisms activates inflammatory signals and promotes the secretion of inflammatory factors which intensifies the remodeling of the extracellular matrix (ECM) and recruits immune cells. Eventually, a mutually engendering circuit of microbial invasion, release of inflammatory mediators, and remodeling of ECM is formed, which triggers continuous inflammation and promotes development of CRC.
The coronavirus disease (COVID-19) pandemic has affected the lives of social media users in an un... more The coronavirus disease (COVID-19) pandemic has affected the lives of social media users in an unprecedented manner. They are constantly posting their satisfaction or dissatisfaction over the COVID-19 situation at their location of interest. Therefore, understanding location-oriented sentiments about this situation is of prime importance for political leaders, and strategic decision-makers. To this end, we present a new fully automated algorithm based on artificial intelligence (AI), for extraction of location-oriented public sentiments on the COVID-19 situation. We designed the proposed system to obtain exhaustive knowledge and insights on social media feeds related to COVID-19 in 110 languages through AI-based translation, sentiment analysis, location entity detection, and decomposition tree analysis. We deployed fully automated algorithm on live Twitter feed from July 15, 2021 and it is still running as of 12 January, 2022. The system was evaluated on a limited dataset between July 15, 2021 to August 10, 2021. During this evaluation timeframe 150,000 tweets were analyzed and our algorithm found that 9,900 tweets contained one or more location entities. In total, 13,220 location entities were detected during the evaluation period, and the rates of average precision and recall rate were 0.901 and 0.967, respectively. As of 12 January, 2022, the proposed solution has detected 43,169 locations using entity recognition. According to the best of our knowledge, this study is the first to report location intelligence with entity detection, sentiment analysis, and decomposition tree analysis on social media messages related to COVID-19 and has covered the largest set of languages.
Understanding the complex dynamics of landslides is crucial for disaster planners to make timely ... more Understanding the complex dynamics of landslides is crucial for disaster planners to make timely and effective decision that saves lives and reduces the economic impact on society. Using the landslide inventory of Chittagong Metropolitan Area (CMA), we created a new Artificial Intelligence (AI) based insight system for the town planners and senior disaster recovery strategists of Chittagong, Bangladesh. Our system generates dynamic AI-based insights for a range of complex scenarios created from 7 different landslide feature attributes. The users of our system can select a particular kind of scenario out of the exhaustive list of 1.054X10 41 possible scenario sets and our AI-based system will immediately predict how many casualties are likely to occur based on the selected kind of scenario. Moreover, an AI-based system shows how landslide attributes (e.g., rainfall, area of mass, elevation, etc.) correlate with landslide casualty by drawing detailed trend lines performing both linear and logistic regressions. According to literature and the best of our knowledge, our CMA scenariobased AI insight system is the first of its kind providing the most comprehensive understanding of landslide scenarios and associated deaths and damages in CMA. The system was deployed on a wide range of platforms including Android, iOS, and Windows systems so that it could be easily adapted to strategic disaster planners. The deployed solutions were handed down to 12 landslide strategists and disaster planners for evaluations whereby 91.67% of users found the solution easy to use, effective and self-explanatory while using via mobile. 1 Introduction Landslides are natural phenomenon that have an adverse effect on human life as well as economy (Rabby and Li, 2020). For the purpose of reducing the negative impact of landslide and to have an increased level of disaster preparedness (Alam, 2020), it is crucial to have a multi-dimensional understanding landslide attributes. The complex nature of landslide dynamics makes it extremely difficult to understand the impact of a particular type of landslide. Bangladesh is susceptible to a variety of natural and human-induced hazards including tropical cyclones, floods, droughts, earthquakes, tsunamis, and landslides (Alam, 2020). Particularly, landslides become recurrent phenomena in the Southeast Bangladesh in recent decades. Therefore, the Government of Bangladesh (GoB) and its coastal residents have been in reducing resultant deaths from tropical cyclones but
Utilizing social media data is imperative in comprehending critical insights on the Russia–Ukrain... more Utilizing social media data is imperative in comprehending critical insights on the Russia–Ukraine cyber conflict due to their unparalleled capacity to provide real-time information dissemination, thereby enabling the timely tracking and analysis of cyber incidents. The vast array of user-generated content on these platforms, ranging from eyewitness accounts to multimedia evidence, serves as invaluable resources for corroborating and contextualizing cyber attacks, facilitating the attribution of malicious actors. Furthermore, social media data afford unique access to public sentiment, the propagation of propaganda, and emerging narratives, offering profound insights into the effectiveness of information operations and shaping counter-messaging strategies. However, there have been hardly any studies reported on the Russia–Ukraine cyber war harnessing social media analytics. This paper presents a comprehensive analysis of the crucial role of social-media-based cyber intelligence in un...
The surge in cybercrime has emerged as a pressing concern in contemporary society due to its far-... more The surge in cybercrime has emerged as a pressing concern in contemporary society due to its far-reaching financial, social, and psychological repercussions on individuals. Beyond inflicting monetary losses, cyber-attacks exert adverse effects on the social fabric and psychological well-being of the affected individuals. In order to mitigate the deleterious consequences of cyber threats, adoption of an intelligent agent-based solution to enhance the speed and comprehensiveness of cyber intelligence is advocated. In this paper, a novel cyber intelligence solution is proposed, employing four semantic agents that interact autonomously to acquire crucial cyber intelligence pertaining to any given country. The solution leverages a combination of techniques, including a convolutional neural network (CNN), sentiment analysis, exponential smoothing, latent Dirichlet allocation (LDA), term frequency-inverse document frequency (TF-IDF), Porter stemming, and others, to analyse data from both s...
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Understanding the complex dynamics of landslides is crucial for disaster planners to make timely ... more Understanding the complex dynamics of landslides is crucial for disaster planners to make timely and effective decisions that save lives and reduce the economic impact on society. Using the landslide inventory of the Chittagong Metropolitan Area (CMA), we have created a new artificial intelligence (AI)-based insight system for the town planners and senior disaster recovery strategists of Chittagong, Bangladesh. Our system generates dynamic AI-based insights for a range of complex scenarios created from 7 different landslide feature attributes. The users of our system can select a particular kind of scenario out of the exhaustive list of 1.054 × 1041 possible scenario sets, and our AI-based system will immediately predict how many casualties are likely to occur based on the selected kind of scenario. Moreover, an AI-based system shows how landslide attributes (e.g., rainfall, area of mass, elevation, etc.) correlate with landslide casualty by drawing detailed trend lines by performin...
Negative events are prevalent all over the globe round the clock. People demonstrate psychologica... more Negative events are prevalent all over the globe round the clock. People demonstrate psychological affinity to negative events, and they incline to stay away from troubled locations. This paper proposes an automated geospatial imagery application that would allow a user to remotely extract knowledge of troubled locations. The autonomous application uses thousands of connected news sensors to obtain real-time news pertaining to all global troubles. From the captured news, the proposed application uses artificial intelligence-based services and algorithms like sentiment analysis, entity detection, geolocation decoder, news fidelity analysis, and decomposition tree analysis to reconstruct global threat maps representing troubled locations interactively. The fully deployed system was evaluated for full three months of summer 2021, during which the autonomous system processed above 22 k news from 2397 connected news sources involving BBC, CNN, NY Times, Government websites of 192 countri...
Tropical cyclones take precious lives, damage critical infrastructure, and cause economic losses ... more Tropical cyclones take precious lives, damage critical infrastructure, and cause economic losses worth billions of dollars in Australia. To reduce the detrimental effect of cyclones, a comprehensive understanding of cyclones using artificial intelligence (AI) is crucial. Although event records on Australian tropical cyclones have been documented over the last 4 decades, deep learning studies on these events have not been reported. This paper presents automated AI-based regression, anomaly detection, and clustering techniques on the largest available cyclone repository covering 28,713 records with almost 80 cyclone-related parameters from 17 January 1907 to 11 May 2022. Experimentation with both linear and logistic regression on this dataset resulted in 33 critical insights on factors influencing the central pressure of cyclones. Moreover, automated clustering determined four different clusters highlighting the conditions for low central pressure. Anomaly detection at 70% sensitivity...
Social media platforms such as Twitter have been used by political leaders, heads of states, poli... more Social media platforms such as Twitter have been used by political leaders, heads of states, political parties, and their supporters to strategically influence public opinions. Leaders can post about a location, a state, a country, or even a region in their social media accounts, and the posts can immediately be viewed and reacted to by millions of their followers. The effect of social media posts by political leaders could be automatically measured by extracting, analyzing, and producing real-time geospatial intelligence for social scientists and researchers. This paper proposed a novel approach in automatically processing real-time social media messages of political leaders with artificial intelligence (AI)-based language detection, translation, sentiment analysis, and named entity recognition (NER). This method automatically generates geospatial and location intelligence on both ESRI ArcGIS Maps and Microsoft Bing Maps. The proposed system was deployed from 1 January 2020 to 6 Fe...
Tropical cyclones devastate large areas, take numerous lives and damage extensive property in Ban... more Tropical cyclones devastate large areas, take numerous lives and damage extensive property in Bangladesh. Research on landfalling tropical cyclones affecting Bangladesh has primarily focused on events occurring since AD1960 with limited work examining earlier historical records. We rectify this gap by developing a new tornado catalogue that include present and past records of tornados across Bangladesh maximizing use of available sources. Within this new tornado database, 119 records were captured starting from 1838 till 2020 causing 8,735 deaths and 97,868 injuries leaving more than 1,02,776 people affected in total. Moreover, using this new tornado data, we developed an end-to-end system that allows a user to explore and analyze the full range of tornado data on multiple scenarios. The user of this new system can select a date range or search a particular location, and then, all the tornado information along with Artificial Intelligence (AI) based insights within that selected sco...
In this review, we have summarized the influence of inflammation-related pathological mechanisms ... more In this review, we have summarized the influence of inflammation-related pathological mechanisms on the development of ulcerative colitis (UC) to colorectal cancer (CRC). Background: UC is a chronic inflammatory bowel disease (IBD) of unknown etiology that affects the colon and rectum. Long-term inflammation of UC may greatly increase the risk of CRC, and the secretion of inflammatory factors and sustained inflammation may be key drivers of UC-associated CRC progression. Compared with the general population, the risk of CRC in patients with UC is 2.4 times higher, and the mortality rate of patients with UC is higher than that of those with sporadic CRC. The use of non-steroidal anti-inflammatory drugs can reduce the probability of UC transforming into CRC. Methods: Literatures about inflammation and UC were extensively reviewed to analyze and discuss. Conclusions: We believe that the mechanism of continuous inflammation that promotes cancer in UC may be the result of the mutual influence of intestinal microbes, inflammatory signals, and tissue remodeling. The invasion of intestinal microorganisms activates inflammatory signals and promotes the secretion of inflammatory factors which intensifies the remodeling of the extracellular matrix (ECM) and recruits immune cells. Eventually, a mutually engendering circuit of microbial invasion, release of inflammatory mediators, and remodeling of ECM is formed, which triggers continuous inflammation and promotes development of CRC.
The coronavirus disease (COVID-19) pandemic has affected the lives of social media users in an un... more The coronavirus disease (COVID-19) pandemic has affected the lives of social media users in an unprecedented manner. They are constantly posting their satisfaction or dissatisfaction over the COVID-19 situation at their location of interest. Therefore, understanding location-oriented sentiments about this situation is of prime importance for political leaders, and strategic decision-makers. To this end, we present a new fully automated algorithm based on artificial intelligence (AI), for extraction of location-oriented public sentiments on the COVID-19 situation. We designed the proposed system to obtain exhaustive knowledge and insights on social media feeds related to COVID-19 in 110 languages through AI-based translation, sentiment analysis, location entity detection, and decomposition tree analysis. We deployed fully automated algorithm on live Twitter feed from July 15, 2021 and it is still running as of 12 January, 2022. The system was evaluated on a limited dataset between July 15, 2021 to August 10, 2021. During this evaluation timeframe 150,000 tweets were analyzed and our algorithm found that 9,900 tweets contained one or more location entities. In total, 13,220 location entities were detected during the evaluation period, and the rates of average precision and recall rate were 0.901 and 0.967, respectively. As of 12 January, 2022, the proposed solution has detected 43,169 locations using entity recognition. According to the best of our knowledge, this study is the first to report location intelligence with entity detection, sentiment analysis, and decomposition tree analysis on social media messages related to COVID-19 and has covered the largest set of languages.
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