Emotion Analysis on EEG Signal Using Machine Learning and Neural Network

Emotion has a significant influence on how one thinks and interacts with others. It serves as a link between how a person feels and the actions one takes, or it could be said that it influences one's life decisions on occasion. Since the patterns of emotions and their reflections vary from person to person, their inquiry must be based on approaches that are effective over a wide range of population regions. To extract features and enhance accuracy, emotion recognition using brain waves or EEG signals requires the implementation of efficient signal processing techniques. Various approaches to human-machine interaction technologies have been ongoing for a long time, and in recent years, researchers have had great success in automatically understanding emotion using brain signals. In our research, several emotional states were classified and tested on EEG signals collected from a well-known publicly available dataset, the DEAP Dataset, using SVM (Support Vector Machine), KNN (K-Nearest Neighbor), and an advanced neural network model, RNN (Recurrent Neural Network), trained with LSTM (Long Short Term Memory). The main purpose of this study is to improve ways to improve emotion recognition performance using brain signals. Emotions, on the other hand, can change with time. As a result, the changes in emotion over time are also examined in our research

The Northern Corridor, Food Insecurity and the Resource Curse for Indigenous Communities in Canada

Food insecurity rates for Canada’s Indigenous people are the worst among developed nations, demanding immediate action to prevent an impending health crisis. Food insecurity in Canada is widespread across most First Nations households (51 per cent).The highest food insecurity rates are experienced by the Inuit in Nunavut (63 per cent), First Nations without access roads (65 per cent), and Alberta First Nations (60 per cent). Indigenous peoples’ food insecurity is associated with a shorter life expectancy andhigher rates of physical and mental illnesses, including four times the diabetes incidence of Canada’s non-Indigenous populations. This paper analyzes the impact on food insecurity of a notional trade northern corridor to reach local and global markets, considering case studies of resource and utility corridors. This research found that, rather than improving food security and providing benefits, trade corridors typically bring a resource curse to Indigenous communities. Also called the ‘paradox of plenty,’ a resource curse occurs when Indigenous communities, particularly First Nation reserves, experience mainly negative economic impacts when their resources are extracted. A resource curse on Indigenous communities is apparent across Canada, including at Norman Wells in the Northwest Territories and Shoal Lake 40 in Ontario, where oil and water pipelines have resulted in negative environmental, health and socio-cultural impacts without providing permanent road access or long-term jobs, and without reducing high food prices. Also, the resource curse is evident for Alberta’s First Nations, which have the highest food insecurity rate of the country’s First Nations, despite being covered in pipelines and extractive industries. To explore the food security impacts of the notional northern corridor, we spatially analyzed its route’s proximity to mineral-rich greenstone belts, roads, and Indigenous communities without all-weather road access. The notional northern corridor route transects many rich mineral deposits to reveal a focus on resource extraction. This notional route appears to prioritize the transport of resources to global markets over Indigenous communities’ needs. The notional route has six ports traversing First Nation territories under the Indian Act but is nearby to only seven of the 122 Indigenous communities lacking road access. This notional route, thus, is linked to Indigenous-specific systemic racist legislation of the Indian Act to bypass Inuit lands in Nunavut, Quebec and Labrador, where communities all lack roads but do not fall under the Indian Act. The Crown’s Indian Act trusteeship over First Nations gives a legal right to usurp reserve or Crown land for any corridor or development. The Indian Act benefits industry, settler and state to access and own Native land and resources, but not First Nations except regarding sustenance activities. The Federal Crown’s trusteeship over First Nations’ land and resources makes First Nations’ people legal “wards of the state,” which has led to inequitable planning control, infrastructure and services. Signs of economic poverty are that most Indigenous communities lack food infrastructure, hospitals, and post-secondary education facilities, with 122 First Nation communities lacking all-season access roads. This inequity indicts Canada for colonialism, racism and failure to uphold the equality clause in its constitution. The notional route to Hudson Bay indicates that First Nations and food security were not fully included in the conception phase of the Northern Corridor. The notional route cuts through the Indigenous-led protected area proposed in the Seal River Watershed to reach Hudson Bay via Churchill rather than Port Nelson. This notional route would undermine the Indigenous-led protected area and the migration of the threatened Caribou population. Oppositely, the NeeStaNan corridor proposed by Fox Lake, York Factory and other First Nations goes to Port Nelson and avoids the Seal River Watershed. Free, prior and informed consent should start at the conception phase to include Indigenous interests. In Northern Canada, where Indigenous people comprise the vast majority, infrastructure development should be Indigenous-led to prioritize Indigenous food security. An Indigenous-led, adequately funded strategy to end food insecurity in Canada’s Indigenous communities within the next decade is needed to turn around a health and human rights crisis. Removing Indigenous-specific systemic racist barriers to Indigenous control over Native land and adequate funding for infrastructure and services will attain Indigenous food security within a decade

Short-chain lipid peroxidation products form covalent adducts with pyruvate kinase and inhibit its activity in vitro and in breast cancer cells

Pyruvate kinase catalyses the last step in glycolysis and has been suggested to contribute to the regulation of aerobic glycolysis in cancer cells. It can be inhibited by oxidation of cysteine residues in vitro and in vivo, which is relevant to the more pro-oxidant state in cancer and proliferating tissues. These conditions also favour lipid peroxidation and the formation of electrophilic fragmentation products, including short-chain aldehydes that can covalently modify proteins. However, as yet few studies have investigated their interactions with pyruvate kinase, so we investigated the effects of three different aldehydes, acrolein, malondialdehyde and 4-hydroxy-2(E)-hexenal (HHE), on the structure and activity of the enzyme. Analysis by LC-MS/MS showed unique modification profiles for each aldehyde, but Cys152, Cys423 and Cys474 were the residues most susceptible to electrophilic modification. Analysis of enzymatic activity under these conditions showed that acrolein was the strongest inhibitor, and at incubation times longer than 2 h, pathophysiological concentrations induced significant effects. Treatment of MCF-7 cells with the aldehydes caused similar losses of pyruvate kinase activity to those observed in vitro, and at lower concentrations than those required to cause cell death, with time and dose-dependent effects; acrolein adducts on Cys152 and Cys358 were detected. Cys358 and Cys474 are located at or near the allosteric or active sites, and formation of adducts on these residues probably contributes to loss of activity at low treatment concentrations. This study provides the first detailed analysis of the structure-activity relationship of C3 and C6 aldehydes with pyruvate kinase, and suggests that reactive short-chain aldehydes generated in diseases with an oxidative aetiology or from environmental exposure such as smoking could be involved in the metabolic alterations observed in cancer cells, through alteration of pyruvate kinase activity

Trust issues that create threats for cyber attacks in cloud computing

The research contribution in this paper is twofold. First, an investigative survey on cloud computing is conducted with the main focus on gaps that is slowing down cloud adoption as well as reviewing the threat remediation challenges. Next, some thoughts are constructed on novel approaches to address some of the widely discussed attack types using machine learning techniques. Such thoughts captured through a series of experiments are expected to give researchers, cloud providers and their customers’ additional insight and tools to proactively protect themselves from known or perhaps even unknown security issues that follow the same patterns

Classifying different denial-of-service attacks in cloud computing using rule-based learning

From traditional networking to cloud computing, one of the essential but formidable tasks is to detect cyber attacks and their types. A cloud provider’s unwillingness to share security-related data with its clients adds to the difficulty of detection by a cloud customer. The research contributions in this paper are twofold. First, an investigative survey on cloud computing is conducted with the main focus on gaps that is hindering cloud adoption, accompanied by a review of the threat remediation challenges. Second, some thoughts are constructed on novel approaches to address some of the widely discussed denial-of-service (DoS) attack types by using machine learning techniques. We evaluate the techniques’ performances by using statistical ranking-based methods, and find the rule-based learning technique C4.5, from a set of popular learning algorithms, as an efficient tool to classify various DoS attacks in the cloud platform. The novelty of our rather rigorous analysis is in its ability to identify insider’s activities and other DoS attacks by using performance data. The reason for using performance data ratherthan traditional logs and security-related data is that the performance data can be collected by the customers themselves without any help from cloud providers. To the best of our knowledge, no one has made such attempts before. Our findings and thoughts captured through a series of experiments in our constructed cloud server are expected to give researchers, cloud providers and customers additional insight and tools to proactively protect themselves from known or perhaps even unknown security issues that have similar patterns

A survey on gaps, threat remediation challenges and some thoughts for proactive attack detection in cloud computing

The long-term potential benefits through reduction of cost of services and improvement of business outcomes make Cloud Computing an attractive proposition these days. To make it more marketable in the wider IT user community one needs to address a variety of information security risks. In this paper, we present an extensive review on cloud computing with the main focus on gaps and security concerns. We identify the top security threats and their existing solutions. We also investigate the challenges/obstacles in implementing threat remediation. To address these issues, we propose a proactive threat detection model by adopting three main goals: (i) detect an attack when it happens, (ii) alert related parties (system admin, data owner) about the attack type and take combating action, and (iii) generate information on the type of attack by analyzing the pattern (even if the cloud provider attempts subreption). To emphasize the importance of monitoring cyber attacks we provide a brief overview of existing literature on cloud computing security. Then we generate some real cyber attacks that can be detected from performance data in a hypervisor and its guest operating systems. We employ modern machine learning techniques as the core of our model and accumulate a large database by considering the top threats. A variety of model performance measurement tools are applied to verify the model attack prediction capability. We observed that the Support Vector Machine technique from statistical machine learning theory is able to identify the top attacks with an accuracy of 97.13%. We have detected the activities using performance data (CPU, disk, network and memory performance) from the hypervisor and its guest operating systems, which can be generated by any cloud customer using built-in or third party software. Thus, one does not have to depend on cloud providers' security logs and data. We believe our line of thoughts comprising a series of experiments will give researchers, cloud providers and their customers a useful guide to proactively protect themselves from known or even unknown security issues that follow the same patterns

Climate change, migration and human rights in Bangladesh: Perspectives on governance

Bangladesh experiences some of the most severe impacts of climate change, with impacts already evident in the coastal regions. Recent data shows that around 32% of the coastal communities in Bangladesh are affected by climate‐induced hazards each year. In 2011, 64% among them were displaced locally and 27% were displaced to other locations in Bangladesh. It requires comprehensive and viable polices and planning to meet the challenges of managing a large number of displaced people. In this context, this paper reviews and investigates the effectiveness of current governance frameworks to address migration of affected communities. It argues that migration can be an effective way to cope with environmental shocks. Finally, it discusses policy imperatives for effective protection of people displaced by climate risks with a special reference to adopting a human rights‐based approach in law and policy making for climate‐induced migration

Effect of Discharge and Upstream Jam Angle on the Flow Distribution beneath a Simulated Ice Jam

The velocity field beneath simulated rough ice jams under various upstream jam angles and discharge were investigated using a particle image velocimetry system. Three discharges were examined at 2.3 L/s, 3.4 L/s, and 4.0 L/s and two upstream ice jam angles were tested at 4° and 6°. Increasing the discharge resulted in high turbulence production beneath the jam. The adverse pressure gradient exerted on the flow increased the levels of the Reynolds shear stress. The measured velocities beneath the jam were used to assess the performances of three traditional field measurement techniques as well as the validity of the two-parameter power law. The two-point measurement technique performed remarkably well with the least mean bias error of 2.0%. The error associated with the different techniques showed their inability to accurately predict the average velocity under high discharge. The two-parameter power law accurately predicted velocity profiles within the equilibrium section of the jam, but failed within the boundary layers when the flow was subjected to a pressure gradient.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Determining Unintentional Island Threshold to Enhance the Reliability in an Electrical Distribution Grid

Due to the significant number of distributed generators in the electric power system, islanding detection requirements are becoming an increasingly prominent aspect of the power system. The island detection system depends on accurate threshold determination since an incorrect threshold might result in a hazardous situation. To evaluate the proposed method’s capacity to discriminate between different events, this study examined different unintentional islanding conditions such as under frequency and over frequency. The purpose of this study is to establish the threshold of the under and over frequency island conditions. The under frequency island condition happens when the distributed generator (DG) capacity exceeds the amount of connected load; on the other hand, the over frequency island condition happens during a higher connected load compared to the capacity of the DG. The contribution of this research is to propose an unintentional island threshold setting technique based on bus voltage angle difference data of the phasor measurement unit (PMU). In the PowerWorld simulator, the Utility Kerteh (location: Terengganu, Malaysia) bus system has been designed and simulated in this work. The test system has four distinct islanding scenarios under two conditions, and the performance of the proposed methods demonstrates that for the under frequency islanding conditions the scenario’s threshold can be taken at a minimum of 40 milliseconds (ms) and a maximum of 60 ms, while the over frequency condition island threshold can be placed at a minimum of 60 ms and a maximum of 80 ms depending on the scenarios. Therefore, the proposed technique will be contributed to increase the reliability of the overall distribution grid so the unintentional island can be detected faster in terms of time

Combating Cyber Attacks in Cloud Systems Using Machine Learning

One of the crucial but complicated task is to detect cyber attacks and their types in any IT networking environment including recent consumption of cloud services. The common practice of existing cloud provider’s is that they are not transparent when it comes to share security related data with its consumers adds to the difficulty of detection by a cloud customer. Contributions of this chapter are segregated into two categories. First, we will demonstrate an easy technique on how cloud customers can collect performance data of their Virtual Machine (VM). Second, some thoughts are constructed on novel approaches to classify some of the widely discussed cyber attack types using machine learning techniques. We will evaluate the techniques’ performances using statistical ranking based methods. The novelty of our rather rigorous analysis is in its ability to identify insider's activities and other cyber attacks using performance data. The reason for using performance data rather than traditional logs and security related data is that the performance data can be collected by the customers themselves without any assistance from the cloud providers. Therefore the aim of these series of experiments in our constructed cloud computing model are expected to give researchers, cloud providers and consumers additional insight and tools to proactively protect their data from known, or perhaps even unknown, security issues that have similar patterns