Depression Intensity Classification from Tweets Using Fast Text Based Weighted Soft Voting Ensemble

Predicting depression intensity from microblogs and social media posts has numerous benefits and applications,including predicting early psychological disorders and stress in individuals or the general public.A major challenge in predicting depression using social media posts is that the existing studies do not focus on predicting the intensity of depression in social media texts but rather only perform the binary classification of depression and moreover noisy data makes it difficult to predict the true depression in the social media text.This study intends to begin by collecting relevant Tweets and generating a corpus of 210000 public tweets using Twitter public application programming interfaces(APIs).A strategy is devised to filter out only depression-related tweets by creating a list of relevant hashtags to reduce noise in the corpus.Furthermore,an algorithm is developed to annotate the data into three depression classes:‘Mild,’‘Moderate,’and‘Severe,’based on International Classification of Diseases-10(ICD-10)depression diagnostic criteria.Different baseline classifiers are applied to the annotated dataset to get a preliminary idea of classification performance on the corpus.Further FastText-based model is applied and fine-tuned with different preprocessing techniques and hyperparameter tuning to produce the tuned model,which significantly increases the depression classification performance to an 84%F1 score and 90%accuracy compared to baselines.Finally,a FastText-based weighted soft voting ensemble(WSVE)is proposed to boost the model’s performance by combining several other classifiers and assigning weights to individual models according to their individual performances.The proposed WSVE outperformed all baselines as well as FastText alone,with an F1 of 89%,5%higher than FastText alone,and an accuracy of 93%,3%higher than FastText alone.The proposed model better captures the contextual features of the relatively small sample class and aids in the detection of early depression intensity prediction from tweets with impactful performances.

Intelligent Breast Cancer Prediction Empowered with Fusion and Deep Learning

Breast cancer is the most frequently detected tumor that eventually could result in a significant increase in female mortality globally.According to clinical statistics,one woman out of eight is under the threat of breast cancer.Lifestyle and inheritance patterns may be a reason behind its spread among women.However,some preventive measures,such as tests and periodic clinical checks can mitigate its risk thereby,improving its survival chances substantially.Early diagnosis and initial stage treatment can help increase the survival rate.For that purpose,pathologists can gather support from nondestructive and efficient computer-aided diagnosis(CAD)systems.This study explores the breast cancer CAD method relying on multimodal medical imaging and decision-based fusion.In multimodal medical imaging fusion,a deep learning approach is applied,obtaining 97.5%accuracy with a 2.5%miss rate for breast cancer prediction.A deep extreme learning machine technique applied on feature-based data provided a 97.41%accuracy.Finally,decisionbased fusion applied to both breast cancer prediction models to diagnose its stages,resulted in an overall accuracy of 97.97%.The proposed system model provides more accurate results compared with other state-of-the-art approaches,rapidly diagnosing breast cancer to decrease its mortality rate.

Fusion-Based Machine Learning Architecture for Heart Disease Prediction

The contemporary evolution in healthcare technologies plays a considerable and signicant role to improve medical services and save human lives.Heart disease or cardiovascular disease is the most fatal and complex disease which it is hardly to be detected through our naked eyes,as numerous people have been suffering from this disease globally.Heart attacks occur when the ranges of vital signs such as blood pressure,pulse rate,and body temperature exceed their normal values.The efcient diagnosis of heart diseases could play a substantial role in the eld of cardiology,while diagnostic time could be reduced.It has been a key challenge for researchers and medical experts to diagnose heart diseases accurately and timely.Therefore,machine learning-based techniques are used for the diagnosis with higher accuracy,using datasets compiled from former medical patients’reports.In recent years,numerous studies have been presented in the literature propose machine learning techniques for diagnosing heart diseases.However,the existing techniques have some limitations in terms of their accuracy.In this paper,a novel Support Vector Machine(SVM)based architecture for heart disease prediction,empowered with a fuzzy based decision level fusion,is presented.The SVMbased architecture has improved the accuracy signicantly as compared to existing solutions,where 96.23%accuracy has been achieved.

Coronavirus: A “Mild” Virus Turned Deadly Infection

Coronaviruses are a family of viruses that can be transmitted from one person to another.Earlier strains have only been mild viruses,but the current form,known as coronavirus disease 2019(COVID-19),has become a deadly infection.The outbreak originated in Wuhan,China,and has since spread worldwide.The symptoms of COVID-19 include a dry cough,sore throat,fever,and nasal congestion.Antimicrobial drugs,pathogen–host interaction,and 2 weeks of isolation have been recommended for the treatment of the infection.Safe operating procedures,such as the use of face masks,hand sanitizer,handwashing with soap,and social distancing,are also suggested.Moreover,travel bans for cities,states,and countries have been put in place,along with lockdowns to control the outbreak.Travel restrictions,mask use,sanitizer or soap use,and avoidance of touching the face and nose have produced encouraging results,whereas the effectiveness of antibiotics has not been proved.The results of isolation for the recovery of infected people have also been promising.Travel bans and lockdowns have caused a slump in economies,and unemployment has risen sharply,resulting in an increase in mental health cases globally.To date,vaccines have been developed and are in use in certain countries,but following standard operating procedures remain critical.The countries following the guidelines can eradicate this virus.New Zealand was the rst country to eliminate the virus from their territory.

Modelling and Simulation of COVID-19 Outbreak Prediction Using Supervised Machine Learning

Novel Coronavirus-19(COVID-19)is a newer type of coronavirus that has not been formally detected in humans.It is established that this disease often affects people of different age groups,particularly those with body disorders,blood pressure,diabetes,heart problems,or weakened immune systems.The epidemic of this infection has recently had a huge impact on people around the globe with rising mortality rates.Rising levels of mortality are attributed to their transmitting behavior through physical contact between humans.It is extremely necessary to monitor the transmission of the infection and also to anticipate the early stages of the disease in such a way that the appropriate timing of effective precautionary measures can be taken.The latest global coronavirus epidemic(COVID-19)has brought new challenges to the scientific community.Artificial Intelligence(AI)-motivated methodologies may be useful in predicting the conditions,consequences,and implications of such an outbreak.These forecasts may help to monitor and prevent the spread of these outbreaks.This article proposes a predictive framework incorporating Support Vector Machines(SVM)in the forecasting of a potential outbreak of COVID-19.The findings indicate that the suggested system outperforms cutting-edge approaches.The method could be used to predict the long-term spread of such an outbreak so that we can implement proactive measures in advance.The findings of the analyses indicate that the SVM forecasting framework outperformed the Neural Network methods in terms of accuracy and computational complexity.The proposed SVM system model exhibits 98.88%and 96.79%result in terms of accuracy during training and validation respectively.

Intelligent Cloud Based Load Balancing System Empowered with Fuzzy Logic

Cloud computing is seeking attention as a new computing paradigm to handle operations more efficiently and cost-effectively.Cloud computing uses dynamic resource provisioning and de-provisioning in a virtualized environment.The load on the cloud data centers is growing day by day due to the rapid growth in cloud computing demand.Elasticity in cloud computing is one of the fundamental properties,and elastic load balancing automatically distributes incoming load to multiple virtual machines.This work is aimed to introduce efficient resource provisioning and de-provisioning for better load balancing.In this article,a model is proposed in which the fuzzy logic approach is used for load balancing to avoid underload and overload of resources.A Simulator in Matlab is used to test the effectiveness and correctness of the proposed model.The simulation results have shown that our proposed intelligent cloud-based load balancing system empowered with fuzzy logic is better than previously published approaches.

Enabling Smart Cities with Cognition Based Intelligent Route Decision in Vehicles Empowered with Deep Extreme Learning Machine

The fast-paced growth of artificial intelligence provides unparalleled opportunities to improve the efficiency of various industries,including the transportation sector.The worldwide transport departments face many obstacles following the implementation and integration of different vehicle features.One of these tasks is to ensure that vehicles are autonomous,intelligent and able to grow their repository of information.Machine learning has recently been implemented in wireless networks,as a major artificial intelligence branch,to solve historically challenging problems through a data-driven approach.In this article,we discuss recent progress of applying machine learning into vehicle networks for intelligent route decision and try to focus on this emerging field.Deep Extreme Learning Machine(DELM)framework is introduced in this article to be incorporated in vehicles so they can take human-like assessments.The present GPS compatibility issues make it difficult for vehicles to take real-time decisions under certain conditions.It leads to the concept of vehicle controller making self-decisions.The proposed DELM based system for self-intelligent vehicle decision makes use of the cognitive memory to store route observations.This overcomes inadequacy of the current in-vehicle route-finding technology and its support.All the relevant route-related information for the ride will be provided to the user based on its availability.Using the DELM method,a high degree of precision in smart decision taking with a minimal error rate is obtained.During investigation,it has been observed that proposed framework has the highest accuracy rate with 70%of training(1435 samples)and 30%of validation(612 samples).Simulation results validate the intelligent prediction of the proposed method with 98.88%,98.2%accuracy during training and validation respectively.

Improving Queuing System Throughput Using Distributed Mean Value Analysis to Control Network Congestion

In this paper, we have used the distributed mean value analysis (DMVA) technique with the help of random observe property (ROP) and palm probabilities to improve the network queuing system throughput. In such networks, where finding the complete communication path from source to destination, especially when these nodes are not in the same region while sending data between two nodes. So, an algorithm is developed for single and multi-server centers which give more interesting and successful results. The network is designed by a closed queuing network model and we will use mean value analysis to determine the network throughput (b) for its different values. For certain chosen values of parameters involved in this model, we found that the maximum network throughput for β≥0.7?remains consistent in a single server case, while in multi-server case for β≥ 0.5?throughput surpass the Marko chain queuing system.