Cyberbullying is a form of harassment or bullying that takes place online or through digital devices like smartphones,computers,or tablets.It can occur through various channels,such as social media,text messages,onlin...Cyberbullying is a form of harassment or bullying that takes place online or through digital devices like smartphones,computers,or tablets.It can occur through various channels,such as social media,text messages,online forums,or gaming platforms.Cyberbullying involves using technology to intentionally harm,harass,or intimidate others and may take different forms,including exclusion,doxing,impersonation,harassment,and cyberstalking.Unfortunately,due to the rapid growth of malicious internet users,this social phenomenon is becoming more frequent,and there is a huge need to address this issue.Therefore,the main goal of the research proposed in this manuscript is to tackle this emerging challenge.A dataset of sexist harassment on Twitter,containing tweets about the harassment of people on a sexual basis,for natural language processing(NLP),is used for this purpose.Two algorithms are used to transform the text into a meaningful representation of numbers for machine learning(ML)input:Term frequency inverse document frequency(TF-IDF)and Bidirectional encoder representations from transformers(BERT).The well-known eXtreme gradient boosting(XGBoost)ML model is employed to classify whether certain tweets fall into the category of sexual-based harassment or not.Additionally,with the goal of reaching better performance,several XGBoost models were devised conducting hyperparameter tuning by metaheuristics.For this purpose,the recently emerging Coyote optimization algorithm(COA)was modified and adjusted to optimize the XGBoost model.Additionally,other cutting-edge metaheuristics approach for this challenge were also implemented,and rigid comparative analysis of the captured classification metrics(accuracy,Cohen kappa score,precision,recall,and F1-score)was performed.Finally,the best-generated model was interpreted by Shapley additive explanations(SHAP),and useful insights were gained about the behavioral patterns of people who perform social harassment.展开更多
Computerized tomography(CT)scans and X-rays play an important role in the diagnosis of COVID-19 and pneumonia.On the basis of the image analysis results of chest CT and X-rays,the severity of lung infection is monitor...Computerized tomography(CT)scans and X-rays play an important role in the diagnosis of COVID-19 and pneumonia.On the basis of the image analysis results of chest CT and X-rays,the severity of lung infection is monitored using a tool.Many researchers have done in diagnosis of lung infection in an accurate and efficient takes lot of time and inefficient.To overcome these issues,our proposed study implements four cascaded stages.First,for pre-processing,a mean filter is used.Second,texture feature extraction uses principal component analysis(PCA).Third,a modified whale optimization algorithm is used(MWOA)for a feature selection algorithm.The severity of lung infection is detected on the basis of age group.Fourth,image classification is done by using the proposed MWOAwith the salp swarm algorithm(MWOA-SSA).MWOA-SSA has an accuracy of 97%,whereas PCA and MWOA have accuracies of 81%and 86%.The sensitivity rate of the MWOA-SSA algorithm is better that of than PCA(84.4%)and MWOA(95.2%).MWOA-SSA outperforms other algorithms with a specificity of 97.8%.This proposed method improves the effective classification of lung affected images from large datasets.展开更多
Breast cancer has become the second leading cause of death among women worldwide.In India,a woman is diagnosed with breast cancer every four minutes.There has been no known basis behind it,and detection is extremely c...Breast cancer has become the second leading cause of death among women worldwide.In India,a woman is diagnosed with breast cancer every four minutes.There has been no known basis behind it,and detection is extremely challenging among medical scientists and researchers due to unknown reasons.In India,the ratio of women being identified with breast cancer in urban areas is 22:1.Symptoms for this disease are micro calcification,lumps,and masses in mammogram images.These sources are mostly used for early detection.Digital mammography is used for breast cancer detection.In this study,we introduce a new hybrid wavelet filter for accurate image enhancement.The main objective of enhancement is to produce quality images for detecting cancer sections in images.Image enhancement is the main step where the quality of the input image is improved to detect cancer masses.In this study,we use a combination of two filters,namely,Gabor and Legendre.The edges are detected using the Canny detector to smoothen the images.High-quality enhanced image is obtained through the Gabor-Legendre filter(GLFIL)process.Further image is used by classification algorithm.Animal migration optimization with neural network is implemented for classifying the image.The output is compared to existing filter techniques.Ultimately,the accuracy achieved by the proposed technique is 98%,which is higher than existing algorithms.展开更多
基金supported by the Science Fund of the Republic of Serbia,Grant No.7373Characterizing Crises-Caused Air Pollution Alternations Using an Artificial Intelligence-Based Framework-crAIRsis and Grant No.7502Intelligent Multi-Agent Control and Optimization applied to Green Buildings and Environmental Monitoring Drone Swarms-ECOSwarm.
文摘Cyberbullying is a form of harassment or bullying that takes place online or through digital devices like smartphones,computers,or tablets.It can occur through various channels,such as social media,text messages,online forums,or gaming platforms.Cyberbullying involves using technology to intentionally harm,harass,or intimidate others and may take different forms,including exclusion,doxing,impersonation,harassment,and cyberstalking.Unfortunately,due to the rapid growth of malicious internet users,this social phenomenon is becoming more frequent,and there is a huge need to address this issue.Therefore,the main goal of the research proposed in this manuscript is to tackle this emerging challenge.A dataset of sexist harassment on Twitter,containing tweets about the harassment of people on a sexual basis,for natural language processing(NLP),is used for this purpose.Two algorithms are used to transform the text into a meaningful representation of numbers for machine learning(ML)input:Term frequency inverse document frequency(TF-IDF)and Bidirectional encoder representations from transformers(BERT).The well-known eXtreme gradient boosting(XGBoost)ML model is employed to classify whether certain tweets fall into the category of sexual-based harassment or not.Additionally,with the goal of reaching better performance,several XGBoost models were devised conducting hyperparameter tuning by metaheuristics.For this purpose,the recently emerging Coyote optimization algorithm(COA)was modified and adjusted to optimize the XGBoost model.Additionally,other cutting-edge metaheuristics approach for this challenge were also implemented,and rigid comparative analysis of the captured classification metrics(accuracy,Cohen kappa score,precision,recall,and F1-score)was performed.Finally,the best-generated model was interpreted by Shapley additive explanations(SHAP),and useful insights were gained about the behavioral patterns of people who perform social harassment.
文摘Computerized tomography(CT)scans and X-rays play an important role in the diagnosis of COVID-19 and pneumonia.On the basis of the image analysis results of chest CT and X-rays,the severity of lung infection is monitored using a tool.Many researchers have done in diagnosis of lung infection in an accurate and efficient takes lot of time and inefficient.To overcome these issues,our proposed study implements four cascaded stages.First,for pre-processing,a mean filter is used.Second,texture feature extraction uses principal component analysis(PCA).Third,a modified whale optimization algorithm is used(MWOA)for a feature selection algorithm.The severity of lung infection is detected on the basis of age group.Fourth,image classification is done by using the proposed MWOAwith the salp swarm algorithm(MWOA-SSA).MWOA-SSA has an accuracy of 97%,whereas PCA and MWOA have accuracies of 81%and 86%.The sensitivity rate of the MWOA-SSA algorithm is better that of than PCA(84.4%)and MWOA(95.2%).MWOA-SSA outperforms other algorithms with a specificity of 97.8%.This proposed method improves the effective classification of lung affected images from large datasets.
文摘Breast cancer has become the second leading cause of death among women worldwide.In India,a woman is diagnosed with breast cancer every four minutes.There has been no known basis behind it,and detection is extremely challenging among medical scientists and researchers due to unknown reasons.In India,the ratio of women being identified with breast cancer in urban areas is 22:1.Symptoms for this disease are micro calcification,lumps,and masses in mammogram images.These sources are mostly used for early detection.Digital mammography is used for breast cancer detection.In this study,we introduce a new hybrid wavelet filter for accurate image enhancement.The main objective of enhancement is to produce quality images for detecting cancer sections in images.Image enhancement is the main step where the quality of the input image is improved to detect cancer masses.In this study,we use a combination of two filters,namely,Gabor and Legendre.The edges are detected using the Canny detector to smoothen the images.High-quality enhanced image is obtained through the Gabor-Legendre filter(GLFIL)process.Further image is used by classification algorithm.Animal migration optimization with neural network is implemented for classifying the image.The output is compared to existing filter techniques.Ultimately,the accuracy achieved by the proposed technique is 98%,which is higher than existing algorithms.
