A study on haloperidol and risperidone induced metabolic derangements in patients with newly diagnosed schizophrenia

Background: The occurrence of metabolic abnormalities in schizophrenic patients has been increased with the rampant use of second-generation antipsychotics. The aim and objective of this study is to compare the metabolic derangements induced by a typical antipsychotic: haloperidol and an atypical antipsychotic, risperidone in patients with newly diagnosed schizophrenia in a tertiary care hospital.Methods: Out of 60 newly diagnosed schizophrenic patients, 30 patients received tablet haloperidol and the remaining 30 patients received tablet risperidone orally. The anthropometric measurements like height, weight, waist circumference was measured and blood investigations like fasting blood glucose level and fasting lipid profile were taken at baseline and at the end of 3 and 6 months of drug therapy. The metabolic derangements induced by the two antipsychotics were compared and analyzed at end of 3rd and 6th month using SPSS software version 16.Results: At the end of 6th month statistically significant differences (p<0.05) were observed in weight, waist circumference, fasting blood sugar, fasting triglyceride and high-density lipoprotein level between the haloperidol and risperidone group on following the International Diabetic Federation (IDF) criteria of metabolic syndrome. Risperidone caused metabolic abnormalities in 13.3%, 4 patients whereas none of the patients in haloperidol group developed metabolic syndrome.Conclusions: Hence it is concluded that the atypical antipsychotic risperidone has been associated with an increased risk of causing metabolic abnormalities than the typical antipsychotic haloperidol. Regular and periodic monitoring of the anthropometric and metabolic parameters in schizophrenic patients on antipsychotics especially the atypical antipsychotics is mandatory to prevent further complications

Zero-Day Malware Detection and Effective Malware Analysis Using Shapley Ensemble Boosting and Bagging Approach

Software products from all vendors have vulnerabilities that can cause a security concern. Malware is used as a prime exploitation tool to exploit these vulnerabilities. Machine learning (ML) methods are efficient in detecting malware and are state-of-art. The effectiveness of ML models can be augmented by reducing false negatives and false positives. In this paper, the performance of bagging and boosting machine learning models is enhanced by reducing misclassification. Shapley values of features are a true representation of the amount of contribution of features and help detect top features for any prediction by the ML model. Shapley values are transformed to probability scale to correlate with a prediction value of ML model and to detect top features for any prediction by a trained ML model. The trend of top features derived from false negative and false positive predictions by a trained ML model can be used for making inductive rules. In this work, the best performing ML model in bagging and boosting is determined by the accuracy and confusion matrix on three malware datasets from three different periods. The best performing ML model is used to make effective inductive rules using waterfall plots based on the probability scale of features. This work helps improve cyber security scenarios by effective detection of false-negative zero-day malware

MULBER: Effective Android Malware Clustering Using Evolutionary Feature Selection and Mahalanobis Distance Metric

Symmetric and asymmetric patterns are fascinating phenomena that show a level of co-existence in mobile application behavior analyses. For example, static phenomena, such as information sharing through collaboration with known apps, is a good example of a symmetric model of communication, and app collusion, where apps collaborate dynamically with unknown malware apps, is an example of a serious threat with an asymmetric pattern. The symmetric nature of app collaboration can become vulnerable when a vulnerability called PendingIntent is exchanged during Inter-Component Communication (ICC). The PendingIntent (PI) vulnerability enables a flexible software model, where the PendingIntent creator app can temporarily share its own permissions and identity with the PendingIntent receiving app. The PendingIntent vulnerability does not require approval from the device user or Android OS to share the permissions and identity with other apps. This is called a PI leak, which can lead to malware attacks such as privilege escalation and component hijacking attacks. This vulnerability in the symmetric behavior of an application without validating an app’s privileges dynamically leads to the asymmetric phenomena that can damage the robustness of an entire system. In this paper, we propose MULBER, a lightweight machine learning method for the detection of Android malware communications that enables a cybersecurity system to analyze multiple patterns and learn from them to help prevent similar attacks and respond to changing behavior. MULBER can help cybersecurity teams to be more proactive in preventing dynamic PI-based communication threats and responding to active attacks in real time. MULBER performs a static binary analysis on the APK file and gathers approximately 10,755 features, reducing it to 42 key features by grouping the permissions under the above-mentioned four categories. Finally, MULBER learns from these multivariate features using evolutionary feature selection and the Mahalanobis distance metric and classifies them as either benign or malware apps. In an evaluation of 22,638 malware samples from recent Android APK malware databases such as Drebin and CICMalDroid-2020, MULBER outperformed others by clustering applications based on the Mahalanobis distance metric and detected 95.69% of malware with few false alarms and the explanations provided for each detection revealed the relevant properties of the detected malware

The Third MSSRF South - South Exchange Travelling Workshop: 15-22 October 2004 Tamil Nadu & Pondicherry, India

The M S Swaminathan Research Foundation (MSSRF) invited about 20 development workers - both from NGOs and from other agencies - to spend eight days, travelling from village to village, visiting knowledge centers and other development projects of MSSRF, meeting the volunteers and the local communities and learning from one another and sharing experiences. This report introduces the third workshop held in October 2004. It concentrates on MSSRF’s work and philosophy and emphasizes rightly that at MSSRF ICTs are not seen as a technical solution on their own but as enablers in a process of local prioritization and problem solving. It relates the success of the program to embedding ICTs in a holistic approach encompassing a diverse range of development initiatives

An Efficient DenseNet-Based Deep Learning Model for Malware Detection

Recently, there has been a huge rise in malware growth, which creates a significant security threat to organizations and individuals. Despite the incessant efforts of cybersecurity research to defend against malware threats, malware developers discover new ways to evade these defense techniques. Traditional static and dynamic analysis methods are ineffective in identifying new malware and pose high overhead in terms of memory and time. Typical machine learning approaches that train a classifier based on handcrafted features are also not sufficiently potent against these evasive techniques and require more efforts due to feature-engineering. Recent malware detectors indicate performance degradation due to class imbalance in malware datasets. To resolve these challenges, this work adopts a visualization-based method, where malware binaries are depicted as two-dimensional images and classified by a deep learning model. We propose an efficient malware detection system based on deep learning. The system uses a reweighted class-balanced loss function in the final classification layer of the DenseNet model to achieve significant performance improvements in classifying malware by handling imbalanced data issues. Comprehensive experiments performed on four benchmark malware datasets show that the proposed approach can detect new malware samples with higher accuracy (98.23% for the Malimg dataset, 98.46% for the BIG 2015 dataset, 98.21% for the MaleVis dataset, and 89.48% for the unseen Malicia dataset) and reduced false-positive rates when compared with conventional malware mitigation techniques while maintaining low computational time. The proposed malware detection solution is also reliable and effective against obfuscation attacks

Design of Waste Management System Using Ensemble Neural Networks

Waste management is an essential societal issue, and the classical and manual waste auditing methods are hazardous and time-consuming. In this paper, we introduce a novel method for waste detection and classification to address the challenges of waste management. The method uses a collection of deep neural networks to allow for accurate waste detection, classification, and waste size quantification. The trained neural network model is integrated into a mobile-based application for trash geotagging based on images captured by users on their smartphones. The tagged images are then connected to the cleaners’ database, and the nearest cleaners are notified of the waste. The experimental results using publicly available datasets show the effectiveness of the proposed method in terms of detection and classification accuracy. The proposed method achieved an accuracy of at least 90%, which surpasses that reported by other state-of-the-art methods on the same datasets

Design of Waste Management System Using Ensemble Neural Networks

Waste management is an essential societal issue, and the classical and manual waste auditing methods are hazardous and time-consuming. In this paper, we introduce a novel method for waste detection and classification to address the challenges of waste management. The method uses a collection of deep neural networks to allow for accurate waste detection, classification, and waste size quantification. The trained neural network model is integrated into a mobile-based application for trash geotagging based on images captured by users on their smartphones. The tagged images are then connected to the cleaners&rsquo; database, and the nearest cleaners are notified of the waste. The experimental results using publicly available datasets show the effectiveness of the proposed method in terms of detection and classification accuracy. The proposed method achieved an accuracy of at least 90%, which surpasses that reported by other state-of-the-art methods on the same datasets

Erratum: Graphene oxide nanopaint (Carbon (2014) 72 (328-337))

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Graphene oxide nanopaint

Nanostructured materials are receiving growing interest in the development of a number of commercial products. In this study, we have developed a multifunctional graphene oxide (GO) nanopaint by incorporating GO sheets in an alkyd resin with suitable non-toxic additives using ball milling. The drying mechanism of the GO nanopaint has been discussed. Intermolecular cross-linking between GO and the lipid chains in the alkyd resin was studied by Fourier transform infra red spectra, Raman spectra, and X-ray photoelectron spectra, respectively. The prepared GO nanopaint exhibited good corrosion-resistant behavior in both acidic and high-salt-content solutions as examined by the immersion and electrochemical corrosion tests. The GO nanopaint coating possesses a corrosion protection efficiency of about 76% in salt water as estimated from the linear polarization studies. The antibacterial property of the GO nanopaint coated surface was studied against three bacterial strains (Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa) and the results showed that GO nanopaint inhibited the bacterial growth on its surface. The in situ biofouling tests demonstrated the inhibition of fouling on the GO nanopaint surface.close5