Demand-side load forecasting in smart grids using machine learning techniques.

Electrical load forecasting remains an ongoing challenge due to various factors, such as temperature and weather, which change day by day. In this age of Big Data, efficient handling of data and obtaining valuable information from raw data is crucial. Through the use of IoT devices and smart meters, we can capture data efficiently, whereas traditional methods may struggle with data management. The proposed solution consists of two levels for forecasting. The selected subsets of data are first fed into the "Daily Consumption Electrical Networks" (DCEN) network, which provides valid input to the "Intra Load Forecasting Networks" (ILFN) network. To address overfitting issues, we use classic or conventional neural networks. This research employs a three-tier architecture, which includes the cloud layer, fog layer, and edge servers. The classical state-of-the-art prediction schemes usually employ a two-tier architecture with classical models, which can result in low learning precision and overfitting issues. The proposed approach uses more weather features that were not previously utilized to predict the load. In this study, numerous experiments were conducted and found that support vector regression outperformed other methods. The results obtained were 5.055 for mean absolute percentage error (MAPE), 0.69 for root mean square error (RMSE), 0.37 for normalized mean square error (NRMSE), 0.0072 for mean squared logarithmic error (MSLE), and 0.86 for R2 score values. The experimental findings demonstrate the effectiveness of the proposed method.

AndroDex: Android Dex Images of Obfuscated Malware.

With the emergence of technology and the usage of a large number of smart devices, cyber threats are increasing. Therefore, research studies have shifted their attention to detecting Android malware in recent years. As a result, a reliable and large-scale malware dataset is essential to build effective malware classifiers. In this paper, we have created AndroDex: an Android malware dataset containing a total of 24,746 samples that belong to more than 180 malware families. These samples are based on .dex images that truly reflect the characteristics of malware. To construct this dataset, we first downloaded the APKs of the malware, applied obfuscation techniques, and then converted them into images. We believe this dataset will significantly enhance a series of research studies, including Android malware detection and classification, and it will also boost deep learning classification efforts, among others. The main objective of creating images based on the Android dataset is to help other malware researchers better understand how malware works. Additionally, an important result of this study is that most malware nowadays employs obfuscation techniques to hide their malicious activities. However, malware images can overcome such issues. The main limitation of this dataset is that it contains images based on .dex files that are based on static analysis. However, dynamic analysis takes time, therefore, to overcome the issue of time and space this dataset can be used for the initial examination of any .apk files.

Protective effects of niacin following high fat rich diet: an in-vivo and in-silico study.

Niacin had long been understood as an antioxidant. There were reports that high fat diet (HFD) may cause psychological and physical impairments. The present study was aimed to experience the effect of Niacin on % growth rate, cumulative food intake, motor activity and anxiety profile, redox status, 5-HT metabolism and brain histopathology in rats. Rats were administered with Niacin at a dose of 50 mg/ml/kg body weight for 4 weeks following normal diet (ND) and HFD. Behavioral tests were performed after 4 weeks. Animals were sacrificed to collect brain samples. Biochemical, neurochemical and histopathological studies were performed. HFD increased food intake and body weight. The exploratory activity was reduced and anxiety like behavior was observed in HFD treated animals. Activity of antioxidant enzymes was decreased while oxidative stress marker and serotonin metabolism in the brain of rat were increased in HFD treated animals than ND fed rats. Morphology of the brain was also altered by HFD administration. Conversely, Niacin treated animals decreased food intake and % growth rate, increased exploratory activity, produced anxiolytic effects, decreased oxidative stress and increased antioxidant enzyme and 5-HT levels following HFD. Morphology of brain is also normalized by the treatment of Niacin following HFD. In-silico studies showed that Niacin has a potential binding affinity with degradative enzyme of 5-HT i.e. monoamine oxidase (MAO) A and B with an energy of ~ - 4.5 and - 5.0 kcal/mol respectively. In conclusion, the present study showed that Niacin enhanced motor activity, produced anxiolytic effect, and reduced oxidative stress, appetite, growth rate, increased antioxidant enzymes and normalized serotonin system and brain morphology following HFD intake. In-silico studies suggested that increase 5-HT was associated with the binding of MAO with Niacin subsequentially an inhibition of the degradation of monoamine. It is suggested that Niacin has a great antioxidant potential and could be a good therapy for the treatment of HFD induced obesity.

Evaluation and classification of obfuscated Android malware through deep learning using ensemble voting mechanism.

With the rise in popularity and usage of Android operating systems, malicious applications are targeted by applying innovative ways and techniques. Today, malware becomes intelligent that uses several ways of obfuscation techniques to hide its functionality and evade anti-malware engines. For mainstream smartphone users, Android malware poses a severe security danger. An obfuscation approach, however, can produce malware versions that can evade current detection strategies and dramatically lower the detection accuracy. Attempting to identify Android malware obfuscation variations, this paper proposes an approach to address the challenges and issues related to the classification and detection of malicious obfuscated variants. The employed detection and classification scheme uses both static and dynamic analysis using an ensemble voting mechanism. Moreover, this study demonstrates that a small subset of features performs consistently well when they are derived from the basic malware (non-obfuscated), however, after applying a novel feature-based obfuscation approach, the study shows a drastic change indicating the relative importance of these features in obfuscating benign and malware applications. For this purpose, we present a fast, scalable, and accurate mechanism for obfuscated Android malware detection based on the Deep learning algorithm using real and emulator-based platforms. The experiments show that the proposed model detects malware effectively and accurately along with the identification of features that are usually obfuscated by malware attackers.

Anti-amoebic effects of synthetic acridine-9(10H)-one against brain-eating amoebae.

Pathogenic A. castellanii and N. fowleri are opportunistic free-living amoebae. Acanthamoeba spp. are the causative agents of granulomatous amebic encephalitis (GAE) and amebic keratitis (AK), whereas Naegleria fowleri causes a very rare but severe brain infection called primary amebic meningoencephalitis (PAM). Acridinone is an important heterocyclic scaffold and both synthetic and naturally occurring derivatives have shown various valuable biological properties. In the present study, ten synthetic Acridinone derivatives (I-X) were synthesized and assessed against both amoebae for anti-amoebic and cysticidal activities in vitro. In addition, excystation, encystation, cytotoxicity, host cell pathogenicity was also performed in-vitro. Furthermore, molecular docking studies of these compounds with three cathepsin B paralogous enzymes of N. fowleri were performed in order to predict the possible docking mode with pathogen. Compound VII showed potent anti-amoebic activity against A. castellanii with IC50 53.46 µg/mL, while compound IX showed strong activity against N. fowleri in vitro with IC50 72.41 µg/mL. Compounds II and VII showed a significant inhibition of phenotypic alteration of A. castellanii, while compound VIII significantly inhibited N. fowleri cysts. Cytotoxicity assessment showed that these compounds caused minimum damage to human keratinocyte cells (HaCaT cells) at 100 µg/mL, while also effectively reduced the cytopathogenicity of Acanthamoeba to HaCaT cells. Moreover, Cathepsin B protease was investigated in-silico as a new molecular therapeutic target for these compounds. All compounds showed potential interactions with the catalytic residues. These results showed that acridine-9(10H)-one derivatives, in particular compounds II, VII, VIII and IX hold promise in the development of therapeutic agents against these free-living amoebae.

3-substituted coumarin derivatives over-activate the HslV protease: A potential drug target for antibacterial activity.

The bacterial HslVU complex consists of two different proteins, i.e., the HslV protease and the HslU ATPase. The functional HslVU enzyme complex forms only when the HslU c-terminal helix is inserted into the cavity located between two adjacent HslV monomers in order to allosterically activate the HslV protease. Based on its essential role in maintaining microbial proteostasis as well its absence from human beings, it is considered a promising therapeutic target for designing antibacterial agents. The goal of the present study was to find out potential drug candidates that could over-activate the HslV protease and produce aberrant proteolysis in pathogenic bacteria. Derivatives of 3-substituted coumarin have been identified as potential HslV protease activators based on their highest docking scores, ideal interaction patterns, and significant in-vitro HslV activation potential. Their ED50 values were in the sub-micromolar range, i.e., 0.4-0.48µM. The conformational stability of the contacts between the HslV dimer and the active compounds was further confirmed by molecular dynamics studies. Correspondingly, the ADMET characteristics of these lead molecules considerably demonstrated their significant non-toxic drug-like abilities. This research not only identified small non-peptidic HslV protease activators but also improved the understanding of the mode of action of 3-substituted coumarin derivatives as antibacterials.

Identification and structural investigation of potential novel drug candidates against lethal human pathogen.

Neisseria meningtidis is responsible for causing meningococcal meningitis along with acute septicaemia in human beings. Functional genomics strategies proved cruciality of certain genes/proteins in Neisseria meningitidis pathogenesis. During the present studies, three important Neisseria meningitidis proteins i.e., Dead box RNA-Helicase, Polyribonucleotide nucleotidyl-transferase PNPase and Ribonuclease-III were targeted for homology modeling and protein-ligand docking studies not only to determine their three dimensional architectures but also to identify their potential novel inhibitors. The Biscoumarin, malonitrile and indole derivatives showed the best inhibitory mode against all of the three enzymes. Since, these enzymes are assembled in Gram-negative bacteria to form RNA degradosome assembly therefore their inhibition will definitely shut off the degradosome assembly and ultimately the decay of RNA, which is an essential life process. This is the first ever structural investigation of these drug targets along with identification of potential novel drug candidates. We believe that these small chemical compounds will be proved as better drugs and will provide an excellent barrier towards Neisseria meningitidis pathogenesis.

On the classification of Microsoft-Windows ransomware using hardware profile.

Due to the expeditious inclination of online services usage, the incidents of ransomware proliferation being reported are on the rise. Ransomware is a more hazardous threat than other malware as the victim of ransomware cannot regain access to the hijacked device until some form of compensation is paid. In the literature, several dynamic analysis techniques have been employed for the detection of malware including ransomware; however, to the best of our knowledge, hardware execution profile for ransomware analysis has not been investigated for this purpose, as of today. In this study, we show that the true execution picture obtained via a hardware execution profile is beneficial to identify the obfuscated ransomware too. We evaluate the features obtained from hardware performance counters to classify malicious applications into ransomware and non-ransomware categories using several machine learning algorithms such as Random Forest, Decision Tree, Gradient Boosting, and Extreme Gradient Boosting. The employed data set comprises 80 ransomware and 80 non-ransomware applications, which are collected using the VirusShare platform. The results revealed that extracted hardware features play a substantial part in the identification and detection of ransomware with F-measure score of 0.97 achieved by Random Forest and Extreme Gradient Boosting.