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Predicting the drug targets in the base of cancer cell line is one of the hottest issues in cancer treatment. Drug sensitivity describes which drug is perfect for cell line in certain condition or disease. This condition exists due to change in human metabolism. Different techniques are used for cancer treatment such as radiotherapy, hormone therapy, chemotherapy, and surgery. Many statistical methods and machine learning algorithms such as support vector machine, principal component analysis (PCA), logistic regression, simple linear regression, naive Bayes classifier, generalized linear regression, and random forest have been used for drug target prediction. However, these predictors take more time for computation using different tools such as MATLAB and R tool. In this study, different machine learning techniques are applied using Apache Spark to predict drug targets. Apache Spark uses the resilient distributed dataset (RDD) technique for in-memory fast computation and fault tolerance. The obtained results indicate that Spark provides better accuracy in short time when compared with existing tools.

Unsupervised anomaly detection in multivariate time series sensor data is a complex task with diverse applications in different domains such as livestock farming and agriculture (LF&A), the Internet of Things (IoT), and human activity recognition (HAR). Advanced machine learning techniques are necessary to detect multi-sensor time series data anomalies. The primary focus of this research is to develop state-of-the-art machine learning methods for detecting anomalies in multi-sensor data. Time series sensors frequently produce multi-sensor data with anomalies, which makes it difficult to establish standard patterns that can capture spatial and temporal correlations. Our innovative approach enables the accurate identification of normal, abnormal, and noisy patterns, thus minimizing the risk of misinterpreting models when dealing with mixed noisy data during training. This can potentially result in the model deriving incorrect conclusions. To address these challenges, we propose a novel approach called "TimeTector-Twin-Branch Shared LSTM Autoencoder" which incorporates several Multi-Head Attention mechanisms. Additionally, our system now incorporates the Twin-Branch method which facilitates the simultaneous execution of multiple tasks, such as data reconstruction and prediction error, allowing for efficient multi-task learning. We also compare our proposed model to several benchmark anomaly detection models using our dataset, and the results show less error (MSE, MAE, and RMSE) in reconstruction and higher accuracy scores (precision, recall, and F1) against the baseline models, demonstrating that our approach outperforms these existing models. [ABSTRACT FROM AUTHOR]

Fake news alludes to the news containing deluding or manufactured information that is groundless, which is purposefully misrepresented and spread among a community. It can mutilate reality and become a cause of many social problems and misunderstandings. Many studies have been conducted to investigate the engagement features. However, the choice of parameters is diverse, secondly, the spreading of news is a continuous process that needs to be evaluated. This study proposes a model for analyzing social engagement features on a news dataset of Twitter which is one of the most important platforms for spreading news. The news data was collected from an open-source data store of Kaggle and LIAR. The data was preprocessed and transformed into proportional values with six engagement features i.e. Retweets, Likes, Comments, Quoted Retweets, Multimedia and Images. These features were divided into 05 classification models with a combination of two engagement features. Based on the results four models were selected for further analysis. Models 1 (Retweet and QRT) and Model 5 (Likes and QRT) showed an imbalance of accuracy metrics on fake and real news data. Model 3 (Comments and QRT) and Model 4 (Retweets and Likes) obtained a good balance on accuracy metrics. Results showed that social engagement features can be used to estimate the credibility of news on Twitter. [ABSTRACT FROM AUTHOR]

In the COVID-19 era, the use of social media platforms has significantly increased leading to misinformation being produced whose management is quite necessary for the domain experts, such as the Reddit social platform where people disseminate extensive information about their health issues using relevant posts and comments. The management of misinformation about COVID-19 impact on mental illness could be quite beneficial for the domain experts. In this regard, we proposed a two-step methodology which could aid domain experts to manage and group the posts and comments information with respect to COVID-19 impact on mental illness. First, we extract the information of well-known mental illnesses (such as depression, anxiety, OCD and PTSD) from the Raddit platform. Second, we leverage the capabilities of unsupervised learning algorithms and text categorisation approach to manage the information. We also proposed the evaluation model to assess the efficacy of the proposed method according to expert opinion. The experimental results indicate the efficacy of the proposed method. Moreover, we observed fuzzy c-means as an outperformed learner (with ARI = 0. 7 6) as compared to K-means (ARI = 0. 7 0) and Agglomerative (ARI = 0. 6 9). [ABSTRACT FROM AUTHOR]

The proliferation of electric vehicle (EV) adoption strains low-voltage distribution networks, particularly in aggregated charging scenarios, prompting utility companies to incentivize charging aggregators for optimizing load balancing within thermal limits. These aggregators utilize machine learning algorithms to understand electricity price signals and orchestrate the optimization of the EV charging process. However, conventional machine learning approaches fall short when dealing with the dynamic and volatile nature of electricity prices, emphasizing the necessity for advanced ensemble models. This paper introduces a novel Deep-Weighted Ensemble Model (DWEM) rooted in standard and stacked Long Short-Term Memory (LSTM) networks designed for wholesale electricity price forecasting, to manage the EV charging at the aggregator level. The ensemble development process involves developing an architecture that highlights the significance of the DWEM model in supporting aggregators for the charging optimization of EVs. The charging optimization problem of aggregated EVs is formulated, and the heuristic mechanism is systematically presented, evaluating various weight configurations, and selecting those characterized by the highest levels of accuracy to comprise the ensemble model. Moreover, we incorporated a standard deviation mechanism to evaluate the impact of the proposed DWEM on forecasting accuracy, mean squared error, and mean absolute error across various standard deviation levels. We leveraged a publicly available Houston electricity dataset and performed a detailed data engineering mechanism, accounting for data both with and without outliers. Subsequently, we applied the proposed DWEM to this dataset and conducting three types of comparative analysis: (a) evaluating model performance in terms of accuracy, mean square error, and mean absolute error; (b) assessing aggregator charging analysis focusing on charging load and cost; and (c) analyzing computational complexity and execution time. The simulation results demonstrated a improvement in accuracy and reduction in charging load and cost compared to state-of-the-art methods, while maintaining competitive computational complexity. • Developed a Deep-Weighted Ensemble Model for wholesale electricity price forecasting. • Formulated a charging optimization problem for aggregated electric vehicles. • Introduced a heuristic mechanism for determining optimal weights in the ensemble model. • Enhanced charging efficiency at the aggregator level. [ABSTRACT FROM AUTHOR]

This study aims to identify the potential association of mental health and social media forum during the outbreak of COVID-19 pandemic. COVID-19 brings a lot of challenges to government globally. Among the different strategies the most extensively adopted ones were lockdown, social distancing, and isolation among others. Most people with no mental illness history have been found with high risk of distress and psychological discomfort due to anxiety of being infected with the virus. Panic among people due to COVID-19 spread faster than the disease itself. Them is information and excessive usage of social media in this pandemic era have adversely affected mental health across the world. Due to limited historical data, psychiatrists are finding it difficult to cure the mental illness of people resulting from the pandemic repercussion, fueled by social media forum. In this study the methodology used for data extraction is by considering the implications of social network platforms (such as Reddit) and levering the capabilities of a semi-supervised co-training technique-based use of Naïve Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM) classifiers. The experimental results shows the efficacy of the proposed methodology to identify the mental illness level (such as anxiety, bipolar disorder, depression, PTSD, schizophrenia, and OCD) of those who are in anxious of being infected with this virus. We observed 1 to 5% improvement in the classification decision through the proposed method as compared to state-of-the-art classifiers. [ABSTRACT FROM AUTHOR]

• Existing technologies like smart grid (SG) and smart meters play a significant role in meeting the everlasting demand of energy consumption, supply, and generation for Peer-to-Peer (P2P) energy trading between different distributed prosumers. Whereas blockchain when used with P2P energy trading plays a major role in cost and security for trading by eliminating any involvement of outsiders and third parties. However, existing works related to the blockchain with P2P energy trading are engaged in increasing the cost related to resource allocation, latency, computational processing, and large network setup. • Moreover, in current existing techniques, peer nodes can send 25-200 transactions per second. By expanding the number of peer nodes, the network traffic volume can also be enhanced. This resulted in a significant reduction in network throughput. The network latency will increase as the number of peer nodes grows. • The objective of this paper is to design and develop a 3-tier architecture, an analytical model, and a hybrid algorithm for network analysis in a blockchain-based P2P energy trading system using reinforcement learning (RL) and feed-forward neural network (FFNN) techniques. In this model, we will examine the various parameters and tradeoffs which affect the delay, throughput, and security in P2P energy trading. This will lead to profitable P2P energy trading between different distributed prosumers. Existing technology like smart grid (SG) and smart meters play a significant role in meeting the everlasting demand of energy consumption, supply, and generation for peer-to-peer (P2P) energy trading between different distributed prosumers. Whereas blockchain when used with P2P energy trading plays a major role in cost and security by eliminating any involvement of outsiders and third parties. However, existing works related to the blockchain with P2P energy trading are engaged in increasing the cost related to resource allocation, latency, computational processing, and large network setup. The objective of this paper is to design and develop a three-tier architecture, an analytical model, and a hybrid algorithm for network analysis in a blockchain-based P2P energy trading system using reinforcement learning (RL) and feed forward neural network (FFNN) techniques. In this model, we will examine the various parameters and tradeoffs which affect the delay, throughput, and security in P2P energy trading. This will lead to profitable P2P energy trading between different distributed prosumers. By analyzing the simulation results of the proposed model and algorithm by benchmarking with the existing state-of-the-art techniques it's clear that the proposed algorithm shows marked improvement over network latency generated results. The simulation of the model is conducted using the iFogSim simulator, Ganache with Ethereum platform, Truffle, Python editor tool, and ATOM IDE with solidity. [ABSTRACT FROM AUTHOR]

While most of the existing class stability assessors just rely on structural information retrieved from a desired source code snapshot. However, class stability is intrinsically characterized by the evolution of a number of dependencies and change propagation factors which aid to promote the ripple effect. Identification of classes prone to ripple effect (instable classes) through mining the version history of change propagation factors can aid developers to reduce the efforts needed to maintain and evolve the system. We propose Historical Information for Class Stability Prediction (HICSP), an approach to exploit change history information to predict the instable classes based on its correlation with change propagation factors. Subsequently, we performed two empirical studies. In the first study, we evaluate the HICSP on the version history of 10 open source projects. Subsequently, in the second replicated study, we evaluate the effectiveness of HICSP by tuning the parameters of its stability assessors. We observed the 4 to 16 percent improvement in term of F-measure value to predict the instable classes through HICSP as compared to existing class stability assessors. The promising results indicate that HICSP is able to identify instable classes and can aid developers in their decision making. [ABSTRACT FROM AUTHOR]

The performance of text classification is highly related to the feature selection methods. Usually, two tasks are performed when a feature selection method is applied to construct a feature set; 1) assign score to each feature and 2) select the top-N features. The selection of top-N features in the existing filter-based feature selection methods is biased by their discriminative power and the empirical process which is followed to determine the value of N. In order to improve the text classification performance by presenting a more illustrative feature set, we present an approach via a potent representation learning technique, namely DBN (Deep Belief Network). This algorithm learns via the semantic illustration of documents and uses feature vectors for their formulation. The nodes, iteration, and a number of hidden layers are the main parameters of DBN, which can tune to improve the classifier’s performance. The results of experiments indicate the effectiveness of the proposed method to increase the classification performance and aid developers to make effective decisions in certain domains. [ABSTRACT FROM AUTHOR]

Context Numerous software design patterns have been introduced and cataloged either as a canonical or a variant solution to solve a design problem. The existing automatic techniques for design pattern(s) selection aid novice software developers to select the more appropriate design pattern(s) from the list of applicable patterns to solve a design problem in the designing phase of software development life cycle. Goal However, the existing automatic techniques are limited to the semi-formal specification, multi-class problem, an adequate sample size to make precise learning and individual classifier training in order to determine a candidate design pattern class and suggest more appropriate pattern(s). Method To address these issues, we exploit a text categorization based approach via Fuzzy c-means (unsupervised learning technique) that targets to present a systematic way to group the similar design patterns and suggest the appropriate design pattern(s) to developers related to the specification of a given design problem. We also propose an evaluation model to assess the effectiveness of the proposed approach in the context of several real design problems and design pattern collections. Subsequently, we also propose a new feature selection method Ensemble-IG to overcome the multi-class problem and improve the classification performance of the proposed approach. Results The promising experimental results suggest the applicability of the proposed approach in the domain of classification and selection of appropriate design patterns. Subsequently, we also observed the significant improvement in learning precision of the proposed approach through Ensemble-IG. Conclusion The proposed approach has four advantages as compared to previous work. First, the semi-formal specification of design patterns is not required as a prerequisite; second, the ground reality of class label assignment is not mandatory; third, lack of classifier’s training for each design pattern class and fourth, an adequate sample size is not required to make precise learning. [ABSTRACT FROM AUTHOR]

Robotic exoskeletons have demonstrated their effectiveness in post-stroke gait rehabilitation therapy. Nevertheless, further research is being conducted to improve existing rehabilitation exoskeletons in terms of ease-of-use and innovative design. Previously, the adaptation of linkage-based mechanisms for rehabilitation exoskeletons has been considered an option. However, finding linkage parameters that will produce the required gait trajectories using a linkage-based exoskeleton, is quite challenging. It is furthermore challenging to obtain parameters of a linkage-based mechanism designed for a gait rehabilitation task that has to produce two trajectories (for knee and ankle joints) simultaneously. In this work, we propose Deep Generative Neural Networks (DGNN) to obtain a set of optimal dimensions and parameters for the Stephenson III six-bar linkage-based gait exoskeleton. The proposed methodology demonstrates high efficacy in determining the linkage parameters for various target trajectories. The proposed framework, once trained, can accurately predict mechanism parameters to achieve two joint trajectories simultaneously. Subsequent to developing the model, walking trajectories from healthy human subjects are given to the model to determine the optimal linkage dimensions of the gait rehabilitation exoskeleton. The proposed model can be used to assist designers in quickly determining the optimized linkage dimensions of linkage-based mechanisms that can provide various target trajectories. [ABSTRACT FROM AUTHOR]

Conventional decision trees use queries each of which is based on one attribute. In this study, we also examine decision trees that handle additional queries based on hypotheses. This kind of query is similar to the equivalence queries considered in exact learning. Earlier, we designed dynamic programming algorithms for the computation of the minimum depth and the minimum number of internal nodes in decision trees that have hypotheses. Modification of these algorithms considered in the present paper permits us to build decision trees with hypotheses that are optimal relative to the depth or relative to the number of the internal nodes. We compare the length and coverage of decision rules extracted from optimal decision trees with hypotheses and decision rules extracted from optimal conventional decision trees to choose the ones that are preferable as a tool for the representation of information. To this end, we conduct computer experiments on various decision tables from the UCI Machine Learning Repository. In addition, we also consider decision tables for randomly generated Boolean functions. The collected results show that the decision rules derived from decision trees with hypotheses in many cases are better than the rules extracted from conventional decision trees. [ABSTRACT FROM AUTHOR]