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Nowadays, the crime rate increases dramatically in every country. Therefore, it is an urgent need for governments and social associations to produce persistent solutions and disincentive penalties to prevent crime issues. Specifically, social media plays an important role in crime rate detection; thus, reducing crime rates significantly. It would be a good medium for the desired task. In this paper, we analyze Twitter data collected from Twitter accounts for seven different locations (Ghaziabad, Chennai, Bangaluru, Chandigarh, Jammu, Gujarat, and Hyderabad) from January 2014 to November 2018 in a case study of India, which is opted to illustrate the efficiency of the proposed work. Sentiment analysis has been used to analyze users' behavior and psychology through the tweets of people to track crime actions. Twitter part-of-speech tagger, which is a Markov Model of first-order entropy, has been used for part-of-speech in online conversational text. Brown clustering is used for a long set of unlabeled tweets. Comparisons are verified with real crime rates from an authorized source of information according to different locations. We also measure the latest crime trends for the highest (Ghaziabad, Uttar Pradesh) and lowest crime cities (Jammu) in India. It has been found that the latest crime trends have been recorded for the time duration of 7 days (23, January 2019 to 30, January 2019). The analyses demonstrate that the obtained results match with the real crime rate data. We believe that these types of studies will help to detect the real-time crime rate for different locations and detect the crime pattern easily. [ABSTRACT FROM AUTHOR]

In Botnet Detection, Domain generation algorithms are the most effective method to intercept and analyze captured package. In this article, we propose a new method to classify harmful domain names using Neutrosophic Sets. Data of domain name, after being selected featured and fuzzed into Neutrosophic Sets will be used to classify benign domain names, malicious domain names and indeterminacy domain names, minimizing false detection of benign domain names. The proposed model is going to be tested and evaluated with other malicious domain detection models in the aspects of accuracy points, Accuracy, Revocation, and F1, all of which show that our proposed model has good results. [ABSTRACT FROM AUTHOR]

Mobile Ad hoc Networks (MANETs) have gained great interests owing to their dynamic and smoothness of exploitation. Conversely, the wireless and energetic nature adds exposed to different types of protection attacks than the other kind of networks. In this kind of attacks, it is essential to expand proficient intrusion-detection system to prevent MANET from different attacks. In this paper, we recommend a new intrusion-detection system called Modified Zone Based Intrusion Detection System (MZBIDS) for MANETs. Evaluated to contemporary methodologies, MZBIDS exhibits superior malicious behavior-detection ratios in convinced situations whereas it may not significantly influence performance of entire network. [ABSTRACT FROM AUTHOR]

A genetic algorithm (GA) belongs to the class of evolutionary algorithms and it is one of the most studied heuristic algorithms to solve graph coloring problems. In this paper, we propose a new GA algorithm for the total graph coloring problem. To the best of our knowledge, no algorithm based on a GA exists in the literature for total graph coloring. In the proposed approach, a novel encoding scheme is introduced, where all the edges and vertices of the graph are represented in a chromosome without any repetition. For the initialization of the population, a greedy algorithm is used to determine the total number of colors required for a total coloring of the graph. The number of colors is used as the fitness value of a chromosome which depends on the sequence of vertices and edges representing the chromosome. We introduce a convergence criteria for GA based on the total coloring conjecture. A two-point crossover and mutation operations, suitable for total coloring, are suggested. The proposed algorithm is applied on some well-known and standard graphs. In our computational tests, graphs are used with a maximum number of 690 vertices and 6650 edges of the graph, respectively. The proposed algorithm determines an optimal solution for 21 graphs among the 27 graphs. The solution of remaining the 6 graphs is near optimal and differs by at most one unit from the optimal value. The results show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Remote sensing applications are playing a vital role to improve the commercial satellite imagery with high resolution. In the spatial information system, object detection is the basic needs for computing the mathematical model. Geographical object related analysis for the image is used to gather data from remote sensing images. In this paper, we propose an Adaptive Convolutional Neural Network model using N-gram for Spatial Object Recognition on Satellite Images. Our methodology needs a learning model for the structures in the images to gather the data using prior knowledge. N-gram uses the functionalities of learning models. Spatial object recognition is performed using the learning method to segment the images with the human subjects that can increase their understanding of including the perception, cognition and decision. The result obtained for two stage of image processing is collected, and a relationship to psychological and mathematical basis is made. The results show that convinced association relevant level to the human perception is serving additional to identify the spatial objects. The experimentation is performed in MATLAB software where the results proved that our methodology is superior suitable for precise object detection and recognition on dissimilar levels of satellite images. [ABSTRACT FROM AUTHOR]

Disease diagnosis from medical images has become increasingly important in medical science. Abnormality identification in retinal images has become a challenging task in medical science. Effective machine learning and soft computing methods should be used to facilitate Diabetic Retinopathy Diagnosis from Retinal Images. Artificial Neural Networks are widely preferred for Diabetic Retinopathy Diagnosis from Retinal Images. It was observed that the conventional neural networks especially the Hopfield Neural Network (HNN) may be inaccurate due to the weight values are not adjusted in the training process. This paper presents a new Modified Hopfield Neural Network (MHNN) for abnormality classification from human retinal images. It relies on the idea that both weight values and output values can be adjusted simultaneously. The novelty of the proposed method lies in the training algorithm. In conventional method, the weights remain fixed but the weights are changing in the proposed method. Experimental performed on the Lotus Eye Care Hospital containing 540 images collected showed that the proposed MHNN yields an average sensitivity and specificity of 0.99 and accuracy of 99.25%. The proposed MHNN is better than HNN and other neural network approaches for Diabetic Retinopathy Diagnosis from Retinal Images. [ABSTRACT FROM AUTHOR]

It is indeed necessary to design of an elderly support mobile healthcare and monitoring system on wireless sensor network (WSN) for dynamic monitoring. It comes from the need for maintenance of healthcare among patients and elderly people that leads to the demand on change in traditional monitoring approaches among chronic disease patients and alert on acute events. In this paper, we propose a new automated patient diagnosis called automated patient diagnosis (AUPA) using ATmega microcontrollers over environmental sensors. AUPA monitors and aggregates data from patients through network connected over web server and mobile network. The scheme supports variable data management and route establishment. Data transfer is established using adaptive route discovery and management approaches. AUPA supports minimizing packet loss and delay, handling erroneous data, and providing optimized decision-making for healthcare support. The performance of AUPA's QoS approach is tested using a set of health-related sensors which gather the patient's data over variable period of time and send from a source to destination AUPA node. Experimental results show that AUPA outperforms the existing schemes, namely SPIN and LEACH, with minimal signal loss rate and a better neighborhood node selection and link selection. It diminishes the jitter compared to the related algorithms. Graphical abstract Stack architecture of AUPA. [ABSTRACT FROM AUTHOR]

This paper proposes a new technique based on Direct Sequence Code Division Multiple Access for underwater acoustically wireless transmission with excessive transmission rate. Environment of subsea is challenging for wireless communication because the medium in which waves are propagating is not air. In fact, it is propagated through fractions of water having different densities. Finding out various techniques for multipath access targeting the physical layer of Acoustic Sensor Networks is indeed necessary. The recent approaches have suggested that coded modulation techniques with exploited diversity are highly preferred in order to enhance the dependability of the acoustic link in different multipath channels. The proposed technique divides the channel into sub ones and transmits information via those sub channels. In variety-spectrum, a signal in a bandwidth is unfold within frequency domain and broad bandwidth. Experimental results show that Bit Error Rate (BER) of this method is better than that of channel equalization in the respective systems. [ABSTRACT FROM AUTHOR]

In this study, we developed Different Artificial Intelligence (AI) models namely Artificial Neural Network (ANN), Adaptive Network based Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM) for the prediction of Compression Coefficient of soil (Cc) which is one of the most important geotechnical parameters. A Monte Carlo approach was used for the sensitivity analysis of the AI models and input parameters. For the construction and validation of the models, 189 soft clayey soil samples were analyzed. In the models study, 13 input parameters: depth of sample, bulk density, plasticity index, moisture content, clay content, specific gravity, void ratio, liquid limit, dry density, porosity, plastic limit, degree of saturation, and liquidity index were used to obtain one output parameter "Cc". Validation of the models was done using statistical methods such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of determination (R2). Results of the model validation indicate that though performance of all the three models is good but SVM model is the best in the prediction of Cc. The Monte Carlo method based sensitivity analysis results show that out of the 13 input parameters considered for the models study, four parameters namely clay, degree of saturation, specific gravity and depth of sample are the most relevant in the prediction of Cc, and other parameters (bulk density, dry density, void ratio and porosity) are the most insignificant parameters for the prediction of Cc. Removal of these insignificant parameters helped to reduce the dimension of the input space and also model running time, and improved significantly the performance of the AI models. The results of this study might help in selecting the suitable AI models and input parameters for better and quick prediction of the Cc of soil. Unlabelled Image • Different Artificial Intelligence (AI) methods were developed for the prediction of Cc. • Monte Carlo approach is proposed for the sensitivity analysis of the AI models. • RMSE, MAE, and R2 were used for the validation of the models. • SVM is the best method in comparison to ANN and ANFIS methods for the prediction of Cc. [ABSTRACT FROM AUTHOR]

Maritime Border Collision is one of the vital concerns in coastal states since the maritime boundaries of any two countries cannot be identified easily during fishing. Maritime domain awareness and the border line control are the essential requirement which happens via recognition, and observing of boats inside their country boundary. It is necessary to identify the maritime border and alert the fisherman during the fishing. In this paper, we propose an Automatic Identification System (AIS) which can protect fishermen by notifying the country's border. If they are nearing towards the International Maritime Border Line (IMBL), an alert will be sent to coast guards via VHF set. Using the inbuilt GPS, AIS can find the location and transmits to the embedded systems, which gathers the recent position by comparing autonomy and longitudinal values with the existing assessment. The proposed system is validated under a case study in the maritime border between India and Sri Lanka, which is identified as Gulf of Mannar. It has been revealed that fishermen can aware that they are about to near the nautical border by means of visual and audio alert. Then, protectors in the coast preserve support and afford supplementary assist to those fishermen. This system also provides collision avoidance by using AIS/ ultrasonic sensors. It has better performance than the relevant methods such as RF (Charan et al. 2016), ECDIS (Vanparia and Ghodasara, International Journal of Computer Applications & Information Technology, 1:58–64, 2014), Android (Kumar et al. 2016), GSM and GPS (Sivagnanam et al., International Journal of Innovative Research in Advanced Engineering (IJIRAE), 2:124–132, 2015). [ABSTRACT FROM AUTHOR]

In this paper, we design and develop a new class of linear algebraic codes defined as soft linear algebraic codes using soft sets. The advantage of using these codes is that they have the ability to transmit m-distinct messages to m-set of receivers simultaneously. The methods of generating and decoding these new classes of soft linear algebraic codes have been developed. The notion of soft canonical generator matrix, soft canonical parity check matrix, and soft syndrome are defined to aid in construction and decoding of these codes. Error detection and correction of these codes are developed and illustrated by an example. [ABSTRACT FROM AUTHOR]

Image steganography plays a vital role in hiding significant secret information within any input image. Numerous steganography techniques have been carried out to hide the secret information in images. In the current scenario, frequency domain techniques are widely preferred which are mostly transform based approaches. Conventionally, the secret data is hidden randomly in the coefficients of the transformed image. However, such random data hiding techniques lead to inferior performance of the overall system. Thus, using an optimization algorithm is obligatory to find out the optimal coefficients. Genetic Algorithm (GA) is normally used for selecting the optimal transform coefficients to enhance the system performance. However, conventional GA based approaches are highly random in nature which again leads to inaccurate results. In this work, a modified GA approach was proposed to determine the optimal coefficients in order to improve the embedding capacity and stego image quality. The achieved average peak signal to noise ratio (PSNR) was 50.29 dB with embedding capacity of 139361 bits. These experimental results validate the practical feasibility of the proposed methodology for image steganography. [ABSTRACT FROM AUTHOR]

The decision-making problems based on fuzzy inference systems have received much attention from the worldwide scientific community. The M-CFIS-FKG model is considered one of the best models to solve classification problems based on uncertain and amplitude input datasets. It can infer and find the output labels of new samples that are not in the fuzzy rule base. Recently, the FKG-Pairs model has been considered an extension of FKG in the M-CFIS-FKG model by combining attribute pairs to infer and find the output labels in the cases of input datasets with incomplete gathering. It has overcome the limitation of the M-CFIS-FKG model. However, with real-time large input datasets or too-small training datasets, the FKG-Pairs model has also revealed limitations as it takes too much time to compute, and the accuracy is still relatively low. This paper has proposed a decision-making model in extreme cases (called FKG-Extreme) by using the Q-learning-based FKG-Pairs approach to enrich the fuzzy rule base after each time step with the cumulative mechanism of new rules. The proposed FKG-Extreme model has overcome the FKG-Pairs model's limitation in the extreme case. It has significantly improved the system's accuracy, while the computation time is acceptable. To validate the proposed model, we conducted experiments based on the standard UCI datasets, and the experimental results demonstrated that the system's performance in terms of accuracy is superior to the other reliable models in case of too small training data. Furthermore, the results of the two-way ANOVA also proved that the FKG-Extreme model is better than the FIS and FKG-Pairs models. [ABSTRACT FROM AUTHOR]

• Prediction of soil consolidation coefficients (Cv) was done using Artificial Intelligent (AI). • Monte Carlo simulations were also performed estimate the robustness of the AI models. • RMSE, MAE, and R2 were used for validation of the models. • Novel ANN-BBO model was better compared to others for predicting the Cv. Coefficient of consolidation (Cv) is an important parameter in the designing of civil engineering structures founded on soil. Determination of the Cv in the laboratory is beset with complexity, therefore several attempts have been made to correlate it with the index properties of soil. In this paper, various advanced soft computing approaches namely Biogeography-Based Optimization based Artificial Neural Networks (ANN-BBO), Artificial Neural Networks (ANN), Adaptive Network based Fuzzy Inference System (ANFIS), and Support Vector Machines (SVM) were applied for quick and accurate prediction of the Cv of soft soil. For this, data of engineering properties of soil of Ha Noi–Hai Phong highway project of Vietnam was utilized as a case study for training and validating the models. Data pre-processing techniques namely correlation matrix and Principal Component Analysis (PCA) were applied in order to identify relevant variables for reducing data dimension while doing predictive analysis. Validation of the models was performed using statistical criteria namely Coefficient of determination (R2), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Monte Carlo simulation method was performed to estimate the robustness of the models. Comparison of results of these models indicated that all the studied models performed well but performance of the ANN-BBO model (R2 = 0.965, RMSE = 0.149, and MAE = 0.108) is the best in predicting the Cv of soil compared with other models such as ANFIS (R2 = 0.921, RMSE = 0.222, and MAE = 0.182), ANN (R2 = 0.922, RMSE = 0.302, and MAE = 0.178), and SVM (R2 = 0.949, RMSE = 0.199, and MAE = 0.112). Therefore, ANN-BBO can be used for better prediction of the Cv based on limited engineering parameters of soil. [ABSTRACT FROM AUTHOR]

Complex fuzzy theory has strong practical background in many important applications, especially in decision-making support systems. Recently, the Mamdani Complex Fuzzy Inference System (M-CFIS) has been introduced as an effective tool for handling events that are not restricted to only values of a given time point but also include all values within certain time intervals (i.e., the phase term). In such decision-making problems, the complex fuzzy theory allows us to observe both the amplitude and phase values of an event, thus resulting in better performance. However, one of the limitations of the existing M-CFIS is the rule base that may be redundant to a specific dataset. In order to handle the problem, we propose a new Mamdani Complex Fuzzy Inference System with Rule Reduction Using Complex Fuzzy Measures in Granular Computing called M-CFIS-R. Several fuzzy similarity measures such as Complex Fuzzy Cosine Similarity Measure (CFCSM), Complex Fuzzy Dice Similarity Measure (CFDSM), and Complex Fuzzy Jaccard Similarity Measure (CFJSM) together with their weighted versions are proposed. Those measures are integrated into the M-CFIS-R system by the idea of granular computing such that only important and dominant rules are being kept in the system. The difference and advantage of M-CFIS-R against M-CFIS is the usage of the training process in which the rule base is repeatedly changed toward the original base set until the performance is better. By doing so, the new rule base in M-CFIS-R would improve the performance of the whole system. Experiments on various decision-making datasets demonstrate that the proposed M-CFIS-R performs better than M-CFIS. [ABSTRACT FROM AUTHOR]

Traditional agricultural systems require huge amount of power for field watering. This paper proposes a smart irrigation system that helps farmers water their agricultural fields using Global System for Mobile Communication (GSM). This system provides acknowledgement messages about the job's statuses such as humidity level of soil, temperature of surrounding environment, and status of motor regarding main power supply or solar power. Fuzzy logic controller is used to compute input parameters (e.g. soil moisture, temperature and humidity) and to produce outputs of motor status. In addition, the system also switches off the motor to save the power when there is an availability of rain and also prevents the crop using panels from unconditional rain. The comparison is made between the proposed system, drip irrigation and manual flooding. The comparison results prove that water and power conservation are obtained through the proposed smart irrigation system. Image 1082 [ABSTRACT FROM AUTHOR]

Dynamic multi-criteria decision-making (DMCDM) models have many meaningful applications in real life in which solving indeterminacy of information in DMCDMs strengthens the potential application of DMCDM. This study introduces an extension of dynamic internal-valued neutrosophic sets namely generalized dynamic internal-valued neutrosophic sets. Based on this extension, we develop some operators and a TOPSIS method to deal with the change of both criteria, alternatives, and decision-makers by time. In addition, this study also applies the proposal model to a real application that facilitates ranking students according to attitude-skill-knowledge evaluation model. This application not only illustrates the correctness of the proposed model but also introduces its high potential appliance in the education domain. [ABSTRACT FROM AUTHOR]

Decision-making activities are prevalent in human life. Many methods have been developed to address real-world decision problems. In some practical situations, decision-makers prefer to provide their evaluations over a set of criteria and weights. However, in many real-world situations, problems include a lack of weight information for the times, criteria, and decision-makers (DMs). To remedy such discrepancies, an optimization model has been proposed to determine the weights of attributes, times, and DMs. A new concept related to the correlation measure and some distance measures for the dynamic interval-valued neutrosophic set (DIVNS) are defined in this paper. An extend Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method in the interval-valued neutrosophic set with unknown weight information in dynamic neutrosophic environments is developed. Finally, a practical example is discussed to illustrate the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Similarity plays a significant implicit or explicit role in various fields. In some real applications in decision making, similarity may bring counterintuitive outcomes from the decision maker's standpoint. Therefore, in this research, we propose some novel similarity measures for bipolar and interval-valued bipolar neutrosophic set such as the cosine similarity measures and weighted cosine similarity measures. The propositions of these similarity measures are examined, and two multi-attribute decision making techniques are presented based on proposed measures. For verifying the feasibility of proposed measures, two numerical examples are presented in comparison with the related methods for demonstrating the practicality of the proposed method. Finally, we applied the proposed measures of similarity for diagnosing bipolar disorder diseases. [ABSTRACT FROM AUTHOR]

• We approached game theory for security & reliability of data transmission in MANET. • A security-aware routing scheme using repeated game model (SAR-RG) was given. • It detects malicious nodes for preventing packet drop and DOS attack in the network. • It was validated on network simulator with 100 nodes in a 500 m × 500 m region. • The proposed SAR-RG scheme reveals better outcomes than related schemes. Security is a crucial factor in Mobile Ad hoc Networks (MANET) where inaccessible environments cause assorted categories of security intimidations to provide the malicious-free network. Game theory plays a crucial part in scheming well-organized security methodologies in computer networks. In this work, an Infinitely-repeated game and cooperation approach is designed to detect malicious nodes and also to enhance energy-efficiency. The goal of an Infinitely-repeated game aims to ensure the defector or malicious node in the network has a short run gain and a long-run loss. The prime objective of the proposed scheme is to attack detection and defense, accuracy for preventing the packet drops. The proposed method is verified via simulation. [ABSTRACT FROM AUTHOR]

This paper presents a system dedicated to monitoring the heart activity parameters using Electrocardiography (ECG) mobile devices and a Wearable Heart Monitoring Inductive Sensor (WHMIS) that represents a new method and device, developed by us as an experimental model, used to assess the mechanical activity of the hearth using inductive sensors that are inserted in the fabric of the clothes. Only one inductive sensor is incorporated in the clothes in front of the apex area and it is able to assess the cardiorespiratory activity while in the prior of the art are presented methods that predict sensors arrays which are distributed in more places of the body. The parameters that are assessed are heart data-rate and respiration. The results are considered preliminary in order to prove the feasibility of this method. The main goal of the study is to extract the respiration and the data-rate parameters from the same output signal generated by the inductance-to-number convertor using a proper algorithm. The conceived device is meant to be part of the "wear and forget" equipment dedicated to monitoring the vital signs continuously. [ABSTRACT FROM AUTHOR]