Recent Patents on Engineering - Volume 15, Issue 2, 2021

Background: Patents suggest that groundwater contaminated with chemicals, bacteria, oils or gases etc. leads to many types of diseases in people. Fresh and clean water plays a significant role in human life. In this study, water samples were collected from different regions of the Kadapa district, Andhra Pradesh. Methods: Water samples were collected in plastic bottles with a tight cap washed with distilled water. Totally, 57 samples were collected and analyzed in the laboratory for physicochemical properties like EC (Electrical Conductivity), pH, TH (Total Hardness), Total Dissolved Solids (TDS),Ca, Cl and F. In this paper, K-means clustering, K-Mediods clustering and Hierarchical clustering methods are used to group the collected regions of water samples based on the water quality. Later outlier analysis was carried out and various interesting patterns were identified. Results: According to the WQI values calculated, all the collected samples were suitable for drinking purpose. According to WQI values calculation, for the collected water sample data, it contained 13 poor tuples, 13 good tuples and 31 excellent tuples. According to K-means clustering, 3 clusters were observed with sizes 8, 17, 32. According to Outlier analysis, the samples from region Pullareddypet (sample No. 7) had the highest EC, TH and TDS values among the 57 collected water samples. The samples from region Veerapalli (Sample No. 37) had the highest fluoride value 3.58 among all 57 samples collected. Conclusion: Unsupervised learning methods such as K-Means Clustering, K-Mediods clustering and Hierarchical clustering methods are described for collecting data regarding the collected water samples’ physico-chemical parameters. The cluster analysis results were compared with WQI values calculated. The three clusters overlapped with each other with a small degree. In the study area, for drinking purpose, only excellent, good, poor category tuples were found. Later, outlier analysis has been described using Box plot method and K-means clustering method. By using outlier analysis using K-means clustering, various interesting hidden patterns from the data were extracted.

Background: Diagnosing diseases is an intricate job in medical field. Machine learning when applied to health care is capable of early detection of disease which would aid to provide early medical intervention. In heart disease prediction, machine learning techniques have played a significant role. Analysis of disease has become vital in health care sectors. The massive data collected by healthcare sectors are preprocessed and analyzed to discover the underlying information in the data for effective decision making and to provide proper medical intervention. The success of machine learning in medical industry is its capability in analyzing the huge amount of data gathered by the health sector and its effectiveness in decision making. Since medical field involves too many manual processes it has become necessary to automate these procedures. Remarkable advancements in electronic medical records have made it possible. Diagnosing diseases is an intricate job in medical field. Objective: The objective of this research is to design a robust machine learning algorithm to predict heart disease. The prediction of heart disease is performed using Ensemble of machine learning algorithms. This is to boost the accuracy achieved by individual machine learning algorithms. Methods: Heart Disease Prediction System is developed where the user can input the patient details and the prediction for the particular patient is made using the model developed. The model will predict the output to be either normal or risky. Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), Support Vector Machines (SVM), K-Nearest Neighbors (KNN) and Naïve Bayes classifier are used as base learners. These algorithms are combined using random forest as the meta classifier. Results: The predictions of classifier are combined using random forest algorithm. The accuracy is lifted from 85.53 % to 87.64 % which is an impressive improvement on accuracy. Conclusion: Various techniques were adopted to preprocess the data to suite the requirement of analysis. Feature selections were made to optimize the performance of machine learning algorithms. Ensemble prediction gave better accuracy when combined using Random forest algorithm as combiner. Better feature selection techniques can be applied to further improve the accuracy.

Background: Hyperspectral (HS) image data comprises of tremendous amount of spatial and spectral information which offers feature identification and classification with high accuracy. As part of the Deep Learning (DL) framework Stacked Autoencoders (SAEs) has been successfully applied for deep spectral features extraction in high dimensional data. HS deep image feature extraction becomes complex and time consuming due to the hundreds of spectral bands available in the hypercubes. Methods: The proposed method aims condense the spectral-spatial information through suitable feature extraction and feature selection methods to reduce data dimension to an appropriate scale. Further, the reduced feature set is processed by SAE for final feature representation and classification. Results: The proposed method has resulted in reduced computation time by ∼ 300s and an improvement in classification accuracy by ~15% as compared to uncondensed spectral-spatial features fed directly to SAE network. Conclusion: Future research could explore the combination of most state-of-the art techniques.

Background: Trustful message transmission within the Vehicular ad-hoc Networks is needed as traffic related safety applications needs successful and reliable delivery of messages. The biggest issue is how only the trustworthy parties can be retained and the misbehaved are revoked. From the background analysis, it gives a detailed view of various vulnerabilities in these networks and the techniques used by the researchers to identify and mitigate the attack. Based on the drawbacks observed in the literature the proposed Bayesian Game Mechanism has been designed. Objective: The major objective of this manuscript is to identify the Denial of Service Attack in Vehicular Ad-hoc Networks as it depletes availability of the resources. This attack is identified using Bayesian Game approach. Methods: The Bayesian game approach is used to identify the attack. It analyzes the behavior of vehicles and classifies them into trustworthy or malicious. Results: The simulation is conducted using Network simulator 2.34 and the traffic model is designed using by considering 100 nodes. From the results it is inferred that Packet drop ratio has been improved by 9.82 % and the delay and throughput has been minimized, when the proposed mechanism is used in the presence of attackers. Conclusion: This paper identifies Denial of Service attack as the most vulnerable attack in this network and has designed game theoretic approach namely Bayesian approach for preventing this attack. The proposed method has minimized the delay and packet drop and improved the throughput when compared against the bench mark.

Background: With the advent of IoT, the deployment of batteries with a limited lifetime in remote areas is a major concern. In certain conditions, the network lifetime gets restricted due to limited battery constraints. Subsequently, the collaborative approaches for key facilities to reduce the constraint demands of the current security protocols. Objective: This work covers and combines a wide range of concepts linked by IoT based on security and energy efficiency. Specifically, this study examines the WSN energy efficiency problem among IoT devices and security for the management of threats in IoT through collaborative approaches and finally outlines the future. The concept of energy-efficient key protocols which clearly cover heterogeneous IoT communications among peers with different resources has been developed. Because of the low capacity of sensor nodes, the energy efficiency in WSNs has been an important concern. Methods: In this paper, we present an algorithm for Artificial Bee Colony (ABC) which reviews security and energy consumption to discuss their constraints in the IoT scenarios. Results: The results of a detailed experimental assessment are analyzed in terms of communication cost, energy consumption and security, which prove the relevance of a proposed ABC approach and a key establishment. Conclusion: The validation of DTLS-ABC consists of designing an inter-node cooperation trust model for the creation of a trusted community of elements that are mutually supportive. Initial attempts to design the key methods for management are appropriate individual IoT devices. This gives the system designers, an option that considers the question of scalability.

Background: The Modified Intelligent Water Drop algorithm incorporated with the proposed heuristic function to enhance the characteristics of randomness, individual diversity to minimize the total energy required to broadcast the data from each sensor node towards the sink node in a network. Objective: The Modified Intelligent Water Drop Algorithm has been designed to achieve the divergence to find out an optimal Minimum Energy Broadcasting tree in WSN. Methods: The proposed variant has been evaluated and compared concerning contemporary Evolutionary techniques using appropriate performance criteria. Results: To achieve optimum result, the proposed Modified Intelligent Water Drop algorithm compared with existing algorithm along with 20 nodes dataset with 30 instances, 50 nodes dataset with 30 instances and 100 nodes dataset with 30 instances. Conclusion: In this perspective, a suitable experimental setup has been designed and experiments are performed on different classes of Minimum Energy Broadcasting instances obtained from standard Minimum Energy Broadcasting library [Comopt 2012] to validate the proposed Modified Intelligent Water Drop Algorithm. The simulation results of MEB for MIWD-HUD with convergence and divergence is given.

Background: Agile is not a methodology neither can it be considered as a peculiar way of developing any software also it is neither a framework nor a process. Agile is a mindset or a collection of beliefs that can be used by the teams for taking the decisions while working on any software development. Agile mindset adopted for the development of software has gained attention of the researchers and industries across the world because otherwise the software project would turn out to be uncertain and very turbid. The universal remedy for the usual failure of the software project development is the agility. Actually saying agile is nothing novice instead it is a meta model based on best practices from the preceding models like waterfall, iterative, incremental and rapid application development method. Objective: The objective of this paper is to highlight various points of comparison between them and conclude that failing fast is failing cheap in case of agile for software development. Methods: So, we can say that an agile always existed but not in a structured and a formal manner. The two main methodologies of agile as an umbrella term are XP and scrum. Results: Thus in this paper we have discussed about scrum as a major methodology and also how various scrum roles contribute towards making teams self-organized to reduce the usual rate of failure of development projects. Conclusion: The success rate of the software applications developed through the agile concept is three times than that of the traditional waterfall method and also the percentage of cost and time overruns is much lower.

Background: Diabetic Retinopathy is the leading cause of vision impairment and its early stage diagnosis relies on regular monitoring and timely treatment for anomalies exhibiting subtle distinction among different severity grades. The existing Diabetic Retinopathy (DR) detection approaches are subjective, laborious and time consuming which can only be carried out by skilled professionals. All the patents related to DR detection and diagnoses applicable for our research problem were revised by the authors. The major limitation in classification of severities lies in poor discrimination between actual lesions, background noise and other anatomical structures. Methods: A robust and computationally efficient Two-Tier DR (2TDR) grading system is proposed in this paper to categorize various DR severities (mild, moderate and severe) present in retinal fundus images. In the proposed 2TDR grading system, input fundus image is subjected to background segmentation and the foreground fundus image is used for anomaly identification followed by GLCM feature extraction forming an image feature set. The novelty of our model lies in the exhaustive statistical analysis of extracted feature set to obtain optimal reduced image feature set employed further for classification. Results: Classification outcomes are obtained for both extracted as well as reduced feature set to validate the significance of statistical analysis in severity classification and grading. For single tier classification stage, the proposed system achieves an overall accuracy of 100% by k- Nearest Neighbour (kNN) and Artificial Neural Network (ANN) classifier. In second tier classification stage, an overall accuracy of 95.3% with kNN and 98.0% with ANN is achieved for all stages utilizing optimal reduced feature set. Conclusion: 2TDR system demonstrates overall improvement in classification performance by 2% and 6% for kNN and ANN respectively after feature set reduction, and also outperforms the accuracy obtained by other state of the art methods when applied to the MESSIDOR dataset. This application oriented work aids in accurate DR classification for effective diagnosis and timely treatment of severe retinal ailment.

Movies, advertisements, and promotional videos are Intellectual Property (IP) and are often shared on open access medium as part of the business. Fueled by the advancements in technology, piracy has become more rampant and a widely spread crime in many countries. Movie piracy is more dangerous to mega-budget producers who struggle to receive the maximum profit of their films, making them compete with smaller films, results in the cheapest alternatives, jeopardizing the production of more films, artistic pool, shooting sites, camera equipment, special effects, quality and much more. Apart from the big shrinking of the job market for film jobs, sponsorship, unions, and merchandising get affected greatly. Many advanced technologies, apart from watermarking, such as Infrared based techniques, are used to estimate the position of the pirate and reduce piracy. This paper reviews the enabling technologies in the fight against video piracy, focusing mainly on methods for stopping these emerging methods of piracy in theatres.

Background: Power failure and its related challenges is the cause of low economic development in most developing countries. Power failure is sometimes caused by the constant technical problems experienced by power network components which are being used for electric power transmission and distribution. Most distribution and transmission substations operate at medium voltages. Medium Voltage (MV) cables provide an efficient means of power distribution to customers. In recent times, medium voltage cables with cross-linked Polyethylene are most commonly used in modern power systems compared to the classical or paper-insulated cables due to their high resistance to Partial Discharge (PD), good electrical properties, and ability to operate at high temperature levels. However, during the process of transmission the deterioration of a cable is inevitable. This reduces the reliability and efficiency of the transmission and distribution process, which is of course a serious concern to power engineers and consumers. Owing to the challenges experienced from the degradation of MV XLPE cables, it has become highly imperative to investigate the causes of failure in power network cables used for the transmission and distribution of electric power. Methods: A comprehensive review was carried out to understand the fundamental causes of MV XLPE degradation. This was pivotal in adopting the right monitoring technique for the MV XLPE cable. Results: The results show that the major causes of MV network cable faults are attributed to either material related or external causes. It was also discovered that there are different fault rates for different MV cable types. Further discoveries show that the XLPE cables failure rate is minimal compared to the Nordic Oil-paper cables which are said to have an average faults occurrences detection of about 4-5 times higher than that of the XLPE cables. Oil paper cables experience high failure rates and in addition experience substantial defects in their cable joints. Conclusion: This paper presented a comprehensive review of the causes of MV XLPE cable degradation and proffers solutions to the lingering challenges associated with MV XLPE cables.

Objective: This paper provides the basics of Android malware, its evolution and tools and techniques for malware analysis. Its main aim is to present a review of the literature on Android malware detection using machine learning and deep learning and identify the research gaps. It provides the insights obtained through literature and future research directions which could help researchers to come up with robust and accurate techniques for the classification of Android malware. Methods: This paper provides a review of the basics of Android malware, its evolution timeline and detection techniques. It includes the tools and techniques for analyzing the Android malware statically and dynamically for extracting features and finally classifying these using machine learning and deep learning algorithms. Results: The number of Android users is increasing at an exponential rate due to the popularity of Android devices. As a result, there are more risks to Android users due to the exponential growth of Android malware. On-going research aims to overcome the constraints of earlier approaches for malware detection. As the evolving malware is complex and sophisticated, earlier approaches like signature-based and machine learning-based approaches are not able to identify it timely and accurately. The findings from the review show various limitations of earlier techniques, i.e. requirement of more detection time, high false-positive and false-negative rates, low accuracy in detecting sophisticated malware and less flexibility. Conclusion: This paper provides a systematic and comprehensive review on the tools and techniques being employed for analysis, classification and identification of Android malicious applications. It includes the timeline of Android malware evolution, tools and techniques for analyzing these statically and dynamically for the purpose of extracting features and finally using these features for their detection and classification using machine learning and deep learning algorithms. On the basis of the detailed literature review, various research gaps are listed. The paper also provides future research directions and insights that could help researchers to come up with innovative and robust techniques for detecting and classifying Android malware.

Background: In a wind farm, the wind speed of the downstream wind turbine will be lower than the wind speed of the upstream wind turbine due to the influence of the wake. Therefore, the wake of wind turbines is one of the uncertain factors predicting the annual power generation of the wind farms. The study on the wake can effectively improve the efficiency of power generation. The arrangement of vertical axis wind turbines in wind farms is rarely studied. Therefore, it is important to study the vertical layout of wind turbines under the influence of wakes to obtain the best layout and unit spacing. Objective: The objective of this study is to obtain the optimal layout and unit distance of wind turbines in Senegal wind farms by studying the arrangement of Senegal vertical axis wind turbines in wind farms. Methods: Based on the ANSYS CFX flow field calculation module, the fluid dynamics model of the Senegal fan was established and the flow field simulation analysis was carried out. Based on the Jensen wake model and its improved model, three layout methods for the wind farm wind turbines were proposed: two units were arranged in series, two units were arranged in parallel, and three units were staggered. Through the simulation model, the wind energy utilization coefficient and wind speed of the wind turbine in the wind farm were obtained. Results: The optimal separation distance between the units was analyzed from four different angles: wind energy utilization coefficient, torque analysis, downstream tail flow and wind speed cloud contour. Finally, based on the optimal arrangement and unit distance, a triangular staggered wind farm composed of 10 units was established, and the integrated flow field characteristics of the whole wind farm were simulated and analyzed. The integrated flow field wake characteristics of the wind farm were obtained. Conclusion: In all the three arrangements, the optimum distance between the units must be three times the diameter of the wind turbine. This arrangement ensures that most of the units are unaffected by the wake, the area affected by the low-velocity wake of the wind farm is small, and the area affected by the high-speed wake is large.

Background and Objective: VANET is an application used for the intelligent transportation system which improves traffic safety as well as its efficiency. We have reviewed the patents related to vehicular Ad-Hoc Network and their issue. To avoid road accidents a lot of information we need in advance. This paper has developed a framework which minimizes the possibilities of the black hole attack in VANET. According to us, there are two possible solutions for this purpose. The first is to see alternative routes for the same destination. The second compromises of exploiting the packet header's packet sequence number which is always included in each packet header. The second procedure is able to verify that 72% to 96% of route which is discovered depends on pause time t which is the minimum time for delay in the packet transition in the network when AODV routing protocol is used for packet transitions. Methods: In this approach we used twenty five nodes. In which two are source nodes, two are destination nodes and four are invaders. We analyses the effects of these invaders on the network and studied their behavior on the network on different time-period to analyses if invader is black hole invader or the invader is Gray hole. To calculate send packets, received packets, packet drop, packet drop fraction, end-to-end delay, AWK script is used. Results and Discussion: Through this work we simulate the result in the time frame of 100 ms manually and on graph the time frame is not available so the time frame is processed by trace graph accordingly. In the simulation we took 25 nodes initially and start the procedure to send the packets over nodes. At first packets are broadcasted to every node to find out the location of nodes and packets are dropped once the path is established and then the packets are transferred to the path established over network. Conclusion: VANET is seen as the future of the network, and the need to secure it is crucial for the safety of it from various attacks. A secured VANET is essential for the future of the network and also currently acquiring this network will also boost the possibility of VANET to develop and reduce the time of its implementation in the real world scenarios. In this work, we have designed a framework and analyzed it for the possible attacks by the black hole, and Gray Hole attacks and also effects of the attacks are recorded and studied by practically using it. After analyzing it’s concluded that the attacks can be implemented and detected over the network.