MOHAMMAD ATIQUE
@sgbau.ac.in
Professor Computer Science & Engineering
Sant Gadge Baba Amravati University, Amravati
RESEARCH INTERESTS
Soft Computing, Artificial Intelligence, Machine Learning
Scopus Publications
Scopus Publications
Aniket K. Shahade, K.H. Walse, V.M. Thakare, and Mohammad Atique
Elsevier BV
Md Saifuddin Ahmed Atique and Cai Xia Yang
American Society of Mechanical Engineers
Abstract Energy harvesting from vibration sources was a very promising field of research throughout the last few decades among the engineers and scientist as considering the necessity of renewable/green energy for the welfare of mankind. Unused vibration energy exists in the surrounding or machineries was always tried to be utilized. Since then, by using piezoelectric transduction, researchers started to harvest the vibration energy. However, after the invention of piezo ceramics Macro Fiber Composites (MFC) by NASA, the research in this field augmented a lot due to its high efficiency to convert mechanical strain or vibration to useful electrical power and vice versa. Apart from energy harvesting researcher concentrated to utilize this harvested energy for daily life and hence application of this harvested energy for structural health monitoring inaugurated. Recent study showed that, the vibration energy harvested from the vehicles or aerospace (UAV) structure is good enough to power its onboard structural health monitoring unit though for feeding this power to any other onboard electrical system is still challenging due to low power generation along with its random production. Moreover, Macro Fiber Composites (MFC) can be used as an actuator to change the shape of aircraft wing to enhance aerodynamic performance and hence, application of MFC for wing morphing design has become popular throughout these years. The purpose of this research work is to depict the recent progress & development that took place in the field of energy harvesting & wing morphing research using macro fiber composites and combining the existing knowledge continue the work further, the future of this harvested energy, new design concept & upcoming challenges along with its possible solution. This work investigates the different configuration of macro fiber composites (MFC) for piezoelectric energy harvesting and its contribution for wing morphing design with enhanced aerodynamics. For the first part of this work, uniform MFC configuration was modeled and built up based on the Euler-Bernoulli beam theory. When the governing differential equations of the systems were derived, by applying the harmonic base excitation, coupled vibration response and the voltage response were obtained. The prediction of the mathematical model was at first verified by unimorph MFC with a brass substrate obtained from the state of art and then validation was justified by MFC unimorph along with three different substrate materials (copper, zinc alloy & galvanized steel) and thickness for the first time in this type of research. Computational & analytical solution revealed that, among these three substrates and for same thickness, maximum peak power at resonance excitation was obtained for the copper substrate. For the second part of the study (i) computational analysis was performed and the output was compared with the real time data obtained from the wind tunnel experiment and the conclusion stood that, with the increment of the incoming flow velocity, the power output from the MFC increases with a thin airfoil made of copper substrates and two MFC on its upper surface (ii) wing morphing design was performed for a NACA 0012 airfoil for the first time where macro fiber composite actuators were used to change the top and bottom surfaces of the airfoil with a view to recording the enhanced aerodynamics performance the designed morphing wing. CFD simulation results were compared with the wind tunnel testing data from the state of art for NACA 0014 for all identical parameters. The enhanced aerodynamics performance observed for designed wing morphing can be used for future concepts like maneuvering of the aircraft without the help of ailerons or for the purpose of active flow control over the aircraft wing.
Md Saifuddin Ahmed Atique and Cai Xia Yang
American Society of Mechanical Engineers
Abstract Rotating machinery are widely used in industry. Machine failures may result in costly downtime. Without effective fault diagnosis, it is impossible to predict the lead-time to failure. Therefore, conducting effective fault detection and identification are desirable and imperative. However, diagnosing faults in rotating machinery is often a labor-intensive and time-consuming practice. This makes conducting effective and efficient fault diagnosis a challenge for technicians and plant maintainers. This paper presents a Lyapunov Exponents-Based K-Nearest Neighbor (LE-Based KNN) analysis method for detecting and classifying rotating machinery fault. To distinguish different machine conditions, Lyapunov exponents (LEs) calculated using nonlinear mapping were selected as features in KNN analysis for classifying the signature of different faults from the machine vibration. The LEs from forty-four vibration recordings served as training dataset for KNN analysis, and the machine health conditions were grouped into four categories. Data from unknown machinery health condition were used as testing data. The results show that proposed LE-Based KNN analysis method can achieve reliable fault classification on operating condition. The LE-Based KNN analysis method has potential improvement in classification with modified KNN and optimized feature selection. This approach can be used to acquire a complete machine vibration profile that may predict occurrence of damage in different parts of machine and help to detect and identify the defective machineries at early stages.
Nilima Dongre, Mohammad Atique, Zeba A Shaik, and Atul D Raut
IEEE
Technology advancements blend to masses with diverse and heterogeneous environments. The Internet of Things (IoT) and Cloud Computing has broken through reachability and availability of services. The security, privacy requirements and challenges manifest with the heterogeneity and the wide range of application domains posing a major concern in adoption of IoT. The literature addresses these changing requirements individually. The paper presents a top-down survey starting with general security challenges in IoT moving to security and privacy challenges, and later exploring security frameworks of IoT. The millions of wide range of devices produce a huge amount of data. Identifying and authenticating these devices and allowing them to process their data locally improves the performance, reduces the bandwidth usage, increases the battery usage and reduces the security risk of attacks making the devices self reliant. The survey identifies the less explored research gaps to group them and address the concerns related to device management, user management, key management, trust management and data management. The surveyed secured frameworks in IoT address the multiple dimensions of security concerns and their aspects.
Namrata Mahakalkar and Mohd. Atique
Springer International Publishing
Harshali P. Patil and Mohammad Atique
Mary Ann Liebert Inc
With the advent of the new information technologies, the growth of online reviews regarding an organization or a company or any other sector has been playing a vital role in improving the sector plans and decisions. The vast significance of the online reviews that determine the sentiment polarity is the hectic challenge of the current scenario. Sentiment classification is a process of classifying the text according to the sentimental polarities of opinions, which has positive or negative. Thus, this article concentrates on presenting a novel method, named CAVIAR-Dragonfly optimization with Extended Naive Bayes (CDNB), for performing sentiment classification and affective state classification. At first, the BITS review from Twitter is subjected to preprocessing, which includes stop word removal and stemming. Then, the next step is the feature extraction, in which all the reviews are converted to a feature vector. After that, all the individual feature vectors are collected to form the feature matrix, which is applied to the proposed C-Dragonfly optimization algorithm, to perform the sentiment classification and affective state classification. The performance of the proposed method is analyzed using the Twitter Sentiment Analysis Training Corpus Data Set based on true positive rate (TPR), true negative rate (TNR), and accuracy. From the analysis, it can be shown that the proposed method yields the maximum TPR, TNR, and accuracy of 89.0934%, 72.3064%, and 79.3591% for sentiment classification and 84.2122%, 66.2187%, and 76.6249% for the sentiment affective state classification.
Mohammad Sharfoddin Khatib and Mohammad Atique
Springer Science and Business Media LLC
A Real-Time Database System is referred to as a transaction execution system which is administered to manage different workloads. To store and to retrieve data onto distinctive application services database systems are utilized. Unfortunately, in most situations, Quality of Service (QoS) and security are detected separately. In order to overcome the issues an efficient real-time database system is introduced, which mainly focused on the optimization of QoS requirement (response time) as well as security strength by arbitrarily changing the security mechanisms (IDPSs) based upon the request from users. To achieve this goal, here we propose a Firefly Gravitational Search Algorithm (FGSA) to optimize the user requesting security policies under different workloads (number of user requests). The database system suffers from intrusion (attack) due to more number of users requesting for the security policies at a time. Due to this, the security of the database system may be decreased. To enhance this, combination (mix) of IDPSs (Intrusion Detection Prevention Systems) is utilized. In our environment, the number of used IDPSs is adopted to represent the effectiveness of the database system. From the simulation result, it is verified that FGSA shows better performance when compared to the existing algorithms in terms of the security strength, response time, IGD as well as with the fitness measure.
Santosh Paudyal, Md Saifuddin Ahmed Atique, and Cai Xia Yang
IEEE
Rotating machinery are continuously operated tools for power generation and mechanical applications. The smooth operation of these tools is fundamental in businesses to accomplish their profitability. The status of such machines can be monitored by continuously assessing their working parameters that aid to identify abnormal behaviors. Upon detection of abnormal behaviors, such machines can be early scheduled for maintenance. Conditioned Based Maintenance (CBM) is one such approach that continuously monitors the machine and recommends taking action before equipment fails. This approach uses monitoring parameters such as temperature, pressure, and vibration signals to minimize unnecessary breakdown and catastrophic failure. Vibration parameters based machine fault identification approach is one of the widely used methods in CBM. Various machine malfunctions can be predicted and detected based on energy at specific frequencies of vibration signals. Common faults such as unbalanced and misalignment are often identified by examining the operating frequencies and their harmonics. Based on machine malfunctions, the dominant differences are expected at these frequencies. Barring few dominating peaks, other peaks are not exactly located at harmonic speed, so it can be misleading to use information from only harmonic speed. Rather than just examining operating frequencies and their harmonics, we looked at the local peaks and identified their frequencies. The acceleration amplitude at the operating speed and the local maximum acceleration amplitude are selected as a vibration feature for the fault classification. The proposed KNN classifier demonstrated its reliability with an accuracy of over 96% for the tested data set of 25 samples.
Mahesh Lokhande and Mohammad Atique
Inderscience Publishers
Harshali P. Patil and Mohammad Atique
Springer Science and Business Media LLC
Sentiment analysis is the critical process, which generates the subjective information from the text documents that are available online. Literature presents various kinds of task, like sentiment classification, affect classification, reason identification, and predictive analysis and so on, for sentiment analysis. This work brings the reason identification system from the classified sentiment and the affect classes through the automation of the optimization techniques. The proposed adaptive autoregressive conditional autoregressive value at risk-dragonfly optimization with Naive Bayes (AA-CDNB) algorithm finds the reasons behind the sentiments present in the tweets by joining the dragonfly algorithm and the Naive Bayes classifier. Also, the proposed model utilizes the tangential weighted moving average (TWMA) model, for predicting the sentiment reasons to appear shortly. The experimentation of the proposed work utilizes the BITS PILANI tweets database for the simulation and further, the results are compared with various models. The proposed AA-CDNB model has outclassed other models with the values of 1, 0.888, and 0.920, for the sensitivity, specificity, and accuracy metrics, respectively. Also, the results of the TWMA prediction model is compared with the other models based on the error performance, and it is proved that the TWMA model has improved results.
M S Khatib and Mohammad Atique
IEEE
It has been growing interest in the performance of management of transaction systems that have significant response time requirements. These requirements are usually specified as hard or soft deadlines on individual transactions and a concurrency control algorithm must attempt to meet the deadlines as well as preserve data consistency in real-time distributed database. This paper proposes a class of simple and efficient Adaptive EndState Concurrency Control Algorithms in which the scheduling of a transaction system is improved by ending transactions that introduce excessive slab. We consider different levels of the ending relationship among transactions and evaluate the impacts of the termination of relationship when the relationship is built in an online or offline fashion. We measure endstate overheads on a system running the Linux real time operating system with distributed environment. The strengths of the work are demonstrated by improving the worst case scheduling of a various example and randomly generated a different numbers of transaction sets.
Pallavi Vijay Chavan and Mohammad Atique
Springer Singapore
In this paper, we consider the novel type of visual cryptography scheme, which can decode concealed images without any cryptographic computations. The hierarchical visual cryptography scheme is perfectly secure and very easy to implement. It divides secret in number of pieces called shares. We extend this scheme with variant of threshold λ. Shares generated out of this scheme are expansionless and capable to reconstruct high-contrast secret. The approach associates the shares of hierarchical visual cryptography using Euclidean distance measure. This visual cryptographic scheme superimposes the shares to decode the secret using minimum distance association.
Prajkta Akre, Harshali Patil, Anand Khandare, and Mohammad Atique
IEEE
Opinions are plays very important role in today's life for purchasing any item or product. This opinion contains lots of information which can be useful for future systems. Opinion mining is an emerging field which combines lots of tasks to find the correct Opinion Words and Targets which describes the product or express the sentiment towards product. In this paper our main focus is on Topical Relation, which shows the relation between opinion target and opinion words. These topical relations are useful to gain the idea of which topics are mostly discussed by customers for particular product. We will also calculate the sentiment of customer reviews which classifies into three categories positive, negative and neutral.
Sangeeta Vhatkar, Samreen Shaikh, and Mohammad Atique
IEEE
One of the recent trends in wireless networking is Wireless sensor network (WSN), in this type of network tiny electronic device sensors organized in an environment where human intervention is problematic. In WSN, various sensors organized in a sensing environment works together in coordination to monitor and control the physical property of an environment. To efficiently perform the given task while keeping the longer network lifetime, WSN requires Energy efficient routing protocol. In this paper two hierarchical routing protocols ECHERP (Equalized Cluster Head Election Routing Protocol) and PDCH (PEGASIS with Double Cluster head) has been discussed and performance of both the protocols is analyzed based on various QoS parameters like Delay, Throughput, Packet Drop Ratio and Energy consumption.
Sangeeta Vhatkar, Archana Nanade, and Mohammad Atique
IEEE
This paper focuses on variants of PEGASIS and CBR routing protocols, PDCH and MBC, which are decedents of Hierarchical routing protocols. Hierarchical routing protocols are best known for energy efficiency. PDCH is a routing protocol with double cluster head. It elects its cluster head based on the distance from base station. In MBC the cluster head is selected based on the residual energy and mobility. A non-cluster head has to transmit data before its transmission power is finised to the cluater head so with in the TDMA schedule data should be send. Here simulation were conducted in NS2 Software. The QoS parameters that were compared were Delay, Throughput, Packet drop ratio and Energy consumed. From the simulation it was clear that PDCH outperformed MBC by 15.13%, 4.87%, 14.29% and 23.19% for the above parameters.
Pradnya Kumbhar, Manisha Mali, and Mohammad Atique
IEEE
The rapid growth of World Wide Web has resulted in massive information from varied sources rising at an exponential rate. The high availability of such disparate information has precipitated the need of automatic text categorization for managing, organizing huge data and knowledge discovery. Main challenges of text classification include high dimensionality of feature space and classification accuracy. Thus, to make classifiers more accurate and efficient, there arises the need of Feature Selection. Genetic algorithms have gained much attention over traditional methods due to its simplicity and robustness to solve the optimization problem and high exponential search ability. Thus, the paper focuses on using Genetic Algorithm (GA) for Feature Selection to obtain optimal features for classifying unstructured data. We build a fuzzy rule-based classifier that automatically generates fuzzy rules for classification. The experiments are conducted on two datasets namely 20-Newsgroup and Reuters-21578 and the results indicate that GA outperforms Principal Component Analysis (PCA).
Mohammad Atique and Amol P. Bhagat
Informa UK Limited
ABSTRACT Utilization of online medical images was limited due to the lack of effective search methods, and text-based searches have been a dominating approach for the medical database management. Medical images of various modalities cannot be effectively indexed or organized with traditional text-based retrieval techniques. This has led to the use of image content for the processing and organizing the database, constituting the so-called content-based image retrieval systems. This paper presents two proposed methods, namely fuzzy connectedness image segmentation with geometric moments (FCISGMs), and localized entropy-based medical image retrieval (LEBIR) for retrieval of Digital Imaging and Communications in Medicine images. FCISGM exploits shape features for precise image retrieval by using fuzzy connectedness image segmentation. LEBIR uses localized entropy for minimizing number of computation which results in efficient image retrieval. Experimental evaluation reveals that the proposed methods outperform the existing methods in terms of precision and recall.
Amol P. Bhagat and Mohammad Atique
IGI Global
This chapter presents novel approach fuzzy connectedness image segmentation with geometric moments (FCISGM) for digital imaging and communications in medicine (DICOM) image mining. As most of the medical imaging data is exchanged in DICOM format, this chapter focuses on the various methodologies available for DICOM image feature extraction and mining. The comparison of existing medical image mining approaches with the proposed FCISGM approach is provided in this chapter. After carrying out exhaustive results it has been found that proposed FCISGM method gives more precise results and requires minimum number of computations compare to other medical image mining approaches resulting in improved relevant outcomes.
Harshali P. Patil and Mohammad Atique
IEEE
In the past years, the World Wide Web (WWW) has become a huge source of user-generated content and opinionative data. Using social media, such as Twitter, Facebook, etc. user share their views, feelings in a convenient way. Social media, such as Twitter, Facebook, etc, where millions of people express their views in their daily interaction, which can be their sentiments and opinions about particular thing. These ever-growing subjective data are, undoubtedly, an extremely rich source of information for any kind of decision making process. To automate the analysis of such data, the area of Sentiment Analysis has emerged. It aims at identifying opinionative data in the Web and classifying them according to their polarity, i.e., whether they carry a positive or negative connotation. Sentiment Analysis is a problem of text based analysis, but there are some challenges that make it difficult as compared to traditional text based analysis This clearly states that there is need of an attempt to work towards these problems and it has opened up several opportunities for future research for handling negations, hidden sentiments identification, slangs, polysemy. However, the growing scale of data demands automatic data analysis techniques. In this paper, a detailed survey on different techniques used in Sentiment Analysis is carried out to understand the level of work
Manish Rana and Mohammad Atique
Elsevier BV
Mohammad Atique
IEEE
In heterogeneous computing systems MinMin and MaxMin are widely used in assigning independent tasks to processors. For N tasks to be assigned to N processors these approaches are known to run in O (KN2) time. An algorithmic improvement that asymptotically decreases the running time complexity of MinMin to O(KN logN) without affecting its solution quality is proposed in [1]. The newly proposed MinMin algorithm is combined with MaxMin, resulting in two hybrid algorithms [1]. The first hybrid algorithm address the drawback of MaxMin in solving problem instances with highly skewed cost distributions while also improving the running time performance of MaxMin. The second hybrid algorithm improves the running time performance without degrading its solution quality. The proposed algorithms are easy to implement. For the large datasets used in the various experiments, MinMin and MaxMin, as well as recent state-of-the-art heuristics, require days, weeks, or even months to produce a solution, whereas the proposed algorithms in this paper produce solutions within only two or three minutes. The new modified algorithms namely MinMax and MinMax+ are proposed and implemented. These algorithms are compared with the existing algorithms MinMin and MaxMin on single objective cases.
Mahesh Lokhande and Mohammad Atique
IEEE
In today's scenario, lots of real-time applications are encountered which includes numerous computations that have to be done in parallel in stipulated period of time. For such tasks single core processors cannot perform well even multiprocessors are not able to, because traditionally, sequential models were used which ignore parallelism between the tasks. However, parallel models have the capability to parallelize specific segments of tasks, thereby leading to shorter response times as possible. In this paper, the problem of scheduling the real time, periodic, implicit deadline nested-parallel tasks on multi-core processors is discussed. Initially, a nested-parallel task model, which is synchronous in nature wherein each task consists of number of segments and each segment consists number of parallel or nested threads, is considered. After, a novel task disintegration technique that disintegrates each parallel or nested-parallel task into a set of sequential tasks is proposed. Here it is proved that the proposed task disintegration technique achieve a resource augmentation bound of 3.414 when scheduled disintegrated tasks using global EDF scheduling, which means, if any task set is feasible on m unit speed processors, it can be scheduled using proposed technique on m 3.414 times faster processors.