Sanyam Shukla

• Ph.D
• Professor (Assistant) at Maulana Azad National Institute of Technology

Recently, researchers have developed nonparallel variants of both extreme learning machine (ELM) and support vector machine (SVM) classifiers to improve learning speed and generalization. However, these nonparallel methods typically solve two SVM-type optimization problems. Thus far, no existing nonparallel ELM variants introduce nonparallel hyperp...

Classification algorithms design predictive models that classify data under one of the predefined categories. The data can be text, image, audio, video, or animation. The Data Complexity Metrics (DCM) gives insight into the different aspects of the data characteristics like data distribution, noise, overlap, and separability. Existing data complexi...

This paper examines the impact of rain on traffic sign recognition system, addressing one of the challenges posed by harsh environmental conditions like low lighting, extreme weather (rain,fog, snow) and reduced sign visibility. A novel system is proposed in this work, which is capable of handling three different rain types (drizzle, torrential, an...

Machine learning (ML) is the process of teaching a machine to understand and make decisions on real-world problems using an efficient set of algorithms. With almost every single task being automated, one such big leap in the fields of artificial intelligence and machine learning is the development of advanced driver assistance systems (ADAS). With...

Imbalanced classification is a challenging problem in the fields of machine learning and data mining. Cost-sensitive methods can handle this issue by considering different misclassification costs of classes. Various modifications of support vector machine (SVM) and extreme learning machine (ELM) have been proposed to handle the class imbalance prob...

In classification problems, detecting a skew class has extensively been studied in the machine learning community. Traditional extreme learning machine (ELM) algorithm becomes biased towards the majority class due to imbalance learning. To handle this problem, several extensions of ELM have been proposed such as variances-constrained weighted ELM (...

In machine learning, a problem is imbalanced when the class distributions are highly skewed. Imbalanced classification problems occur usually in many application domains and pose a hindrance to the conventional learning algorithms. Several approaches have been proposed to handle the imbalanced learning. For example, Weighted kernel-based SMOTE (WKS...

Imbalanced problems occur in real-world applications when the number of majority instances far exceeds the number of minority instances. Traditional extreme learning machine (ELM) classifier becomes biased towards the majority class due to imbalanced learning. To handle this inherent drawback, several modifications of ELM have been proposed such as...

Learning from the imbalanced problem is among the most attractive issues in the contemporary machine learning community. However, the extensive majority of attention in this domain is given to the two-class imbalanced problems, while their much more complex multiclass counterparts are comparatively unexplored. It has been shown (Huang et al. in IEE...

Imbalanced learning is one of the substantial challenging problems in the ield of data mining. The datasets that have skewed class distribution pose hindrance to conventional learning methods. Conventional learning methods give the same importance to all the examples. This leads to the prediction inclined in favor of the majority classes. To solve...

Many real-world applications are imbalance classification problems, where the number of samples present in one class is significantly less than the number of samples belonging to another class. The samples with larger and smaller class proportions are called majority and minority class respectively. Weighted extreme learning machine (WELM) was desi...

Class imbalance problem happens when the training dataset contains significantly fewer instances of one class compared to another class. Traditional classification algorithms like extreme learning machine (ELM) and support vector machine (SVM) are biased towards the majority class. They minimize the least squares error due to which the minority cla...

Many real-world applications suffer from the class imbalance problem, in which some classes have significantly fewer examples compared to the other classes. In this paper, we focus on online sequential learning methods, which are considerably more preferable to tackle the large size imbalanced classification problems effectively. For example, weigh...

Imbalanced learning is one of the substantial challenging problems in the field of data mining. The datasets that have skewed class distribution pose hindrance to conventional learning methods. Conventional learning methods give the same importance to all the samples. This leads to biased accuracy, which favors the majority classes. Several classif...

Class imbalanced learning is a well-known issue, which exists in real-world applications. Datasets that have skewed class distribution raise hindrance to the traditional learning algorithms. Traditional classifiers give the same importance to all the samples, which leads to the prediction biased towards the majority classes. To solve this intrinsic...

Extreme Learning machine (ELM) is emerged as an efficient fast learning classifier for real valued classification problems. Voting Based ELM, V-ELM uses majority voting based ensembling technique to further improve the performance of ELM. V-ELM gives better performance at the cost of increased computational and memory requirement. This paper extend...

Extreme Learning machine, ELM emerged as an efficient fast learning classifier for real valued classification problems. ELM suffers from the problem of instability due to random initialization of weights between the input and the hidden layer. Voting Based Extreme Learning Machine, VELM uses majority voting based ensembling technique to reduce this...

Many real-life problems can be described as imbalanced classification problems, where the number of samples belonging to one of the classes is heavily outnumbered than the numbers in other classes. The samples with larger and smaller class proportion are referred to as the majority and the minority class respectively. Traditional extreme learning m...

Class imbalance problem occurs when the training dataset contains significantly fewer samples of one class (minority-class) compared to another class (majority-class). Conventional extreme learning machine (ELM) gives equal importance to all the samples leading to the results which favor the majority-class. Numerous variants of ELM-like weighted EL...

This paper comments on the recently published article entitled "Traffic sign recognition using kernel extreme learning machines with deep perceptual features". This paper presents a disagreement on the claim of the above-mentioned article related to the reduction in computational complexity. This paper also suggests not to use a single notation for...

Extreme learning machine (ELM) is one of the foremost capable, quick genuine esteemed classification algorithm with good generalization performance. Conventional ELM does not take into account the class imbalance problem effectively. Numerous variants of ELM-like weighted ELM (WELM), Boosting WELM (BWELM) etc. have been proposed in order to diminis...

Wind turbine power curve provides technical specification of the wind turbine in the form of nominal wind power readings. This information may used to monitor the performance of the power system, estimate the power produced by the turbine, optimize the operational cost, and improve the reliability of the power system. However, this information is n...

Imbalance problem occurs when the majority class instances outnumber the minority class instances. Conventional extreme learning machine (ELM) treats all instances with same importance leading to the prediction accuracy biased towards the majority class. To overcome this inherent drawback, many variants of ELM have been proposed like Weighted ELM,...

Data preprocessing is an important step for designing classification model. Normalization is one of the preprocessing techniques used to handle the out-of-bounds attributes. This work develops 14 classification models using different learning algorithms for dynamic selection of normalization technique. This work extracts 12 data complexity measures...

Extreme Learning machine, ELM emerged as an efficient fast learning classifier for real valued classification problems. ELM suffers from the problem of instability due to random initialization of weights between the input and the hidden layer. Voting Based Extreme Learning Machine, VELM uses majority voting based ensembling technique to reduce this...

In paper, we have proposed a novel summarization framework to generate a quality summary by extracting Relevant-Informative-Novel (RIN) sentences from topically related document collection called as RIN-Sum. In the proposed framework, with the aim to retrieve user's relevant informative sentences conveying novel information, ranking of structured s...

This paper introduces a novel method based on auxiliary matrix to hide a text data in an RGB plane. To hide the data in RGB planes of image via scanning, encryption and decryption. To enhance the security, the scanning technique combines two different traversals – spiral and snake traversal. The encryption algorithm involves auxiliary matrix as a p...

Extreme learning machine (ELM) is a generalized single hidden layer feed forward network in which weights and biases between the input layer and hidden layer are randomly assigned whereas, the weights between the hidden layer and the output layer are analytically determined. The optimal number of hidden neurons in ELM is evaluated by varying the nu...

While training a model with data from a dataset, we have to think of an ideal way to do so. The training should be done in such a way that while the model has enough instances to train on, they should not over-fit the model and at the same time, it must be considered that if there are not enough instances to train on, the model would not be trained...

Email Print Request Permissions Fault detection in ball bearing has attracted attention of various researchers. Several Statistical features have been proposed and used by various researchers for fault detection in ball bearing. This work analyzes the importance of various available statistical features by different methods which includes graphical...

Extreme learning machine (ELM) is emerged as an effective, fast, and simple solution for real-valued classification problems. Various variants of ELM were recently proposed to enhance the performance of ELM. Circular complex-valued extreme learning machine (CC-ELM), a variant of ELM, exploits the capabilities of complex-valued neuron to achieve bet...

Extreme learning machine is state of art supervised machine learning technique for classification and regression. A single ELM classifier can however generate faulty or skewed results due to random initialization of weights between input and hidden layer. To overcome this instability problem ensemble methods can be employed. Ensemble methods may ha...

Voting based Extreme learning machine was recently proposed to reduce the error due to variance in Extreme Learning Machine. This paper further refines the algorithm by using entropy based ensemble pruning. Results obtained shows significant improvement in performance along with reduction in computational and storage requirement.

Extreme Learning Machine is a fast real valued single layer feed forward neural network. Its performance fluctuates due to random initialization of weights between input and hidden layer. Voting based Extreme Learning Machine, VELM is a simple majority voting based ensemble of Extreme learning machine which was recently proposed to reduce this perf...

In increasing trends of network environment every one gets connected to the system. So there is need of securing information, because there are lots of security threats are present in network environment. A number of techniques are available for intrusion detection. Data mining is the one of the efficient techniques available for intrusion detectio...

Due to continuous growth of the internet technology, there is need to establish security mechanism. So for achieving this objective various NIDS has been propsed. Datamining is one of the most effective techniques used for intrusion detection. This work evaluates the performance of unsupervised learning techniques over benchmark intrusion detection...

Abstract—Due to the increase in internet users, there is a rapid growth in spam e-mails. In recent years, kernel function have received major attention, particularly due to the increased popularity of Support Vector Machine. It is a best classifier forbinary classification. Kernel functions are used to map data intohigh dimensional feature space. I...

Clustering is the one of the efficient datamining techniques for intrusion detection. In clustering algorithm kmean clustering is widely used for intrusion detection. Because it gives efficient results incase of huge datasets. But sometime kmean clustering fails to give best result because of class dominance problem and no class problem. So for rem...

Due to fast growth of the internet technology there is need to establish security mechanism. So for achieving this objective NIDS is used. Datamining is one of the most effective techniques used for intrusion detection. This work evaluates the performance of unsupervised learning techniques over benchmark intrusion detection datasets. The model gen...

Mercury exposure related oxidative stress has been incriminated at least in part, to its toxic effects in different organs. The present investigation was carried out to study the ameliorative effects of nutritional supplementation (zinc, selenium, lipoic acid and magnesium) in the liver, kidney, brain and blood. Adult rats of Sprague Dawley strain...

Hepatoprotective efficacy of propolis extract (honeybee hive product, 200 mg/kg, p.o.) was studied against biochemical and histopathological changes induced by carbontetrachloride (CCl4, 0.15 ml/kg, i.p.). Silymarin, a known hepatoprotective drug was used as positive control. Subchronic exposure to CCl4 for 3 weeks (5 days a week) caused sharp elev...

Administration of carbon tetrachloride (0.2 ml/kg) intraperitoneally to normal rats caused significant decrease in Hb, RBC and WBC Counts, and considerable increase in the level of transaminases, blood sugar and the activity of serum alkaline phosphatase. A marked increase in the serum protein was also observed. Significant decrease was observed in...

This study was carried out to investigate the hepatoprotective effect of Terminalia belerica fruit extract and its comparison with its active principle (3, 4, 5-trihydroxybenzoic acid) against carbon tetrachloride induced toxicity. Hepatoprotective effect of therapeutic agents was identified by prophylactic and curative studies. Administration of c...

The protective effect of Terminalia belerica fruit extract and its active principle (gallic acid: 3,4,5-trihydroxybenzoic acid) were investigated against carbon tetrachloride induced toxicity in rats. Carbon tetrachloride caused significant increase in the activity of alkaline phosphatase, transaminases and protein content. Hepatic lipid peroxidati...