Applications of CMOS & QCA Nanotechnology in WiMAX/WiFi Wireless Communication will lead towards Architectural Innovation & Smart Devices, Value Added Services, Full Scale QoS (Quality of Service) and Higher Reliability and Security. Our Research Work will help in addressing Long term Technical Challenges pertaining to “**Design**, Development and Application of CMOS & QCA Nanotechnology in WiMAX/WiFi/Satellite and other Wireless Communication Systems,” and provide feedback to ICs Designers/Managers, Wireless Standards Managers, Wireless and Internet Service Providers and other Interest Groups.

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Support vector machines (SVMs), being computationally powerful tools for supervised learning [1–3], have already outperformed most other systems in a wide variety of applications [4–6]. As a million stone of the SVM, twin support vector machine (TWSVM) [7] determines two nonparallel hyeperplanes such that each hyperplane is closer to one of two classes and as far as possible from the other one, which has been studied wildly in recently. The preliminary TWSVMs [7–9] **design** for **binary** class classiﬁcation problem. The **binary** TWSVMs are excellent at dealing with some certain probability model data (such as “Cross Planes” data) with less training time. Thus, the methods of constructing the nonparallel hyperplanes have been studied extensively [10–16].

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Madzarov et al.[5] is presented a novel architecture of Support Vector Machine classifiers utilizing **binary** **decision** **tree**(SVM-BDT) for solving multiclass problem. **Design** the hierarchy of **binary** **decision** subtask using SVMs with clustering algorithm. The clustering model utilizes distance measures at the kernel space instead of input space. The experimental result indicates that the training phase of SVM- BDT is faster while comparing better accuracy with other SVM based approaches, ensembles of **tree**(Bagging and random forest) and neural network. During the testing phase, due to its logarithmic complexity, SVM-BDT is much faster than widely used multi-class SVM methods like OaO and OaA.

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The APBDT-SVM is providing better multiclass classification performance. Utilizing a **decision** **tree** architecture with a probabilistic output of SVM takes much less computation for deciding that in which class unknown sample is placed. Here proposed a new and original technique that combines **Binary** **Decision** **Tree** and SVM associate with a sigmoid function(PSVM) to estimate the probability of membership to each sub-groups. Probabilistic function for each leaf are built after traversing each nodes and leaves. It is critical to have proper structuring for the better performance of APBDT-SVM. After analyzing other multiclass methods like OaO, OaA, BDT and DAG we conclude that APBDT-SVM provides better classification accuracy. APBDT-SVM also provides better result in training and testing time compare to other multiclass methods. The result shows that APBDT is accurate and efficient method as an other multiclass method. APBDT-SVM lead to a dramatic improvement in recognition speed when addressing problems with maximum number of classes.

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Our approach is to embed a dynamic **tree** in a static complete **tree** by maintaining a height bound of log n + O(1) for the dynamic **tree**, where n is its current size. It follows that the dynamic **tree** can be embedded in a complete **tree** of height log n + O(1) and size O(n). Whenever n has doubled, we create a new static **tree**. The following subsections are devoted to **tree** rebalancing schemes achieving height log n + O(1). Our scheme is very similar to the **tree** balancing scheme of Andersson [2] and to the scheme of Itai et al. [14] for supporting insertions into the middle of a file. Bender et al. [5] used a similar scheme in their cache oblivious search trees, but used it to solve the “packed-memory problem”, rather than directly to maintain balance in a **tree**. Note that the embedding of a dynamic **tree** in a complete **tree** implies that we cannot use rebalancing schemes which are based on rotations, or, more generally, schemes allowing subtrees to be moved by just changing the pointer to the root of the subtree, as e.g. is the case in the rebalancing scheme of Fagerberg [10] achieving height ⌈log n + o(1)⌉.

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Here, we use a double cuckoo seek calculation is utilized to remove designs from a gathering of unsupervised choice trees made through a various levelled strategy. In different investigates in information mining writing has presented various calculations for grouping. In our examination, we present an ideal choice **tree** system for information bunching dependent on **Binary** Cuckoo Search Algorithm. The as good as ever clarification is supplanting the dominant part helpful clarification in the home. The accompanying portrayal framework is chosen by Cuckoo Search calculation: Every egg in a home symbolizes a clarification, and a Cuckoo egg symbolizes a novel clarification [7]. The aim is to use the most likely improved egg to re-establish a not all that immense egg from the all the eggs in the homes. The methodology of grouping uses the following steps.

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non-leaf node in a binary tree has non-empty left and right sub trees. A strictly binary tree with n level always contains[r]

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ABSTRACT. **Decision** **tree** is one of the classification techniques for classi- fying sequential **decision** problems such as those in medical domain. This paper discusses an evaluation study on different single **decision** **tree** classifi- ers. There are various single **decision** **tree** classifiers which have been ex- tensively applied in medical **decision** making; each of these classifies the data with different accuracy rate. Since accuracy is crucial in medical deci- sion making, it is important to identify a classifier with the best accuracy. The study examines the performance of fourteen single **decision** **tree** classi- fiers on three medical data sets, i.e. Wisconsin’s breast cancer data sets, Pi- ma Indian diabetes data sets and hepatitis data sets. All classifiers were trained and tested using WEKA and cross validation. The results revealed that classifiers such as FT, LMT, NB **tree**, Random Forest and Random **Tree** are the five best single classifiers as they constantly provide better accuracy in their classifications.

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Table 4 shows the results of applying the margin **tree** classifier (complete linkage) with feature selection, on the data sets described earlier. Tenfold cross-validation was used to choose the margin fraction parameter α, and both CV error and test set error are reported in the table. Also shown are results for nearest shrunken centroids (Tibshirani et al., 2001), using cross-validation to choose the shrinkage parameter. This method starts with centroids for each class, and then shrinks them towards the overall centroid by soft-thresholding. We see that (a) hard thresholding generally improves upon the error rate of the full margin **tree**; (b) margin trees outperform nearest shrunken centroids on the whole, but not in every case. In some cases, the number of genes used has dropped substantially; to get smaller number of genes one could look more closely at the cross-validation curve, to check how quickly it was rising.

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In Section 2, we describe the details of the syntac- tic **decision** **tree** LM. Construction of a single-**tree** model is difficult due to the inevitable greediness of the **tree** construction process and its tendency to overfit the data. This problem is often addressed by interpolating with lower order **decision** trees. In Sec- tion 3, we point out the inappropriateness of backoff methods borrowed from n-gram models for **decision** **tree** LMs and briefly describe a generalized interpo- lation for such models. The generalized interpola- tion method allows the addition of any number of trees to the model, and thus raises the question: what is the best way to create diverse **decision** trees so that their combination results in a stronger model, while at the same time keeping the total number of trees in the model relatively low for computational practical- ity. In Section 4, we explore and evaluate a variety

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The steps to be followed at reception end by the desired receiver are First receiver have to receive both the in- order and either pre-order or post-order data. After that construct a **tree** with max 2 child for each node. After forming the **tree** apply the mechanism of BFS When you apply the BFS for the **tree** formed from the received traversal data the receiver will get the original data send by the sender.

Most formal verification tools need a **design** to be converted to a canonical data structure in order for the formal verification algorithms to be used. Several data structures have been proposed to address this need, however none of them, with the exception of TED [1-3], can handle designs at the vector-level. Therefore, formal verification tools today do make use of bit-level representation for capturing a **design**, and therefore have limitations in processing large designs. On the other hand, a graph-based representation for designs at the RT-level, coupled with efficient algorithms, provides a mechanism for handling large designs. However, TED, which has a good performance in representing vector-level designs, is not good at representing Boolean expressions. Therefore, TED is not efficient for representing designs at the RT-level. In fact, RT-level designs consist of both vector-level and logic-level parts. Many parts of an RT-level **design** including its controller may be described by Boolean expressions. So, in addition to a good vector- level representation, having a good Boolean function manipulation is essential for an RT-level data structure. The focus of this paper is to introduce Attributed TED, a high-level graph-based representation for the manipulation of RT-level descriptions. This representation is based on TED. This paper addresses the mentioned shortcomings of TED for achieving a better data structure for RT-level representation and formal verification. Experimental results demonstrate that Attributed TED yields better performance than TED using a number of benchmark circuits.

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ABSTRACT: Trees are widely used abstract data type or we can say trees are the data structure that implementing abstract data type. The reason behind to use the trees are that whenever we want to store information in forms of hierarchy we can use trees. Trees provide the moderate insertion/ deletion but quicker than arrays and slower than unordered linked lists. Trees don’t have an upper limit on number of nodes as nodes are linked using pointers. **Binary** trees are special case of **tree** where every node has almost two children. **Binary** search **tree** is a node-based **binary** **tree** data structure in which left sub **tree** of a node contains nodes with key value less than the root node key and right sub **tree** that contains nodes with key value greater than root key. **Binary** Search **Tree** provides moderate access or search and it is quicker than linked list but slower than arrays .With the **binary** search **tree**, **tree** shape depends on insertions order and that can be degenerated. So, with **binary** search **tree** we can’t guarantee efficient insertion and retrieval. Finally we look at red-black trees, a variation of **binary** search trees that overcome **binary** search tree’s limitations through a logarithmic bound on insertion and retrieval.

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In [1] authors have introduced new data mining approach to detect malicious executable rather than traditional heuristic approach which was more costly and ineffective. Using data mining framework they found out patterns which detected malicious binaries. Thus doubling the detection rate compared to traditional signature based approach. [2] We are introduced with static techniques to detect malware using techniques of information gain (IG) and principle component analysis (PCA). In [3] authors have described a hybrid model to detect malicious executable, where they have used three feature set to create a dataset. They are as follows:-i] **Binary** n-gram, ii] Derived Assembly Features (DAF), iii] Dynamic Link Library (DLL) calls. They use this features to train classifier and then trained classifier is used to test on unseen malicious executables. Here in [4] authors have combined machine learning techniques with data mining techniques. They have gathered numerous training samples and processed it. After this is done they have selected only relevant features for prediction and evaluated variety of methods like Naïve Bayes, Boosted **decision** **tree**. In [5] authors use techniques of sliding window to select n-grams as per the value of n taken. We select n-gram of malware instances to classify it correctly. They have mentioned how to reduce large data size and dimensions.

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The proposed Brent-kung adder is more flexible to speed up the **binary** addition and the arrangement is like **tree** structure for the high performance of arithmetic operations. In recent years, Field programmable gate arrays are mostly utilized because they improve the speed of microprocessor based applications such as mobile communication, DSP and telecommunication. The efficient Brent- kung adder contains two stages. They are pre-processing stage and generation stage.

We investigate the problems of multiclass cancer classification with gene selection from gene expression data. Two diﬀerent con- structed multiclass classifiers with gene selection are proposed, which are fuzzy support vector machine (FSVM) with gene selection and **binary** classification **tree** based on SVM with gene selection. Using F test and recursive feature elimination based on SVM as gene selection methods, **binary** classification **tree** based on SVM with F test, **binary** classification **tree** based on SVM with recursive feature elimination based on SVM, and FSVM with recursive feature elimination based on SVM are tested in our experiments. To accelerate computation, preselecting the strongest genes is also used. The proposed techniques are applied to analyze breast cancer data, small round blue-cell tumors, and acute leukemia data. Compared to existing multiclass cancer classifiers and **binary** classifi- cation **tree** based on SVM with F test or **binary** classification **tree** based on SVM with recursive feature elimination based on SVM mentioned in this paper, FSVM based on recursive feature elimination based on SVM can find most important genes that aﬀect certain types of cancer with high recognition accuracy.

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In the fast growing world understanding a situation is highly dynamic process. The decisions are to be made in an instantaneous fashion following different stages of risk and ambiguity. Project managers and leaders are forced to make such critical decisions that change the effect of the company. One such critical **decision** that involves rationalism and logical diagnosing is the process of choosing a feasible employee for a given project. The main objective of this research work is to propose strategies for optimally electing employees for an effective team formation and to understand the relationship between team’s success and given project’s success. The method adopted for achieving the central theme of the article is to develop a novel research model that wraps three dominant machine learning approaches to form a triangular hybridization for a better quality team formation. The three methods inspired are Artificial Neural Network (ANN), **Decision** **Tree** (DT), and a proposed method called Ensemble **Decision** **Tree** (EDT) which is a boosted **decision** **tree** using logit boost algorithm that are embedded into the proposed research model for achieving the desired goal. As a pilot scale attempt the model is validated by training and testing it over 474 freelancers from leading sites. The results infer that there exist a direct dependence of team and project success and the proposed EDT approach outperforms other two methods yielding an accuracy of about 87.34% in predicting the unknown sample as a valid or an invalid agent for the current project under consideration.

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The analysis results are very promising as for comparing with the related works. As stated earlier Huang and Hsu [2] analyzed the same data and reached the results showing that the accuracies of DA, DT and ANN are 82.1%, 86.36% and 97.78% respectively, and 80%, 10%, and the remaining 10% of the whole dataset were ran- domly used for training, testing, and validation respectively. ANN result of 97.78% accuracy doesn’t outperform our **decision** **tree** based AdaBoost result of 95.01%, because in this study not a part of data is selected for test, but 10-fold cross validation technique is used for stability. Another admirable study [5] analyzed the same data by least squares support vector machine (LS-SVM) utilizing a **binary** **decision** **tree**, optimizing the parameters by PSO, they reached a classification accuracy rate of 91.62%, again outperformed by AdaBoost ensemble with base classifiers of **decision** trees.

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The **binary** **tree** based comparator structure can be divided into two stages, where the first stage includes eight 2 bit comparators in parallel along with encoding circuitries. The second stage consists of a single 8 bit comparator. The second stage implements a radix 2 reconciling with the footed dynamic logic comparator. Constant Delay logic is utilized in the second stage due to its domino compatibility and it acts as a high performance logic boundary between the dynamic comparator and the static logic comparator. The clock **tree** is arranged such that the CD logic always operates in the high performance D to Q mode. The static inverted comparison circuit acts as a Logic Block for the constant delay comparator which reduces the unwanted glitch that is seen at the output while computing the final stage comparison. Inversion Property is employed in each of the stages in order to avoid unnecessary inverters.

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This chance node is thus resolved or collapsed into a single EV, in this case $675,000. Now use this amount as the payoff value for the “submit application” branch of its **decision** node. You may notice that the “don’t submit application” branch also can have an expected value, in this case (-$100,000). You resolve a **decision** node in terms of the greatest **decision** branch EV and disregard any lower values. Use double-hatch marks to indicate a branch from a **decision** node that is disregarded.

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