The emergence of social media has allowed people to express their feelings on products, services, films, and so on. The feeling is the user’s view or attitude towards any topic, object, event, or service. Overall, feelings have always influenced people’s decision-making. In recent years, emotions have been analyzed intensively in natural language, but many problems still have to be watched. One of the most important problems is the lack of precise classification resources. Most of the research into feeling gradation is concerned with the issue of polarity grading, although, in many practical applications, this relatively grounded feeling measure is insufficient. Design methods are therefore essential, which can accurately classify feelings into a natural language. The principal goal of the research is to develop an overflow of grammatical rules-based classification of Indian language tweets. In this work, three main challenges are identified to classify feelings in Indian language tweets and possible methods for tackling such issues. Firstly, it has been found that the informal nature of tweets is crucial for the classification of feelings. Based on the tweets, the mental illness of the person has been classified. Therefore, to categorize Indian language tweets, a combination of grammar rules based on adjectives and negations is proposed. Secondly, people often express their feelings with slang words, abbreviations, and mixed words. A technique called field tags is used to include nongrammatical arguments such as slang words and diverse words. Thirdly, if a tweet is more complex, the morphological richness of the Indian language results in a loss of performance. The grammar rules are embedded in N-gram techniques and machine learning methods. These methods are grouped into three approaches, which functionally predict Indian language tweets with syntactic words.

No abstract

Early and automatic detection of colorectal tumors is essential for cancer analysis, and the same is implemented using computer-aided diagnosis (CAD). A computerized tomography (CT) image of the colon is being used to identify colorectal carcinoma. Digital imaging and communication in medicine (DICOM) is a standard medical imaging format to process and analyze images digitally. Accurate detection of tumor cells in the complex digestive tract is necessary for optimal treatment. The proposed work is divided into two phases. The first phase involves the segmentation, and the second phase is the extraction of the colon lesions with the observed segmentation parameters. A deep convolutional neural network (DCNN) based residual network approach for the colon and polyps’ segmentation from the CT images is applied over the 2D CT images. The residual stack block is being added to the hidden layers with short skip nuance, which helps to retain spatial information. ResNet-enabled CNN is employed in the current work to achieve complete boundary segmentation of the colon cancer region. The results obtained through segmentation serve as features for further extraction and classification of benign as well as malignant colon cancer. Performance evaluation metrics indicate that the proposed network model has effectively segmented and classified colorectal tumors with dice scores of 91.57% (on average), sensitivity = 98.28, specificity = 98.68, and accuracy = 98.82.

In this paper, we model a distributed system consisting of n processes by a respective set of n Communicating Finite State Machines (CFSMs). The processes run concurrently and communicate with each other to accomplish a common goal. As opposed to the traditional product automaton built from the specified CFSMs, whose state-space explodes, we build a compressed model of what are defined as Communicating Minimal Prefix Machines (CMPMs) by simulating the CFSMs concurrently in parallel. The states of CMPMs form a well-founded, partial order. This model can be used to perform reachability analysis of the given system to check the safety properties such as communication deadlocks. The algorithm to generate the CMPMs model from CFSMs is presented in pseudo-code and its complexity discussed

No abstract

No abstract

In this paper, substrate integrated waveguide based filtenna operating at X band is proposed. The model is designed on a low-loss dielectric substrate having a thickness of 1.6 mm and comprises shorting vias along two edges of the substrate walls. To realize a bandpass filter, secondary shorting vias are placed close to primary shorting vias. The dimension and position of the vias are carefully analyzed for X band frequencies. The model is fabricated on Roger RT/duroid 5880 and the performance characteristics are measured. The proposed model achieves significant impedance characteristics with wider bandwidth in the X band. The model also achieves a maximum gain of 7.46 dBi in the operating band, thus making it suitable for X band applications.

In this paper, the goal is to perform the verification of fault-tolerant properties of a peer-to-peer (P2P) network consisting of n nodes running n corresponding parallel processes. The specification of the processes is in the form of communicating finite state machines (CFSMs). The work to be reported in this paper follows the prequel work wherein, instead of the traditional approach to construct a single synchronous product machine by composing the given CFSMs, we simulate each of the CFSMs in the non-local environment of other CFSMs and generate a set of what are called Communicating Minimal Prefix Machines(CMPMs). In this paper, we take the CMPMs model and perform the reachability analysis of certain global state vectors without losing the locality of the CFSMs of the given specification. This method cuts down the state space explosion and also opens out the possibility of distributed exploration of the local CFSM states. Faulttolerance consists of both safety and liveness properties and our approach provides a sound platform for performing state exploration/model-checking to verify these properties of the given set of application tasks that run in the P2P network.