Miracle-dz/UniFAR-FTUs huggingface dataset:
A CNN-LBP IMAGE MODELING AND CLASSIFICATION SCHEME FOR MANGO LEAF DISEASE DETECTION

: - One of the major threats to the global economy of food is caused by various diseases in plants. These diseases affect the quality as well as the quantity of the fruits, vegetables and so on that has a direct influence on efficiency which in turn is reflected by huge prices. There is a need of recognizing these diseases at early stages so that the productivity and quality of the fruits, vegetables etc. are not degraded. From the literature survey conducted, it is analyzed that the various methods such as ANN, SVM, CNN etc. were already proposed in order to detect the plant disease at early stages. However, most of the techniques are facing issue for only focusing on the contrast enhancement and not improving the content quality which in turn affects the performance of feature extraction models that do not retrieve enough features from the images. Thus, to overcome the limitations of the traditional methods, a novel approach is proposed that utilize Kuwahara filter for edge enhancement with histogram equalization to improve the quality and contrast of the image. Furthermore, to extract important features LBP feature extraction technique is implemented to get pattered information for MCNN classifier to get trained and provide accurate results. The simulation of the proposed work is performed in MATLAB environment and the results are achieved for accuracy, missing report rate and false report rate. The simulation outcomes demonstrate the high efficiency of the proposed model in terms of the accuracy, missing report rate and false report rate.
Title : Development and validation of Pyroptosis ‐ related lncRNAs prediction model for bladder cancer Running title : Pyroptosis ‐ related lncRNAs prediction model for bladder cancer

Bladder cancer is one of the highly heterogeneous disorders accompanied by a poor prognosis. This study aimed to construct a model based on pyroptosis‐related lncRNA to evaluate the potential prognostic application in bladder cancer. The mRNA expression profiles of bladder cancer patients and corresponding clinical data were downloaded from the public database from The Cancer Genome Atlas (TCGA). Pyroptosis‐related lncRNAs were identified by utilizing a co-expression network of Pyroptosis‐related genes and lncRNAs. The lncRNA was further screened by univariate Cox regression analysis. Finally, 8 pyroptosis-related lncRNA markers were established using Lasso regression and multivariate Cox regression analysis. Patients were separated into high and low-risk groups based on the performance value of the median risk score. Patients in the high-risk group had significantly poorer overall survival (OS) than those in the low-risk group (p < 0.001), and In multivariate Cox regression analysis, the risk score was an independent predictive factor of OS ( HR>1, P<0.01). The area under the curve (AUC) of the 3and 5-year OS in the receiver operating characteristic (ROC) curve were 0.742 and 0.739 respectively. In conclusion, these 8 pyroptosis-related lncRNA and their markers may be potential molecular markers and therapeutic targets for bladder cancer patients.
A REVIEW PAPER: DENOISING TECHNIQUE FOR DIFFERENT TYPE OF IMAGES.

: An Image is the collection of large number of digital data and this digital data is much prone to digital noise. This noise is added during the time of image acquisition or transmission due to various reasons. Removing noise from digital images is always a big challenge in the field of image processing. Now days there are several techniques and algorithms are developed which are successful in removing the large amount of noise . This review paper presents the different types of images used during the image processing and how to denoise them. The present denoising techniques are classified into different groups and a brief overview of various algorithms and analysis is provided.
Machine-vision-based study on driving speed of construction machinery

The driving speed of construction machinery is one of the basic variables to predict such traction property parameters as traction force,slipping loss,traction efficiency and slipping rate.Owing to driving surface conditions,the measured speeds from traditional contact measurement methods are not equivalent to actual driving speeds.Based on the textural randomness of driving surfaces,a machine-vision-based technology is first proposed for driving speed measurement.By matched-tracking the consecutive images via the CCD camera installed on vehicle body,the position in two neighboring frames of the same textural property is then obtained.With interior and exterior parameters and image frame frequencies,the real-time instantaneous driving speeds are calculated.Finally,the feasibility of the proposed method is verified via experimental simulation by capturing surface texture images.
Environmental Salinity Preference of Rana palustris

Amphibians face many daily challenges in their natural habitats, ranging from escaping predators to finding suitable mates. In addition, they may also find themselves facing anthropogenic challenges, such as crossing busy highways and coping with polluted water. This study focuses on the impacts that water polluted with road salt may have on roadside amphibian populations, utilizing outdoor and laboratory experiments. Both experiments tested water salinity preference for the pickerel frog (Rana palustris), and found that R. palustris did not show preference for either low or high salinity in short-term choice trials. Therefore, it is possible that R. palustris are not able to detect or do not show aversion behaviors toward potentially physiologically dangerous high salinities, which could lead to population-level effects if salinities are elevated. Further studies of long-term behaviors in response to increased salinity are needed. In addition, further studies on the effects of alternative road treatment chemicals on amphibian physiology are recommended.
Deep Multi-View Breast Cancer Detection: A Multi-View Concatenated Infrared Thermal Images Based Breast Cancer Detection System Using Deep Transfer Learning

This paper simply presents a fully automated breast cancer detection system as “Deep Multi-view Breast cancer Detection” based on deep transfer learning. The deep transfer learning model i.e., Visual Geometry Group 16 (VGG 16) is used in this approach for the correct classification of Breast thermal images into either normal or abnormal. This VGG 16 model is trained with the help of Static as well as Dynamic breast thermal images dataset consisting of multi-view, single view breast thermal images. These Multi-view breast thermal images are generated in this approach by concatenating the conventional left, frontal and right view breast thermal images taken from the Database for Mastology Research with Infrared image for the first time in order to generate a more informative and complete thermal temperature map of breast for enhancing the accuracy of the overall system. For the sake of genuine comparison, three other popular deep transfer learning models like Residual Network 50 (ResNet50V2), InceptionV3 network and Visual Geometry Group 19 (VGG 19) are also trained with the same augmented dataset consisting of multi-view as well as single view breast thermal images. The VGG 16 based Deep Multi-view Breast cancer Detect system delivers the best training, validation as well as testing accuracies as compared to their other deep transfer learning models. The VGG 16 achieves an encouraging testing accuracy of 99% on the Dynamic breast thermal images testing dataset utilizing the multi-view breast thermal images as input. Whereas the testing accuracies of 95%, 94% and 89% are achieved by the VGG 19, ResNet50V2, InceptionV3 models respectively over the Dynamic breast thermal images testing dataset utilizing the same multi-view breast thermal images as input.
Conventional Ponseti vs. Accelerated Ponseti in the management of cases of idiopathic Clubfoot

Aim: The aim of the study to analyze the efficacy of accelerated Ponseti method in the management of CTEV. Material and methods: This was a prospective observational study conducted in the Department of Orthopaedics, Vardhman Institute of Medical Sciences, Pawapuri, Nalanda Bihar, India from March 2016 to February 2017, after taking the approval of the protocol review committee and institutional ethics committee. A total of 70 children (100 feet) were treated. Among these 70 children, 35 children (50 feet) were treated by standard Ponseti method and 35 children (50 feet) were treated by accelerated Ponseti method. Results: A total of 70 children (100 feet) were treated; of which 35 children (50 feet) were treated by standard Ponseti method and 35 children (50 feet) were treated by accelerated Ponseti method. In the standard Ponseti group, 15 children had bilateral clubfoot, 10 were unilateral on left side, and 10 were unilateral on right side. Among 35 children, 22 (62.86%) were male and 13 (37.14%) were female. Mean age at presentation was 24.9 days. Total mean Pirani score at presentation was 4.91. Most of the cases required six casts for correction, with a mean of 5.77. Tenotomy was performed in 4 cases (11.43%). The mean number of days the child was in cast was 52.8. 6 cases (17.14%) had a relapse. All relapses were corrected by repeat casting. Mean Pirani score at 3 months follow-up was 0.081. In the accelerated Ponseti group, 15 children had bilateral clubfoot, 12 were unilateral on left side, and 8 were unilateral on right side. Among 35 children, 18 (51.43%) were male and 17(48.57%) were female. Mean age at presentation was 27.5 days. Total mean Pirani score at presentation was 5.42. Conclusion: we conclude that accelerated Ponseti method with plaster changes two times a week is as effective as Ponseti method in the treatment of idiopathic CTEV. Keyword: Accelerated ponseti method, Idiopathic clubfoot, Standard ponseti.
BI-DIRECTIONAL COMMUNICATION SYSTEM FOR DEAF AND DUMB

Communication is the process of exchanging information, views and expressions between two or more persons, in both verbal and nonverbal manner. Hand gestures are the nonverbal method of communication used along with verbal communication. A more organized form of hand gesture communication is known as sign language. In this language each alphabet of the English vocabulary is assigned a sign. The physically disabled person like the deaf and the dumb uses this language to communicate with
Analyzing Faulty Knowledge Systems in Project-Based Analyzing Faulty Knowledge Systems in Project-Based Environments: An Empirical Investigation in a R&D Organization Environments: An Empirical Investigation in a R&D Organization

This investigation contributes a framework of the relationships among the faulty elements of a knowledge system in a project-based organization. The framework was developed during an applied research project at a R&D organization. A lmowledge system can be defined as a series of inputs, processes, and outputs that interact with each other with the purpose of enhance the performance and capabilities of an organization or work unit through knowledge. The framework was developed using literature, our experience as applied researchers, and data collected from members of a R&D organization. Data was collected through a survey, interviews, and group meetings. The process of developing the framework and the framework itself can be used by project-based organizations and R&D organizations in particular to design and enhance knowledge systems in projects. Academics and researchers can use the results of this investigation as a foundation for further research and development in the area of knowledge management and organizational learning in projects.
ASYMMETRIC DSFC CODED COOPERATIVE NETWORK

: Distributed space frequency coding [DSFC] provide increased throughput in wireless communication. In this paper, a novel algorithm is proposed for asymmetric Distributed Space frequency coding. It is implemented at relay node for Decode and Forward [DAF] protocol. In asymmetric DSFC, the number of paths per fading channels is different. The asymmetric DSFC with direct link and without direct link with cyclic redundancy coding is designed and analyzed with the help of SER versus SNR performance and compares with existing system which had been implemented for symmetric DSFC. DSFC with direct link from base station to destination node is designed to improve the cooperative and frequency diversity.
REVIEW AND ANALYSIS OF VARIOUS EFFICIENT FREQUENT PATTERN ALGORITHMS

: Frequent pattern mining is the widely researched field in data mining because of its importance in many real life applications. Many algorithms are used to mine frequent patterns which give different performance on different datasets. Apriori, Eclat and FP Growth are the initial basic algorithm used for frequent pattern mining. CATS Tree extends the idea of FP-Tree to improve storage compression and allow frequent pattern mining without generation of candidate item sets. It allows mining only through a single pass over the database. The efficiency of Apriori, FP-Growth, and CATS Tree for incremental mining is very poor. The implemented CATSIM Tree uses more memory compared to Apriori, FP-Growth and CATS Tree, but with advancement in technology, is not a major concern. In this work CATSIM Tree with modifications in CATS Tree is implemented to support incremental mining with better results.
SENTIMENT ANALYSIS: A DETAILED REVIEW

: Sentiment Analysis is turning into an imperative idea nowadays. The clients as well as the organizations likewise analysis the reviews different clients so as to either purchase or to improve the nature of the items. Think about any portion, an organization identified with FMCG items or even the generation place of films depend on the client review so as to improve the nature of administration or products. The reviews are imperative for the creation organizations as well as helps different clients additionally to more readily go for that item or administration or not. This paper is an endeavor to review the ideas of the sentiment analysis, its techniques and so forth .
Adaption of Technological Implementation Towards SMEs’ Perlis Performance in Halal Industry

Adaption of Information Communication Technology (ICT) among SME’s is becoming more important in enhancing the holistic competitiveness in halal market challenging. Today, Halal food industry is rapidly growing and has contributed to the Malaysia’s economic development. Small Medium Enterprise (SME) has taken this opportunity to aim for the achievement in halal food industry. This paper aims to examine the adaption of technological implementation towards SMEs performance in Halal food industry. The methodology used was quantitative method and the questionnaires were distributed among SMEs food supplier at Perlis. SMEs Halal was selected for the study due to the fact that there are currently many ICT service platforms were used for Halal business. The finding demonstrated that the adaption of technology implementation affects SMEs performance. The findings had highlighted the important factors of technology that need to be given attention by the SMEs in order to be more competitive and gain excellence performance in Halal food industry. The implications of this study would contribute to understanding of the technology adoption towards SMEs performance in Halal industry.
Playfully Changing the Prevailing Climate of Opinion: Climate change as focal metaphor of effective global governance

Introduction Denial: the "elephants in the living room" Contrasting conventional foci: technology vs gardening Bridging metaphoric focus -the nature of engagement "Playing" with interrelated metaphors Psychological engagement -excitement? Developing playful insight Vital distinction: gaming vs playing Games: prime focus of finite players Play: mediatisation of playfulness -envirotainment? climatotainment? Higher dimensions of "game-play space" Meta-games Playing with the rules: emergence of infinite players Playing with the rules: cons and pros Transforming "game-play space" Apathy and quenching excitement Playfully getting things into focus Entrainment and enactivism Game of Life and Death: beyond Homo ludens? Playful exploration of ecopsychological embodiment of climate change pathways Towards Homo conjugens -humanity as Rosetta stone? Conclusion References
IAES International Journal of Artificial Intelligence (IJ-AI)

Received Aug 31, 2021 Revised Sep 27, 2021 Accepted Oct 12, 2021 A chatbot is a computer program designed to simulate an interactive communication to user (human) via text, audio, or video. Currently several banks in Indonesia have adopted chat technology in customer service. The application of artificial intelligence in customer service aims to prepare banks for the challenges of industry banking 4.0. In addition, it is also to solve problems currently faced by customer service. Implementing chatbot platform in banking in Indonesia is not just plug and play, although there are quite a lot of chatbot platforms available, including Rasa Platform, Botika Platform, and Kata.ai Platform. However, this study only evaluates two chatbot platforms, namely Rasa and Botika, where the two platforms are considered not yet able to be immediately adopted by banks. This is because the application of banking technology in Indonesia must refer to regulatory regulations, including those related to environmental needs, language, speed, and accuracy to understand the intent of users. Hence, research is needed to decide which chatbot platform can be implemented in the banking industry without violating regulatory regulations. From the results of evaluations conducted using the usability and hedonic motivation system adoption system (HMSAM) methods, it is found that users prefer Botika platform to be implemented in the banking industry.
SURVEY PAPER ON TOPIC MODELLING

: Topic Modeling provides a convenient way to analyze big unclassified text. A topic contains a cluster of words that frequently occurs together. A topic modeling can connect words with similar meanings and distinguish between uses of words with multiple meanings. This paper provides two categories that can be considered under the field of topic modeling. First one discusses the area of methods of Topic Modeling, which has four methods and can be considered under this category. These methods are Latent Semantic Analysis (LSA), Probabilistic Latent Semantic Analysis (PLSA), Latent Dirichlet Allocation (LDA), and Correlated Topic Model (CTM). The second category is called Topic Evolution Model, it considers an important factor time. In this category, different models are discussed, such as Topic Over Time (TOT), Dynamic Topic Models (DTM), Multiscale Topic Tomography, Dynamic Topic Correlation Detection, Detecting Topic Evolution in scientific literatures, etc.
A NEW APPROACH FOR FINDING SEQUENTIAL RULES IN SEQUENCE DATASET

: Sequential rule is specifies that if some event(s) take placed, some further event(s) are expected to take place with a specified probability or confidence. Sequential rule is also termed as-temporal or episode or prediction rule. There are numerous areas for which sequential rule mining has been appropriate for instance-e-learning, weather observation, drought management and stock market analysis. In the last few years, a range of approaches for uncovering sequential rules in especially huge databases have been emerged. Although there have been a large number of algorithms designed for sequential rule mining, but investigating efficient and scalable algorithms is still very challenging. In this paper, we have proposed a new approach towards the finding of sequential rules that utilized the concept of pattern growth approach. The outcome shows the excellence of our approach as compared to the CMRules approach.
DESIGN AND ANALYSIS OF QCA CRYPTOGRAPHIC CIRCUITS

: In this model, the total capacity of the QCA circuits is divided into two main components, leakage strength and switch power. The power loss is set during clock fluctuations, from low to high or high to low, as a leakage and power loss during the switch period as a switching power. More recently, studies have been conducted to design various QCA structures more efficiently. For example, there are many improved designs for QCA tools logic circuits using exclusive or portals reverse gates and various alternative QCA wiring approaches In addition, much attention has been paid to the design of the memory cell as vital elements in QCA devices, The ring-based QCA methods are a pioneering way to design well with the ambitious goal of promoting traditional CMOS based RAM (SRAM) while the techniques for designing each of these circuits have been established individually, the overall design needs.
Title PRICE AND QUANTITY COMPETITION: DO MIXED OLIGOPOLIES CONSTITUTE AN EQUILIBRIUM? Sub Title

This paper explores market interaction by oligopolists who may choose different action spaces. It discusses a question of the possibility of equilibrium in oligopoly where some firms control prices and others quantities . More specifically, we consider a two-stage game in which three firms first decide simultaneously whether they take as a control variable price or quantity , and afterwards compete contingent on the chosen types of control variables . By adopting a numerical approach, we show that such mixed oligopolies may emerge as an equilibrium in the two-stage game, depending on the degree of substitutability and complementarity between goods .
ANALYSIS ON TECHNIQUES FOR LUNG CANCER CLASSIFICATION USING CT IMAGES

: Lung cancer is one of the causes of cancer deaths. Lung cancer is a malignant lung tumor characterized by uncontrolled cell growth in tissues of the lung. Although, CT scan imaging is best imaging technique in medical field, it is difficult for doctors to interpret and identify the cancer from CT scan images. Therefore, computer aided diagnosis can be helpful for doctors to identify the cancerous cells accurately. Many computer aided techniques using image processing and machine learning has been researched and implemented. In this research, various lung cancer classification techniques are studied where the CT images are preprocessed using different filters like Median, Gabor, Gaussian etc., features are extracted and used for classification using the different classifiers such as SVM, CNN, Random Forest etc.