Automatic detection of cyberbullying using multi-feature based artificial intelligence with deep decision tree classification

Recent studies have shown that cyberbullying is a rising youth epidemic. In this paper, we develop a novel automated classification model that identifies the cyberbullying texts without fitting them into large dimensional space. On the other hand, a classifier.cannot provide a limited convergent solution due to its overfitting problem. Considering such limitations, we developed a text classification engine that initially pre-processes the tweets, eliminates noise and other background information, extracts the selected features and classifies without data overfitting. The study develops a novel Deep Decision Tree classifier that utilizes the hidden layers of Deep Neural Network (DNN) as its tree node to process the input elements. The validation confirms the accuracy of classification using the novel Deep classifier with its improved text classification accuracy

Influence of spatial arrangement, biofertilizers and bioirrigation on the performance of legume-millet intercropping system in rainfed areas of southern India

In this study, we checked the potential of bioirrigation – defined as a process of hydraulic lift where transfer of water occurs from deep soil layers to top soil layers through plant roots. We tested this in a pigeon pea (PP) – finger millet (FM) intercropping system in a field study for two consecutive growing seasons (2016/17 and 2017/18) at two contrasting sites in Bengaluru and Kolli Hills, India. Our objective was also to optimize the spatial arrangement of the intercropped plants (2 PP:8 FM), using either a row-wise or a mosaic design. The field trial results clearly showed that spatial arrangement of component plants affected the yield in an intercropping system. The row-wise intercropping was more effective than mosaic treatments at the Bengaluru field site, while at Kolli Hills, both row-wise and mosaic treatment performed equally. Importantly, biofertilizer application enhanced the yield of intercropping and monoculture treatments. This effect was not influenced by the spatial arrangement of component plants and by the location of the field experiment. The yield advantage in intercropping was mainly due to the release of PP from interspecific competition. Despite a yield increase in intercropping treatments, we did not see a positive effect of intercropping or biofertilizer on water relations of FM, this further explains why PP dominated the competitive interaction, which resulted in yield advantage in intercropping. FM in intercropping had significantly lower leaf water potentials than in monoculture, likely due to strong interspecific competition for soil moisture in intercropping treatments. Our study indicates that identity plant species and spatial arrangement/density of neighbouring plant is essential for designing a bioirrigation based intercropping system

Intercropping transplanted pigeon pea With finger millet: Arbuscular mycorrhizal fungi and plant growth promoting rhizobacteria boost yield while reducing fertilizer input

Pigeon pea (Cajanus cajan) and finger millet (Eleusine coracana) are staple food crops for millions of the rural population in Asia and Africa. We tested, in field trials over three consecutive seasons at two sites in India, an intercropping and biofertilization scheme to boost their yields under low-input conditions. Pigeon pea seedlings were raised during the dry season and transplanted row-wise into fields of finger millet, and arbuscular mycorrhizal fungi (AMF) and plant growth-promoting rhizobacteria (Pseudomonas) were added alone or in combination to both pigeon pea and finger millet. Our major findings are (i) effects of the biofertilizers were particularly pronounced at the site of low fertility; (ii) dual inoculation of AMF+PGPR to finger millet and pigeon pea crops showed increased grain yields more effectively than single inoculation; (iii) the combined grain yields of finger millet and pigeon pea in intercropping increased up to +128% due to the biofertilizer application; (iv) compared to direct sowing, the transplanting system of pigeon pea increased their average grain yield up to 267% across site, and the yield gains due to biofertilization and the transplanting system were additive. These technologies thus offer a tool box for sustainable yield improvement of pigeon pea and finger millet

A Hybrid Speech Enhancement Algorithm for Voice Assistance Application

In recent years, speech recognition technology has become a more common notion. Speech quality and intelligibility are critical for the convenience and accuracy of information transmission in speech recognition. The speech processing systems used to converse or store speech are usually designed for an environment without any background noise. However, in a real-world atmosphere, background intervention in the form of background noise and channel noise drastically reduces the performance of speech recognition systems, resulting in imprecise information transfer and exhausting the listener. When communication systems’ input or output signals are affected by noise, speech enhancement techniques try to improve their performance. To ensure the correctness of the text produced from speech, it is necessary to reduce the external noises involved in the speech audio. Reducing the external noise in audio is difficult as the speech can be of single, continuous or spontaneous words. In automatic speech recognition, there are various typical speech enhancement algorithms available that have gained considerable attention. However, these enhancement algorithms work well in simple and continuous audio signals only. Thus, in this study, a hybridized speech recognition algorithm to enhance the speech recognition accuracy is proposed. Non-linear spectral subtraction, a well-known speech enhancement algorithm, is optimized with the Hidden Markov Model and tested with 6660 medical speech transcription audio files and 1440 Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) audio files. The performance of the proposed model is compared with those of various typical speech enhancement algorithms, such as iterative signal enhancement algorithm, subspace-based speech enhancement, and non-linear spectral subtraction. The proposed cascaded hybrid algorithm was found to achieve a minimum word error rate of 9.5% and 7.6% for medical speech and RAVDESS speech, respectively. The cascading of the speech enhancement and speech-to-text conversion architectures results in higher accuracy for enhanced speech recognition. The evaluation results confirm the incorporation of the proposed method with real-time automatic speech recognition medical applications where the complexity of terms involved is high

Measurement and analysis of pocket milling features in abrasive water jet machining of Ti-6Al-4V alloy

The present work deals with the size effect of abrasive water jet milling parameters on the square pockets of Ti-6Al-4V alloy. In this study, the abrasive mesh size, water jet pressure and traverse rate were chosen as milling variables and their effect on pocket features such as depth of cut, undercut, material removal rate, and surface roughness were examined. This study also characterizes the milled pocket surfaces under different milling conditions. Most of the measurements and surface characterizations were done using the Dino-Lite Digital Microscope. For both #80 and #100 abrasives, the AWJ-milled pockets were formed with variations in depth milled and rugged surface by increasing the water jet pressure from 175 to 200 MPa under all the selected traverse rate conditions. Also, the variations of depth of cut in successive trajectories found to have a speed bump effect. At these settings, distribution of energy to the work material was more due to deceleration of jet in the boundary close by and changes made in the feed directions in raster path from 0 & DEG; to 90 & DEG; at a step-over distance of 0.2 mm. This yielded undercuts in the milled pocket corners. However, there was a significant reduction in the undercut with a water jet pressure of 125 MPa and a traverse rate of 3500 mm/min were employed. Besides, the abrasive mesh size #100 had a better surface topography, and also strong jet footprints were observed with mesh size of #80. Based on the experiments results, the size effect of different milling parameters was seen having influence on the pocket geometry and surface features

Investigation on the Optical Design and Performance of a Single-Axis-Tracking Solar Parabolic trough Collector with a Secondary Reflector

The design of solar concentrating collectors for the effective utilization of solar energy is a challenging condition due to tracking errors leading to different divergences of the solar incidence angle. To enhance the optical performance of solar parabolic trough collectors (SPTC) under a diverged solar incidence angle, an additional compound parabolic concentrator (CPC) is introduced as a secondary reflector. SPTC with CPC is designed and modeled for a single axis-tracking concentrating collector based on the local ambient conditions. In this work, the optical performance of the novel SPTC system with and without a secondary reflector is investigated using MATLAB and TRACEPRO software simulations for various tracking errors. The significance parameters such as the solar incidence angle, aperture length, receiver tube diameter, rim angle, concentration ratio, solar radiation, and absorbed flux are analyzed. The simulation results show that the rate of the absorbed flux on the receiver tube is significantly improved by providing the secondary reflector, which enhances the optical efficiency of the collector. It is found that the optical efficiency of the SPTC with a secondary reflector is 20% higher than the conventional collector system for a solar incidence angle of 2°. This work can effectively direct the choice of optimal secondary reflectors for SPTC under different design and operating conditions

Dual Challenge of a Cecoureterocele with Calculus: A rare case report

Ureteroceles are the most prevalent urinary tract malformations in humans. Only 5% of ureteroceles are predicted to prolapse and it usually occurs in childhood. We outline the clinical history, radiological results, and a potential course of treatment for this challenging condition. A 32-year-old female checked herself into our institution with complaints of burning urination and 20 years of complaints of urethral ballooning when urinating. Initial sonographic evaluation revealed that at the left vesicoureteric junction, a cystic lesion extends into the bladder, with a hyperechoic focus causing posterior acoustic shadowing. CT scan confirmed the diagnosis of an ureterocele with calculus. A voiding cystourethrogram revealed a left-sided ureterocele that descends down the urethra and into the interlabial region. CT cystogram verified the presence of a left-sided cecoureterocele with calculus. Cecoureterocele is a rare variant of ectopic ureteroceles. Girls experience this condition more frequently than boys, and they are predisposed to vesicoureteric reflux and recurrent infections. To prevent problems like renal function loss, recurrent urinary tract infections, and urinary incontinence, it is important to gain diagnostic confirmation of these circumstances. Less invasive surgical techniques like endoscopic ureterocele puncture or even nonoperative treatment appear to produce comparable functional outcomes. When a patient arrives with a urethral protrusion, one should be extra cautious. In this case report, a cecoureterocele that has prolapsed is presented in a rare way. It presents an important chance to evaluate the clinical and diagnostic characteristics of this urinary tract abnormality

Ensemble Machine Learning-Based Approach for Predicting of FRP–Concrete Interfacial Bonding

Developments in fiber-reinforced polymer (FRP) composite materials have created a huge impact on civil engineering techniques. Bonding properties of FRP led to its wide usage with concrete structures for interfacial bonding. FRP materials show great promise for rehabilitation of existing infrastructure by strengthening concrete structures. Existing machine learning-based models for predicting the FRP–concrete bond strength have not attained maximum performance in evaluating the bond strength. This paper presents an ensemble machine learning approach capable of predicting the FRP–concrete interfacial bond strength. In this work, a dataset holding details of 855 single-lap shear tests on FRP–concrete interfacial bonds extracted from the literature is used to build a bond strength prediction model. Test results hold data of different material properties and geometrical parameters influencing the FRP–concrete interfacial bond. This study employs CatBoost algorithm, an improved ensemble machine learning approach used to accurately predict bond strength of FRP–concrete interface. The algorithm performance is compared with those of other ensemble methods (i.e., histogram gradient boosting algorithm, extreme gradient boosting algorithm, and random forest). The CatBoost algorithm outperforms other ensemble methods with various performance metrics (i.e., lower root mean square error (2.310), lower covariance (21.8%), lower integral absolute error (8.8%), and higher R-square (96.1%)). A comparative study is performed between the proposed model and best performing bond strength prediction models in the literature. The results show that FRP–concrete interfacial bonding can be effectively predicted using proposed ensemble method