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Summary: The right to privacy refers to an individual's decision about how personal information can be gathered, utilized, and disseminated. Individual consent and openness are the most important foundations for gaining consumers' confidence, and this pushes businesses to use privacy‐enhancing techniques while developing systems. The purpose of a privacy‐aware design is to safeguard data in such a manner that it does not expand an adversary's current understanding of an individual beyond what would be permitted. When these data pieces are coupled with the plethora of source data accessible outside the system to identify a user, this becomes crucial. Individual privacy is protected by privacy rules all around the globe, but they are often complicated and ambiguous, making their translation into practical and technologically privacy‐friendly structures difficult. The main contribution of this article is that we use Shannon's entropy (SE) to construct an objective measure that may guide our major technical design choices. And for privacy‐aware architecture, simplifying the state‐of‐the‐art security approaches given in the literature. [ABSTRACT FROM AUTHOR]

Cancer patients undergoing treatment with antineoplastic drugs often experience chemotherapy-induced neuropathic pain (CINP), and the therapeutic options for managing CINP are limited. Here, we show that systemic paclitaxel administration upregulates the expression of neurotrophin-3 (Nt3) mRNA and NT3 protein in the neurons of dorsal root ganglia (DRG), but not in the spinal cord. Blocking NT3 upregulation attenuates paclitaxel-induced mechanical, heat, and cold nociceptive hypersensitivities and spontaneous pain without altering acute pain and locomotor activity in male and female mice. Conversely, mimicking this increase produces enhanced responses to mechanical, heat, and cold stimuli and spontaneous pain in naive male and female mice. Mechanistically, NT3 triggers tropomyosin receptor kinase C (TrkC) activation and participates in the paclitaxel-induced increases of C–C chemokine ligand 2 (Ccl2) mRNA and CCL2 protein in the DRG. Given that CCL2 is an endogenous initiator of CINP and that Nt3 mRNA co-expresses with TrkC and Ccl2 mRNAs in DRG neurons, NT3 likely contributes to CINP through TrkC-mediated activation of the Ccl2 gene in DRG neurons. NT3 may be thus a potential target for CINP treatment. Synopsis: Neurotrophin-3 (NT3) activates tropomyosin receptor kinase C (TrkC) to elevate C–C chemokine ligand 2 (CCL2) in dorsal root ganglion neurons contributing to paclitaxel-induced neuropathic pain. NT3 expression is increased in DRG neurons after systemic paclitaxel injection. Blocking this increase inhibits paclitaxel-induced nociceptive hypersensitivity. The inhibitory effect is mediated by preventing NT3-triggered TrkC activation and subsequent CCL2 production in DRG neurons. Neurotrophin-3 (NT3) activates tropomyosin receptor kinase C (TrkC) to elevate C-C chemokine ligand 2 (CCL2) in dorsal root ganglion neurons contributing to paclitaxel-induced neuropathic pain. [ABSTRACT FROM AUTHOR]

Background and Purpose: Peripheral nerve trauma‐induced dysregulation of pain‐associated genes in the primary sensory neurons of dorsal root ganglion (DRG) contributes to neuropathic pain genesis. RNA‐binding proteins participate in gene transcription. We hypothesized that RALY, an RNA‐binding protein, participated in nerve trauma‐induced dysregulation of DRG pain‐associated genes and nociceptive hypersensitivity. Methods and Results: Immunohistochemistry staining showed that RALY was expressed exclusively in the nuclei of DRG neurons. Peripheral nerve trauma caused by chronic constriction injury (CCI) of unilateral sciatic nerve produced time‐dependent increases in the levels of Raly mRNA and RALY protein in injured DRG. Blocking this increase through DRG microinjection of adeno‐associated virus 5 (AAV5)‐expressing Raly shRNA reduced the CCI‐induced elevation in the amount of eukaryotic initiation factor 4 gamma 2 (Eif4g2) mRNA and Eif4g2 protein in injured DRG and mitigated the development and maintenance of CCI‐induced nociceptive hypersensitivity, without altering basal (acute) response to noxious stimuli and locomotor activity. Mimicking DRG increased RALY through DRG microinjection of AAV5 expressing Raly mRNA up‐regulated the expression of Eif4g2 mRNA and Eif4g2 protein in the DRG and led to hypersensitive responses to noxious stimuli in the absence of nerve trauma. Mechanistically, CCI promoted the binding of RALY to the promoter of Eif4g2 gene and triggered its transcriptional activity. Conclusion and Implications: Our findings indicate that RALY participates in nerve trauma‐induced nociceptive hypersensitivity likely through transcriptionally triggering Eif4g2 expression in the DRG. RALY may be a potential target in neuropathic pain management. [ABSTRACT FROM AUTHOR]

Nowadays, searching the relevant documents from a large dataset becomes a big challenge. Automatic query expansion is one of the techniques, which addresses this problem by refining the query. A new query expansion approach using cuckoo search and accelerated particle swarm optimization technique is proposed in this paper. The proposed approach mainly focused to find the most relevant expanded query rather than suitable expansion terms. In this paper, Fuzzy logic is also employed, which improves the performance of accelerated particle swarm optimization by controlling various parameters. We have compared the proposed approach with other existing and recently developed automatic query expansion approaches on various evaluating parameters such as average recall, average precision, Mean-Average Precision, F-measure and precision-recall graph. We have evaluated the performance of all approaches on three datasets CISI, CACM and TREC-3. The results obtained for all three datasets depict that the proposed approach gets better results in comparison to other automatic query expansion approaches. [ABSTRACT FROM AUTHOR]

Recently, the spread of fake images on social media platforms has become a significant concern for individuals, organizations, and governments. These images are often created using sophisticated techniques to spread misinformation, influence public opinion, and threaten national security. This paper begins by defining fake images and their potential impact on society, including the spread of misinformation and the erosion of trust in digital media. This paper also examines the different types of fake images and their challenges for detection. We then review the recent approaches proposed for detecting fake images, including digital forensics, machine learning, and deep learning. These approaches are evaluated in terms of their strengths and limitations, highlighting the need for further research. This paper also highlights the need for multimodal approaches that combine multiple sources of information, such as text, images, and videos. Furthermore, we present an overview of existing datasets, evaluation metrics, and benchmarking tools for fake image detection. This paper concludes by discussing future directions for fake image detection research, such as developing more robust and explainable methods, cross-modal fake detection, and the integration of social context. It also emphasizes the need for interdisciplinary research that combines computer science, digital forensics, and cognitive psychology experts to tackle the complex problem of fake images. This survey paper will be a valuable resource for researchers and practitioners working on fake image detection on social media platforms. [ABSTRACT FROM AUTHOR]

The identification and apprehending of suspects by law enforcement authorities rely heavily on facial sketches. The sketch artist creates sketches based on the witnesses' memories. Sketch artists are few and limited in their availability. It is also evident that as time passes, the eyewitness forgets many of the important details, which can be expensive in time-sensitive investigations. The sketch was used to obtain the suspect's image through the state-of-the-art sketch-photo retrieval model, which missed the relevance of time sensitivity. A linguistic description-based suspect face image retrieval approach is presented in this study. In the proposed approach, the facial attribute-value pair is extracted from eyewitness descriptions. Facial attribute saliency is also studied in this work and validated with the Fuzzy Analytic Hierarchy Process (FAHP) model. A weighted score is computed to retrieve the suspect face images. The effectiveness of the proposed method is assessed by comparing it to existing linguistic sketch-based retrieval methods as well as the sketch to photo retrieval models. As compared to state-of-the-art approaches, experimental results give an accuracy of 94.98%. [ABSTRACT FROM AUTHOR]

Faces are the most common biometric used for the identification of a person. Law enforcement agencies use face as a key point to identify the suspect involved in unlawful activities. Forensic sketches are normally developed by the sketch artist based on verbal details provided by an eyewitness about the suspect. In a forensic sketch, the facial description depends on the memory of the eyewitness; therefore, there is uncertainty in facial attributes. In the recent past, lots of sketch-to-photograph retrieval methods are proposed by many researchers; however, they have ignored the uncertainty of facial attributes for suspect face retrieval. Recently, linguistic information is also utilized for suspect face retrieval. In this paper, we have provided an extensive review of the available methods for suspect face retrieval using visual and linguistic information. The review focuses firstly on the traditional methods and their categorization also shows the evolution of suspect face retrieval approaches over the years. We have also shown the summary of the performance of representative state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Spotting fake news is a critical problem nowadays. Social media are responsible for propagating fake news. Fake news propagated over digital platforms generates confusion as well as induce biased perspectives in people. Detection of misinformation over the digital platform is essential to mitigate its adverse impact. Many approaches have been implemented in recent years. Despite the productive work, fake news identification poses many challenges due to the lack of a comprehensive publicly available benchmark dataset. There is no large-scale dataset that consists of Indian news only. So, this paper presents IFND (Indian fake news dataset) dataset. The dataset consists of both text and images. The majority of the content in the dataset is about events from the year 2013 to the year 2021. Dataset content is scrapped using the Parsehub tool. To increase the size of the fake news in the dataset, an intelligent augmentation algorithm is used. An intelligent augmentation algorithm generates meaningful fake news statements. The latent Dirichlet allocation (LDA) technique is employed for topic modelling to assign the categories to news statements. Various machine learning and deep-learning classifiers are implemented on text and image modality to observe the proposed IFND dataset's performance. A multi-modal approach is also proposed, which considers both textual and visual features for fake news detection. The proposed IFND dataset achieved satisfactory results. This study affirms that the accessibility of such a huge dataset can actuate research in this laborious exploration issue and lead to better prediction models. [ABSTRACT FROM AUTHOR]

The present world is intimidated by the problem of water scarcity that is to be addressed immediately. So, it is wise to treat wastewater to meet the massive need for drinking water for the fast-growing population. The magnificent application of Internet of Things (IoT) technology in many smart cities has derived fruitful results. This research study has proposed a real-time system using IoT that regularly monitors specific crucial parameters of a wastewater treatment plant and informs any plant's dysfunction to the operator. Furthermore, the large stream of data sets generated by IoT sensors in real-time can be analyzed and processed by complex event processing (CEP). This study was experimented with Smart Treatment (SMARTreat) architecture and its application in a simple water system of an industrial estate in South India. The proposed architecture showed outstanding results and has received positive comments from the water treatment plant managers. [ABSTRACT FROM AUTHOR]

The invention of novel light-harvesting materials is one of the primary reasons behind the acceleration of current scientific advancement and technological innovation in the solar sector. Organometal halide perovskite (OHP) has recently attracted a great deal of interest because of the high-energy conversion efficiency that has reached within a few years of its discovery and development. Modern machine learning (ML) technology is quickly advancing in a variety of fields, providing blueprints for the discovery and rational design of new and improved material properties. In this paper, we apply ML to optimize the material composition of OHPs, propose design methods and forecast their performance. Our ML model is built using 285 datasets that were taken from about 700 experimental articles. We have developed two different ML models to predict the bandgap and performance parameters of solar cell. In the first model, we employed three ML algorithms to investigate the relationship between bandgap and perovskite material composition. We estimated the performance characteristics using projected and actual bandgap. Second, ML models are used to predict the performance parameters employing the bandgap of perovskite and energy difference between electron transport layer (ETL) and hole transport layer (HTL) with perovskite as an input parameter. Simulation results suggest that the artificial neural network (ANN) technique, which predicts the bandgap by taking into consideration how cations and halide ions interact with one another, demonstrates a better degree of accuracy (with a Pearson coefficient of 0.91 and root mean square error of 0.059). The constructed ML model closely fits the theoretical prediction made by Shockley and Queisser, and that is almost hard for a person to discover from an aggregation of datasets. [ABSTRACT FROM AUTHOR]

Biodiesel is a biological renewable source produced from the conversion of triglycerides to alkyl esters. Palm oil is one of the most used lipid feedstocks for biodiesel production. It becomes necessary to optimize the transesterification reaction parameters to reduce the cost and enhance the quality of biodiesel. This study focuses on the use of homogenous sulfuric acid as a catalyst for the transesterification of palm fatty acids to methyl esters in a batch-scale reactor. A novel examination of transesterification reaction input parameters using the technique for order performance by similarity to ideal solution optimization technique and the effect of these parameters on yield, viscosity, and density of palm biodiesel using 3D surface graphs is investigated in this research. The present optimization approach is implemented to find out the optimum ranking of biodiesel production. From the experimental and numerical simulation, optimum results were observed at the catalyst concentration of 6% (w/w), reaction temperature of 70 °C, the reaction time of 120 min, and alcohol to oil molar ratio of 30:1 at which yield of 95.35%, viscosity of 5.0 cSt, and density of 880 kg/m3 of palm biodiesel were obtained. The different physicochemical properties of produced palm methyl esters are obtained within standards set by international authorities. Selected optimized process parameters can be used for commercial-scale biodiesel production. [ABSTRACT FROM AUTHOR]

A figurative language expression known as sarcasm implies the complete contrast of what is being stated with what is meant, with the latter usually being rather or extremely offensive, meant to offend or humiliate someone. In routine conversations on social media websites, sarcasm is frequently utilized. Sentiment analysis procedures are prone to errors because sarcasm can change a statement's meaning. Analytic accuracy apprehension has increased as automatic social networking analysis tools have grown. According to preliminary studies, the accuracy of computerized sentiment analysis has been dramatically decreased by sarcastic remarks alone. Sarcastic expressions also affect automatic false news identification and cause false positives. Because sarcastic comments are inherently ambiguous, identifying sarcasm may be difficult. Different individual NLP strategies have been proposed in the past. However, each methodology has text contexts and vicinity restrictions. The methods are unable to manage various kinds of content. This study suggests a unique ensemble approach based on text embedding that includes fuzzy evolutionary logic at the top layer. This approach involves applying fuzzy logic to ensemble embeddings from the Word2Vec, GloVe, and BERT models before making the final classification. The three models' weights assigned to the probability are used to categorize objects using the fuzzy layer. The suggested model was validated on the following social media datasets: the Headlines dataset, the "Self-Annotated Reddit Corpus" (SARC), and the Twitter app dataset. Accuracies of 90.81%, 85.38%, and 86.80%, respectively, were achieved. The accuracy metrics were more accurate than those of earlier state-of-the-art models. [ABSTRACT FROM AUTHOR]

Out of various strategies to extenuate the diesel engine emissions, use of water-blended diesel fuel in emulsified form is found to be a prominent option. The studies carried out by various researchers give quite inconsistent results for optimum water concentration, surfactant concentration, and HLB value. An effort has been made in this paper to analyze this inconsistency of water concentration and surfactant for engine emissions and performance along with the effect of compression ratio. The work has been carried out on a non-road, constant rpm, VCR (variable compression ratio) diesel engine. Different emulsions of 5%, 10%, 15%, and 20% water-in-diesel with 3% emulsifier concentration were tested on the diesel-optimized engine at CR 21. The results showed that emulsion of 15% water-in-diesel is optimal on the basis of emissions and performance. Further, the selected 15% emulsion is tested with 4% emulsifier concentration. It improved the stability of emulsion and performance of the engine with slight adverse effects on the NOx emissions. Then, emulsion with 4% surfactant and 15% water is tested to determine optimum compression ratio for emulsified fuels. CR 20 is found in optimum. Remarkably 9.28% improvement in BTE is observed reaching up to 23.89% as compared to 21.86% with bare diesel. 25.1% reduction in NOx and more than 50% reduction in smoke is observed. Overall, it is concluded that the water-blended diesel emulsion could serve as a fuel-efficient cleaner combustion technology and needs to be standardized. Abbreviations HLB: Hydrophile–Lipophile Balance; Cv: Calorific Value; W/D: Water in Diesel; PM: Particulate matter; RPM: Rotation per minute; HC: Hydrocarbon; CR: Compression Ratio; EGT: Exhaust Gas Temperature; BTE: Brake Thermal Efficiency; NOx: Oxides of Nitrogen; BSFC: Brake specific fuel consumption [ABSTRACT FROM AUTHOR]

The most salient argument that needs to be addressed universally is Early Breast Cancer Detection (EBCD), which helps people live longer lives. The Computer-Aided Detection (CADs)/Computer-Aided Diagnosis (CADx) system is indeed a software automation tool developed to assist the health professions in Breast Cancer Detection and Diagnosis (BCDD) and minimise mortality by the use of medical histopathological image classification in much less time. This paper purposes of examining the accuracy of the Convolutional Neural Network (CNN), which can be used to perceive breast malignancies for initial breast cancer detection to determine which strategy is efficient for the early identification of breast cell malignancies formation of masses and Breast microcalcifications on the mammogram. When we have insufficient data for a new domain that is desired to be handled by a pre-trained Convolutional Neural Network of Residual Network (ResNet50) for Breast Cancer Detection and Diagnosis, to obtain the Discriminative Localization, Convolutional Neural Network with Class Activation Map (CAM) has also been used to perform breast microcalcifications detection to find a specific class in the Histopathological image. The test results indicate that this method performed almost 225.15% better at determining the exact location of disease (Discriminative Localization) through breast microcalcifications images. ResNet50 seems to have the highest level of accuracy for images of Benign Tumour (BT)/Malignant Tumour (MT) cases at 97.11%. ResNet50's average accuracy for pre-trained Convolutional Neural Network is 94.17%. [ABSTRACT FROM AUTHOR]

In the current work, n-butanol-diesel blends were tested on a small size agriculture-based compression ignition (CI) engine. Taguchi analysis was carried out to identify the optimum blending ratio and engine operating parameters. Experiments were conducted with n-butanol/diesel blends (10–20% by volume) by varying compression ratio (CR) (17.5–19.5), injection timing (21–25 CA btdc) and injection pressure (200–220 bar). The 20% n-butanol/diesel blend (BU20) showed better results of performance and emissions at increased CR under similar operating conditions. When engine was fueled with BU20, reduction in Smoke, NOx (Nitrogen-oxides) and CO (Carbon-monoxide) were observed to be 49.03%, 13.68% and 5.88%, respectively, in comparison to diesel. However, HC (Hydrocarbons) were found to be higher by 11.76% for BU20 as compared to diesel. [ABSTRACT FROM AUTHOR]

Social media are the main contributors to spreading fake images. Fake images are manipulated images altered through software or by other means to change the information they convey. Fake images propagated over microblogging platforms generate misrepresentation and stimulate polarization in the people. Detection of fake images shared over social platforms is extremely critical to mitigating its spread. Fake images are often associated with textual data. Hence, a multi-modal framework is employed utilizing visual and textual feature learning. However, few multi-modal frameworks are already proposed; they are further dependent on additional tasks to learn the correlation between modalities. In this paper, an efficient multi-modal approach is proposed, which detects fake images of microblogging platforms. No further additional subcomponents are required. The proposed framework utilizes explicit convolution neural network model EfficientNetB0 for images and sentence transformer for text analysis. The feature embedding from visual and text is passed through dense layers and later fused to predict fake images. To validate the effectiveness, the proposed model is tested upon a publicly available microblogging dataset, MediaEval (Twitter) and Weibo, where the accuracy prediction of 85.3% and 81.2% is observed, respectively. The model is also verified against the newly created latest Twitter dataset containing images based on India's significant events in 2020. The experimental results illustrate that the proposed model performs better than other state-of-art multi-modal frameworks. [ABSTRACT FROM AUTHOR]

In the reported study, a dynamic analytical model is developed to propose the energy, exergy, environmental impact, and economic analyses of the water heating system at Jaipur (India) with an evacuated tube compound parabolic concentrator field of a total area of 81 m2. Consequently, the model is used to perform parametric studies to report the effect of operating and meteorological parameters on the productivity and performance of the system. Moreover, the system's performance, environmental impact, and economic aspects have been investigated and compared under different meteorological conditions at four different Rajasthan (India) locations using TMY2 weather data files. Results clarified that Jodhpur receives the highest solar radiation intensity from these four locations. The model results were validated with the experimental data, and a good agreement has prevailed. Consequently, the results indicate the highest annual energy and exergy gain for Jodhpur with 79.72 MWh and 9.311 MWh, respectively, followed by Jaisalmer, Barmer, and Jaipur. The economic analysis results clarified that the simple payback period ranged from 4.5 to 4.75 years and the discounted payback period ranged from 6.6 to 7 years based on a 6% discount rate. At the same time, the levelized cost of heating for the given system is around 0.023 $/kWh which is very economical closest to that of CNG as a fuel which costs around 0.059 $/kWh. The internal rate of return is reported to be 16.76, 16.82, 16.77, and 16.75% for Barmer, Jodhpur, Jaipur, and Jaisalmer, respectively, and savings of 74.4, 78.1, 75.4, and 73.8 tonnes of CO2 emission to the environment. [ABSTRACT FROM AUTHOR]

Privacy is a significant problem in communications networks. As a factor, trustworthy knowledge sharing in computer networks is essential. Intrusion Detection Systems consist of security tools frequently used in communication networks to monitor, detect, and effectively respond to abnormal network activity. We integrate current technologies in this paper to develop an anomaly-based Intrusion Detection System. Machine Learning methods have progressively featured to enhance intelligent Anomaly Detection Systems capable of identifying new attacks. Thus, this evidence demonstrates a novel approach for intrusion detection introduced by training an artificial neural network with an optimized Bat algorithm. An essential task of an Intrusion Detection System is to maintain the highest quality and eliminate irrelevant characteristics from the attack. The recommended BAT algorithm is used to select the 41 best features to address this problem. Machine Learning based SVM classifier is used for identifying the False Detection Rate. The design is being verified using the KDD99 dataset benchmark. Our solution optimizes the standard SVM classifier. We attain optimal measures for abnormal behavior, including 97.2 %, the attack detection rate is 97.40 %, and a false-positive rate of 0.029 %. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks are generally employed for observing and monitoring specific environments. WSNs are made from a huge amount of low-cost sensor nodes separated and distributed in different environments for distributing data through sensor nodes. The collection of data by the various sensors were transmitted into the Base Station. An enhanced Trust-Based Adaptive Acknowledgment based Intrusion-Detection System was proposed from positive distributions in WSNs. A Kalman filter algorithm is used in Multi-objective Particle Swarm Optimization to predict trust nodes over the WSN. Simulations were carried out for non-malicious (0% malicious) networks, and various ranges of malicious nodes in the network were investigated. The outcomes show that the proposed MPSO achieves an improvement of 3.3% than PSO at 0% malicious nodes concerning the PDR. Similarly, at 30% malicious, the PDR of MPSO achieves better by 3.5% than PSO in WSN. [ABSTRACT FROM AUTHOR]

In the present work, a pebble bed thermal energy storage (PBTES) system is developed to utilize the waste energy from engine exhaust. The developed PBTES is integrated with an electric dynamometer coupled stationary Diesel engine for experimental investigation. The engine performance is compared with and without integration of the PBTES system. The 60-75% of energy can be stored in the fabricated system during the charging process at various load conditions. It is found that nearly 11-15% of engine fuel energy can be saved using this storage system considering the charging process. Heat recovery/discharging from PBTES shows that 6-8.5% of fuel primary energy can be saved. The system combined (engine+PBTES) efficiency varies from 11-38% at different load conditions. The highest exergy saved is obtained as 3.32% when a 3 kW load is applied. The developed system can be easily used for domestic or industrial use space heating or hot fluid requirements. [ABSTRACT FROM AUTHOR]

The climate has changed absolutely in every area in just a few years as digitized, making high-speed internet service a significant need in the future. Future Internet is supposed to face exponential growth in traffic, and highly complicated infrastructure, threatening to make conventional NTC approaches unreliable and even counterproductive. In recent days, AI Stimulated state-of-the-art breakthroughs with the ability to tackle extensive and multifarious challenges, and the network community is initiated by considering the NTC prototype from legacy rule-based towards a novel AI-based. Design and execution are applied to interdisciplinary become more essential. A smart home network supports various applications and smart devices within the proposed work, including e-health devices, regular computing devices, and home automation devices. Many devices accessible through the Internet by Home GateWay for Congestion (HGC) in a smart home. Throughout this paper, a Software-Defined Network Home GateWay for Congestion (SDNHGC) architecture for improved management of remote smart home networks and protection of the significant network's SDN controller. It enables effective network capacity regulation, focused on real-time traffic analysis and core network resource allocation. It cannot control the Network in dispersed smart homes. Our innovative SDNHGC expands power across the connectivity network, a smart home network enabling improved end-to-end monitoring of networks. The planned SDNHGC directly will gain centralized device identification by classifying traffic through a smart home network. Several of the current traffic classifications approach, checking deep packets, cannot have this real-time device knowledge for encrypted data to solve this issue. [ABSTRACT FROM AUTHOR]

At a grocery store, product supply management is critical to its employee's ability to operate productively. To find the right time for updating the item in terms of design/replenishment, real-time data on item availability are required. As a result, the item is consistently accessible on the rack when the client requires it. This study focuses on product display management at a grocery store to determine a particular product and its quantity on the shelves. Deep Learning (DL) is used to determine and identify every item and the store's supervisor compares all identified items with a preconfigured item planning that was done by him earlier. The approach is made in II-phases. Product detection, followed by product recognition. For product detection, we have used You Only Look Once Version 5 (YOLOV5), and for product recognition, we have used both the shape and size features along with the color feature to reduce the false product detection. Experimental results were carried out using the SKU-110 K data set. The analyses show that the proposed approach has improved accuracy, precision, and recall. For product recognition, the inclusion of color feature enables the reduction of error date. It is helpful to distinguish between identical logo which has different colors. We can achieve the accuracy percentage for feature level as 75 and score level as 81. [ABSTRACT FROM AUTHOR]

Judiciary is one of the three important organs of the government. The independence of the judiciary is crucial for the performance of its functions without fear or favor. The independence of judiciary basically refers to non-interference from any other organ of the government restraining its function to deliver justice in a fair and impartial manner. However, it doesn’t mean absence of any form of accountability. Montesquieu who propounded the idea of independence of judiciary majorly relied on the theory of separation of power to ensure an independent judicial system. Despite all the efforts, maintaining the judiciary free from all forms of bias is still a far-reaching dream. This paper examines the concept and importance of the independence of judiciary. The paper also highlights the various ways in which the independence of the judiciary can be curtailed. The paper further includes the interrelation between the doctrine of separation of powers and the concept of independence of the judiciary. The measures adopted by various constitutions, with special reference to the Indian and US constitutions, are also discussed and compared. [ABSTRACT FROM AUTHOR]

Cervical cancer ranks as the second most cause of female cancer deaths in India and is the second leading cause of cancer deaths in women aged 15 to 44 years in India. About 87% cervical cancer deaths occur in less developed regions. The estimation of new cancer cases, by major states of India, reveals that burden is very high, in those states which are highly populous. Nearly 41.3% of cancers seen in Indian females are accounted by cancer of cervix. Cervical cancer is a huge trouble in most agricultural nations, where it is a significant reason for mortality and morbidity among females. Several factors are attributed to the widespread incidence of cancer, the precise etiology of which remains unclear. Awareness of cancer should be encouraged in its prevention, detection, and treatment. Indian women face a 2.5% cumulative life me risk and 1.4% cumulative death risk from cervical cancer, at any given time. Nearly 6.6% of women in the general popular on are estimated to harbor cervical HPV infection. Early detection and treatment via screening can prevent up to 80% of cervical cancers in developed countries, where efficient screening programs are in place. This article review the current status of cervical cancer in India and the key barriers affecting women's participation in the cervical screening programme. India has pressing need to foster cervical cancer screening project and local area level endeavors to further develop information about cervical disease and screening programs. This work would assist with saving a huge number of young females and their families from an incredible disaster. [ABSTRACT FROM AUTHOR]

Schizophrenia (SZ) is a severe progressive neurodegenerative as well as disruptive behavior disorder affecting innumerable people throughout the world. The discovery of potential biomarkers in the clinical scenario would lead to the development of effective methods of diagnosis and would provide an understanding of the prognosis of the disease. Moreover, breakthrough inventions for the treatment and prevention of this mysterious disease could evolve as a result of a thorough understanding of the clinical biomarkers. In this review, we have discussed about specific biomarkers of SZ an emphasis has been laid to delineate (1) diagnostic biomarkers like neuroimmune biomarkers, metabolic biomarkers, oligodendrocyte biomarkers and biomarkers of negative and cognitive symptoms, (2) therapeutic biomarkers like various neurotransmitter systems and (3) prognostic biomarkers. All the biomarkers were evaluated in drug-naïve (at least for 4 weeks) patients in order to achieve a clear comparison between schizophrenic patients and healthy controls. Also, an attempt has been made to elucidate the potential genes which serve as predictors and tools for the determination of biomarkers and would ultimately help in the prevention and treatment of this deadly illness. [ABSTRACT FROM AUTHOR]

Lump in the breast, discharge of blood from the nipple, and deformation of the nipple/breast and its texture are the symptoms of breast cancer. Though breast cancer is very common in women, men can also get breast cancer. In the early stages, BCD makes use of Thermal Mammograms Breast Images (TMBI). The cost of treatment can be severely reduced in the early stages of detection. Based on the techniques of segmentation, the Breast Cancer Detection (BCD) works. Moreover, by providing a balanced, reliable and appropriate second opinion, a tremendous role has been played by ML in medical practices due to enhanced Information and Communication Technology (ICT). For the purpose of making the whole detection process of Malignant Tumor (MT)/Benign Tumor (BT) very resourceful and timeefficient, there is now a possibility to form an automated and precise Computer-Aided Diagnosis System (CADs). Several Image Pattern Recognition Techniques were used to classify breast cancer using Thermal Mammograms Image Processing Techniques (TMIPT) in the present investigation. Presenting a new model to classify the BCD with the help of TMIPT, thermal imaging, and smart devices is the aim of this research article. Using well-designed experiments like Intensive Preoperative Radio Therapy (IPRT) and BCD, the implementation and valuation of a concrete application are carried out. This proposed method is for the automatic classification of TMBI of a similar standard so that the thermal camera of FLIR One Gen 3 One 3rd Generation (FLIR One Gen 3) that can be attached to the smart devices are capable of capturing BCD using Machine Learning (ML) algorithms. To imitate the behaviour of human Artificial Intelligence (AI), designing drug formulations, helping in clinical diagnosis and robotic surgery systems, finding medical statistical datasets, and decoding human diseases' wireless network model as well as cancer are the reasons for the ML to empower the computer and robots. The outperformance of the ML models against all other classifiers and scoring impressively across heterogeneous performance metrics like 98.44% of Precision, 98.83% of Accuracy, and 100% of Recall are observed from the comparative analysis. [ABSTRACT FROM AUTHOR]

The use of an automatic query expansion technique is to enhance the performance of the Information Retrieval System. Selecting the candidate terms for query expansion is an essential task to make query more precise to extract the most suitable documents. This paper provides a method to select the best terms for query enhancement. Firstly, the effect of abbreviation resolution, Lexical Variation, Synonyms, n-gram pseudo-relevance feedback, Co-occurrence method on baseline approaches of query expansion is analyzed.. In this work, we used the Okapi BM25 algorithm for ranking. We used Concept-based normalization to deal with concept terms. Here our results show the improvement in results than the baseline approach. A new combined technique that integrates lexical variation, synonyms, n-gram pseudo relevance feedback for query enhancement is proposed. For experimental purpose three English written datasets CACM, CISI, and TREC-3 is used. The obtained results show improvement in the performance of query expansion concerning mean average precision, F-measure, and precision-recall curve. [ABSTRACT FROM AUTHOR]

Sarcasm is a language phrase that conveys the polar opposite of what is being said, generally something highly unpleasant to offend or mock somebody. Sarcasm is widely used on social media platforms every day. Because sarcasm may change the meaning of a statement, the opinion analysis procedure is prone to errors. Concerns about the integrity of analytics have grown as the usage of automated social media analysis tools has expanded. According to preliminary research, sarcastic statements alone have significantly reduced the accuracy of automatic sentiment analysis. Sarcastic phrases also impact automatic fake news detection leading to false positives. Various individual natural language processing techniques have been proposed earlier, but each has textual context and proximity limitations. They cannot handle diverse content types. In this research paper, we propose a novel hybrid sentence embedding-based technique using an autoencoder. The framework proposes using sentence embedding from long short term memory-autoencoder, bidirectional encoder representation transformer, and universal sentence encoder. The text over images is also considered to handle multimedia content such as images and videos. The final framework is designed after the ablation study of various hybrid fusions of models. The proposed model is verified on three diverse real-world social media datasets—Self-Annotated Reddit Corpus (SARC), headlines dataset, and Twitter dataset. The accuracy of 83.92%, 90.8%, and 92.80% is achieved. The accuracy metric values are better than previous state-of-art frameworks. [ABSTRACT FROM AUTHOR]

Globally, cities are emerging into Smart Cities (SC) as a result of sustainable cities and the adaption of recent Internet of Things (IoT) technology. It is becoming essential to involve students in sustainability as engineering and technology are crucial elements in fixing the past adverse effects on our globe. Engineering e-learners are being educated on the sustainable development of SC in many Smart e-learning Tools (SeT) and infrastructure faculties around the world, especially in developing Asian countries such as India. This research paper presents an advanced solution for interactive Smart Learning Environment (SLE) systems based on new IoT technologies in the Virtual Reality (VR) and Augmented Reality (AR) found in Smart Learning Environments (SLE) for SC people. The proposed IoT-Ve-LS system provides an optimized solution for online classes to attend classes using VR/AR glasses to feel the interactions between Smart Digital Devices (SDD) as practically as in practice. The new Virtual e-Learning System (Ve-LS) is experimental, allows automatic Information and Communications Technology (ICT) development, and offers an extraordinary SLE for increased global recognition. This paper focuses on IoT-Ve-LS, a tool for SLE. The IoT-Ve-LS domain has been fast-growing through the emerging technological trends of the IoT. The IoT-Ve-LS method used in the design and implementation allows flexible usage and integration of the online courses by SLE. The impacts of empirical E-learning evaluation on implementing IoT techniques in online tutoring have been analysed to find out its research hypothesis. Our IoT-sensor-based Reservoir Computing allows the classification of short-term learning language sentences relatively quickly, highlighting the minimal training time and optimized solution of real-time cases for controlling temporal and sequential signals at the cloud computing level. The triangulation analysis in information gathering endorses the theoretical models that use computable and personalized approaches. [ABSTRACT FROM AUTHOR]

Small shrub known as Guggul [Commiphora wightii (Arn.) Bhandari] is found in tropical and subtropical areas. It is supposed to be originated in arid regions of central Asia and northern Africa prevalent in the eastern Himalayas and western India on Rocky Tracks. The gum and bark of the plant are effective in treating obesity, arthritis, indolence, piles, gonorrhoea, cough, hernia, and leucoderma. Guggul is a 1.8-4.0 m tall, shrub with tangled, twisted branches and glandularpubescent juvenile portions. Traditionally, it is used to treat several diseases by the tribes or ethnic groups in various parts of the country. It is pharmaceutically and therapeutically relevant owing to the existence of different secondary metabolites. [ABSTRACT FROM AUTHOR]

The existence of mankind depends on the existence of plants and organisms. An important step towards protection of the local and endangered plants can be conservation of the local plant communities in the premises of educational and government institutions. Vardhman Mahaveer Open University Campus (25 acres) is a unique area. In an exploratory survey of VMOU campus, a total of 113 plant species under 45 families have been recorded. Of these 113 species, 12 are shrubs (11 %), 33 Herbs (29 %) 29 climbers (26 %) and 39 species belong to trees (34 %). Fabaceae is found to be dominant having highest (22) number of species followed by Apocynaceae (07). The present study deals with plant diversity of angiosperms found in the campus of VMOU Campus, Kota, Rajasthan. [ABSTRACT FROM AUTHOR]

Health complications during the gestation period have evolved as a global issue. These complications sometimes result in the mortality of the fetus, which is more prevalent in developing and underdeveloped countries. The genesis of machine learning (ML) algorithms in the healthcare domain have brought remarkable progress in disease diagnosis, treatment, and prognosis. This research deploys various ML algorithms to predict fetal health from the cardiotocographic (CTG) data by labelling the health state into normal, needs guarantee, and pathology. This work assesses the influence of various factors measured through CTG to predict the health state of the fetus through algorithms like support vector machine, random forest (RF), multi‐layer perceptron, and K‐nearest neighbours. In addition to this, the regression analysis and correlation analysis revealed the influence of the attributes on fetal health. The results of the algorithms show that RF performs better than its peers in terms of accuracy, precision, recall, F1‐score, and support. This work can further enhance more promising results by performing suitable feature engineering in the CTG data. [ABSTRACT FROM AUTHOR]

The chief objective of this study is to utilize non‐edible, carbon‐rich oil derived from Prunus domestica seeds as blended fuel. The combustion efficiency of derived blends shall further be enhanced by blending higher alcohol into biodiesel/diesel blends. This work details the impact of effectively utilizing waste and non‐edible oil to replace fossil fuels partially. In this study, the P. domestica is trans‐esterified to methyl ester and blended with diesel at 20%. Further to enhance the ignition nature, butanol, higher alcohol, is blended to P. domestica biodiesel/diesel blends at 5% and 10%. The ignition pattern of the modified fuels is extensively studied and compared with diesel in a stationary research diesel engine. This study involves four fuel samples, namely, neat P. domestica methyl ester and is termed PDME. 20% of PDME is blended with 80% diesel is referred to as PDME20. 20% of PDME with 5% n‐butanol and 75% diesel is termed PDME20Bu5D75, 20% of PDME with 10% n‐butanol and 70% diesel is termed as PDME20Bu10D70 and diesel. Addition of PDME and butanol at different blends found miscible with diesel. PDME and butanol provided a suitable time for reaction and mixing, complete combustion, higher O2 content, and rich fuel‐air mixture, resulting in lower emissions than diesel. Further, butanol aided better atomization, effective combustion and resulted in higher efficiency with lower fuel consumption. Adding butanol also lowered the viscosity and improved the mixing process, and produced better in‐cylinder pressure and heat release rate. [ABSTRACT FROM AUTHOR]

Computing has become more invisible, widespread and ubiquitous since the inception of the Internet of Things (IoT) and Web of Things. Multiple devices that surround us meet user's requirements everywhere. Multiple Middleware Framework (MF) designs have come into existence because of the rapid development of interactive services in Heterogeneous Systems. This resulted in the delivery of interactive services throughout Heterogeneous Environments (HE). Users are given free navigation between devices in a widespread environment and continuously interact with each other from any chosen device. Numerous interactive devices with recent interactive platforms (for example, Smart Phones, Mobile Phones, Personal Computer (PC) and Personal Digital Assistant (PDA)) are available in the market. For easy access to information and services irrespective of the device used for working and even at the drastic change of the environment, the execution of applications on a broad spectrum of computing devices is propelled by the availability of the above-mentioned platforms. Different applications that need interoperability to coordinate and correspond with each other should be facilitated. Using a standard interface and data format, HE must link various devices from various platforms together to communicate with each other. To aid the interactive services performed by a middleware framework that operates on Application Programming Interface (API) over HEs, this issue aims to endorse an Adaptable Service Application Programming Interface (ASAPI). [ABSTRACT FROM AUTHOR]

Cloud computing is an Information Technology deployment model established on virtualization. Task scheduling states the set of rules for task allocations to an exact virtual machine in the cloud computing environment. However, task scheduling challenges such as optimal task scheduling performance solutions, are addressed in cloud computing. First, the cloud computing performance due to task scheduling is improved by proposing a Dynamic Weighted Round-Robin algorithm. This recommended DWRR algorithm improves the task scheduling performance by considering resource competencies, task priorities, and length. Second, a heuristic algorithm called Hybrid Particle Swarm Parallel Ant Colony Optimization is proposed to solve the task execution delay problem in DWRR based task scheduling. In the end, a fuzzy logic system is designed for HPSPACO that expands task scheduling in the cloud environment. A fuzzy method is proposed for the inertia weight update of the PSO and pheromone trails update of the PACO. Thus, the proposed Fuzzy Hybrid Particle Swarm Parallel Ant Colony Optimization on cloud computing achieves improved task scheduling by minimizing the execution and waiting time, system throughput, and maximizing resource utilization. [ABSTRACT FROM AUTHOR]

Irretrievable loss of vision is the predominant result of Glaucoma in the retina. Recently, multiple approaches have paid attention to the automatic detection of glaucoma on fundus images. Due to the interlace of blood vessels and the herculean task involved in glaucoma detection, the exactly affected site of the optic disc of whether small or big size cup, is deemed challenging. Spatially Based Ellipse Fitting Curve Model (SBEFCM) classification is suggested based on the Ensemble for a reliable diagnosis of Glaucomain theOptic Cup (OC) and Optic Disc (OD) boundary correspondingly. This research deploys the Ensemble Convolutional Neural Network (CNN) classification for classifying Glaucoma or Diabetes Retinopathy (DR). The detection of the boundary between the OC and the OD is performed by the SBEFCM, which is the latest weighted ellipse fitting model. The SBEFCM that enhances and widens the multi-ellipse fitting technique is proposed here. There is a preprocessing of input fundus image besides segmentation of blood vessels to avoid interlacing surrounding tissues and blood vessels. The ascertaining of OCandODboundary, which characterizedmany output factors for glaucoma detection, has been developed by EnsembleCNNclassification, which includes detecting sensitivity, specificity, precision, andArea Under the receiver operating characteristic Curve (AUC) values accurately by an innovative SBEFCM. In terms of contrast, the proposed Ensemble CNNsignificantly outperformed the current methods. [ABSTRACT FROM AUTHOR]

Recently, an innovative trend like cloud computing has progressed quickly in InformationTechnology. For a background of distributed networks, the extensive sprawl of internet resources on the Web and the increasing number of service providers helped cloud computing technologies grow into a substantial scaled Information Technology service model. The cloud computing environment extracts the execution details of services and systems from end-users and developers. Additionally, through the system's virtualization accomplished using resource pooling, cloud computing resources become more accessible. The attempt to design and develop a solution that assures reliable and protected authentication and authorization service in such cloud environments is described in this paper. With the help of multi-agents, we attempt to represent Open-Identity (ID) design to find a solution that would offer trustworthy and secured authentication and authorization services to software services based on the cloud. This research aims to determine how authentication and authorization services were provided in an agreeable and preventive manner. Based on attack-oriented threat model security, the evaluation works. By considering security for both authentication and authorization systems, possible security threats are analyzed by the proposed security systems. [ABSTRACT FROM AUTHOR]

All living things, including plants, animals, and humans, need water in order to live. Even though the world has a lot of water, only about 1% of it is fresh and usable. As the population has grown and water has been used more, fresh water has become a more valuable and important resource. Agriculture uses more than 70% of the world's fresh water. People who work in agriculture are not only the world's biggest water users by volume, but also the least valuable, least efficient, and most subsidized water users. Technology like smart irrigation systems must be used to make agricultural irrigation more efficient so that more water is used. A system like this can be very precise, but it needs information about the soil and the weather in the area where it is going to be used. This paper analyzes a smart irrigation system that is based on the Internet of Things and a cloud-based architecture. This system is designed to measure soil moisture and humidity and then process this data in the cloud using a variety of machine learning techniques. Farmers are given the correct information about water content rules. Farming can use less water if they use smart irrigation. [ABSTRACT FROM AUTHOR]

Diesel engines are lean burn engines; hence CO and HC emissions in the exhaust are less likely to occur in substantial amounts. The emissions of serious concern in compression ignition engines are particulate matter and nitrogen oxides because of elevated temperature conditions of combustion. Hence the researchers have strived continuously to lower down the temperature of combustion in order to bring down the emissions from CI engines. This has been tried through premixed charge compression ignition, homogeneous charge compression ignition (HCCI), gasoline compression ignition and reactivity controlled compression ignition (RCCI). In this study, an attempt has been made to critically review the literature on low-temperature combustion conditions using various conventional and alternative fuels. The problems and challenges augmented with the strategies have also been described. Water-in-diesel emulsion technology has been discussed in detail. Most of the authors agree over the positive outcomes of water-diesel emulsion for both performance and emissions simultaneously. [ABSTRACT FROM AUTHOR]

This research presents a mathematical model using the exergy analysis of evacuated tube compound parabolic concentrator under the meteorological conditions of Jaipur–India. Moreover, the effect of hourly variation of solar radiation intensity and ambient temperature over the exergetic efficiency of evacuated tube compound parabolic concentrator are analyzed. The maximum exergetic efficiency was 12.83%, whereas day-wise efficiency varied from 4.70 to 8.45% on average. The maximum energy and exergy gain recorded are 252.2 and 46.84 kW h per day. Exergy destructions are highest during energy transfer from the absorber to the receiver tubes (46%), followed by exergy destructions due to optical (36%). [ABSTRACT FROM AUTHOR]