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A versatile approach based on convolutional neural networks for early identification of diseases in tomato plants
Agriculture is one of the primary occupations in many countries. Tomatoes are grown by many farmers in countries where the water resource is available in abundance. Improper methods of cultivation and failure to identify the diseases when it is in the nascent stage results in the reduction of crop yield thus affecting the outcome of cultivation. This paper proposes a novel method of early identification of diseases in tomato plants by making use of convolutional neural networks (CNN) and image processing. Dataset from an open repository was considered for training and testing and the algorithm was capable of identifying nine different varieties of diseases that affect the tomato plant at its early stages. The images of tomato leaves were fed for identification through processing and classification. An optimum model was developed by analyzing various architectures of CNN including the VGG, ResNet, Inception, Xception, MobileNet and DenseNet. The performance of each of these architectures was compared and various metrics like the accuracy, loss, precision, recall and area under the curve (AUC) were analyzed. 2022 World Scientific Publishing Company. -
A versatile sensor capable of ratiometric fluorescence detection of trace water and turn-on detection of Cu2+ modulating the binding interaction of a Cu(ii) complex with BSA and DNA complemented by docking studies
A fluorescent molecule, pyridine-coupled bis-anthracene (PBA), has been developed for the selective fluorescence turn-on detection of Cu2+. Interestingly, the ligand PBA also exhibited a red-shifted ratiometric fluorescence response in the presence of water. Thus, a ratiometric water sensor has been utilized as a selective fluorescence turn-on sensor for Cu2+, achieving a 10-fold enhancement in the fluorescence and quantum yield at 446 nm, with a lower detection limit of 0.358 ?M and a binding constant of 1.3 106 M?1. For practical applications, sensor PBA can be used to detect Cu2+ in various types of soils like clay soil, field soil and sand. The interaction of the PBA-Cu(ii) complex with transport proteins like bovine serum albumin (BSA) and ct-DNA has been investigated through fluorescence titration experiments. Additionally, the structural optimization of PBA and the PBA-Cu(ii) complex has been demonstrated by DFT, and the interaction of the PBA-Cu(ii) complex with BSA and ct-DNA has been analyzed using theoretical docking studies. 2024 The Royal Society of Chemistry. -
A Video Surveillance-based Enhanced Collision Prevention and Safety System
Road traffic crashes that result in fatalities have become a global phenomenon. Therefore, it is imperative to use caution and vigilance while being on the road. Human mistake, going over the speed limit, being preoccupied while driving or walking, disobeying safety precautions, and other factors can also contribute to such unforeseen accidents or injuries, which can result in both bodily and material loss. So, safety is what we seek to achieve. Furthermore, as the number of automobiles has increased, so too have collisions between vehicles and pedestrians. Using computer vision and deep learning approaches, this research seeks to anticipate such encounters. The data often comes from traffic surveillance cameras in video formats. We have therefore concentrated on video sequences of vehicle-pedestrian collisions. We begin with a detection phase that includes the identification of vehicles and pedestrians; for this phase, we employed YOLO v3 (You Only Look Once). YOLO v3 has 80 classes, but we only took six of them: person, car, bike, motorcycle, bus, and truck. Following detection, the Euclidean distance approach is used to determine the interspace between the vehicle and the pedestrian. The closer the distance between a vehicle and a pedestrian, the more likely it is that they will collide. As a result, pedestrians in risk are located, and once we are aware of the pedestrians in danger, we search for nearby safer regions to alert them to head to the nearest location that is secure. Grenze Scientific Society, 2023. -
A Voting Enabled Predictive Approach for Hate Speech Detection
In today's digital environment, hate speech, which is defined as disparaging and discriminating communication based on personal characteristics, presents a big difficulty. Hate crimes and the rising amount of such content on social media platforms are two examples of how it is having an impact. Large volumes of textual data require manual analysis and categorization, which is tedious and subject to prejudice. Machine learning (ML) technologies have the ability to automate hate speech identification with increased objectivity and accuracy in order to overcome these constraints. This article intends to give a comparative analysis of various ML models for the identification of hate speech. The proliferation of such content online and its negative repercussions on people and society are explored, as is the necessity for automated hate speech recognition. This paper intends to support the creation of efficient hate speech detection systems by performing a comparative analysis of ML models. Random forest records the best performance with higher accuracy and low response delay period for hate speech detection. The results will help enhance automated text classification algorithms and, in the end, promote a safer and more welcoming online environment by illuminating the benefits and drawbacks of various approaches. 2023 IEEE. -
A Way Towards Next-Gen Networking System for the Development of 6G Communication System
In this talk, the advancements announced by sixth-generation mobile communication (6G) as compared to the earlier fifth-generation (5G) system are carefully examined. The analysis, based in existing academic works, underscores the goal of improving diverse communication aims across various services. This study finds five crucial 6G core services designed to meet distinct goal requirements. To explain these services thoroughly, the framework presents two central features and delineates eight significant performance indices (KPIs). Furthermore, a thorough study of supporting technologies is performed to meet the stated KPIs. A unified 6G design is suggested, imagined as a combination of these supporting technologies. This design plan is then explained by the lens of five prototype application situations. Subsequently, possible challenges contained in the developing track of the 6G network technology are carefully discussed, followed by suggested solutions. The debate ends in an exhaustive examination of possibilities within the 6G world, seeking to provide a strategy plan for future research efforts. 2024 IEEE. -
A web forensic optimization framework for investigating false information on social media using the ForenOptiNet model
Todays technological advancements in the field of digital media have resulted in the unprecedented transmission of information leading to unauthorized exploitation. Businesses use social media as the primary marketing platform. Considering the severity of spreading misinformation and fake news in our society due to false marketing by bogus businesses, there is a great need to demystify this propagation using web forensics-based frameworks. In order to increase consumer equity, the rapid spreading of malicious information makes it hard for users to differentiate between real and false information. This research intends to design an effective and adaptable framework for detecting false information campaign carried out by criminals affecting online social network (ONS). A novel ForenOptiNet model is designed and diverse data gathered from the Reddit and INFD dataset is used to train the suggested model. The Web Forensic-Based Investigation Optimization (WFBIO) algorithm provides a high accuracy classification of malicious content from the web. Moreover, the WFBIO framework enhances the robustness of the ForenOptiNet model and ensures that the proposed approach can effectively identifies misinformation and fake news to validate factual claims. Results of the simulation analysis provides a muti-level mechanism combining anomaly detection and ForenOptiNet model together outperforming other state-of the-art optimization algorithms trained against CNNs with SGD, Adagrad and AdaDelta. While these baselines yielded accuracies between 55 and 92%, our proposed model achieved highest accuracy of 99% accuracy with an effective front-end design integration. The Author(s) 2025. -
A weighted-Weibull distribution: Properties and applications
The paper describes a two parameter model and its relationship to the widely used Weibull model. Mathematical properties of the distribution like survival and hazard functions, moments, harmonic and geometric means, Shannon entropy and mean residual life are derived. Different methods of estimation are discussed and a simulation study is performed to verify the efficiency of estimation methods. Applications of our distribution in different scenarios observed in real life areillustrated. 2023 John Wiley & Sons Ltd. -
A Worldwide Test of the Predictive Validity of Ideal Partner Preference Matching
Ideal partner preferences (i.e., ratings of the desirability of attributes like attractiveness or intelligence) are the source of numerous foundational findings in the interdisciplinary literature on human mating. Recently, research on the predictive validity of ideal partner preference matching (i.e., Do people positively evaluate partners who match vs. mismatch their ideals?) has become mired in several problems. First, articles exhibit discrepant analytic and reporting practices. Second, different findings emerge across laboratories worldwide, perhaps because they sample different relationship contexts and/or populations. This registered reportpartnered with the Psychological Science Acceleratoruses a highly powered design (N = 10,358) across 43 countries and 22 languages to estimate preference-matching effect sizes. The most rigorous tests revealed significant preference-matching effects in the whole sample and for partnered and single participants separately. The corrected pattern metric that collapses across 35 traits revealed a zero-order effect of ? =.19 and an effect of ? =.11 when included alongside a normative preference-matching metric. Specific traits in the level metric (interaction) tests revealed very small (average ? =.04) effects. Effect sizes were similar for partnered participants who reported ideals before entering a relationship, and there was no consistent evidence that individual differences moderated any effects. Comparisons between stated and revealed preferences shed light on gender differences and similarities: For attractiveness, mens and (especially) womens stated preferences underestimated revealed preferences (i.e., they thought attractiveness was less important than it actually was). For earning potential, mens stated preferences underestimatedand womens stated preferences overestimatedrevealed preferences. Implications for the literature on human mating are discussed. 2024 American Psychological Association -
A worldwide test of the predictive validity of ideal partner preference matching.
Ideal partner preferences (i.e., ratings of the desirability of attributes like attractiveness or intelligence) are the source of numerous foundational findings in the interdisciplinary literature on human mating. Recently, research on the predictive validity of ideal partner preference matching (i.e., Do people positively evaluate partners who match vs. mismatch their ideals?) has become mired in several problems. First, articles exhibit discrepant analytic and reporting practices. Second, different findings emerge across laboratories worldwide, perhaps because they sample different relationship contexts and/or populations. This registered reportpartnered with the Psychological Science Acceleratoruses a highly powered design (N = 10,358) across 43 countries and 22 languages to estimate preference-matching effect sizes. The most rigorous tests revealed significant preference-matching effects in the whole sample and for partnered and single participants separately. The corrected pattern metric that collapses across 35 traits revealed a zero-order effect of ? =.19 and an effect of ? =.11 when included alongside a normative preference-matching metric. Specific traits in the level metric (interaction) tests revealed very small (average ? =.04) effects. Effect sizes were similar for partnered participants who reported ideals before entering a relationship, and there was no consistent evidence that individual differences moderated any effects. Comparisons between stated and revealed preferences shed light on gender differences and similarities: For attractiveness, men's and (especially) women's stated preferences underestimated revealed preferences (i.e., they thought attractiveness was less important than it actually was). For earning potential, men's stated preferences underestimatedand women's stated preferences overestimatedrevealed preferences. Implications for the literature on human mating are discussed. (PsycInfo Database Record (c) 2025 APA, all rights reserved) 2024 American Psychological Association All rights, including for text and data mining, AI training, and similar technologies, are reserved. -
AADS: An Automated Accident Detection and Nighttime Surveillance System Using Fine-Tuned YOLOv10 Deep Learning Techniques
Computer vision-based surveillance is very important today's security systems to detect, track and regulate the security much better than standard cameras. However, like any other performance measurement systems they have potential pitfalls and technical, ethical, and legal implications must be well understood. The continuous rise in connection and interaction implies that safety of the public especially when navigating roads or operating in public domains is paramount. The conventional approaches to accident identification include observation or reporting from witnesses and always record slow and imprecise outcomes. With the improvement of AI and computer visions, especially with deep learning models such as YOLO, accident detection is changing. YOLO v10 which is incorporated in the surveillance systems, performs real time video analysis to provide object and pattern recognition of accidents including car accidents and incidents involving the pedestrians. When applied to the initial set of annotated accident images, the fine-tuning of the YOLO v10 model enhances its detection capability. The system is in watching the video frames that contain aberrations and issues and alarms are issued when the accidents happen and relayed to the monitoring stations or emergency departments for proper response. The optimized YOLOv10 here delivers a meaningful testing score of 72.3% mAP to outperform the regular YOLOv10 efficiency in incident detection. 2025 IEEE. -
Abelian-Type Results for the Mexican Hat Wavelet Transform of Compactly Supported Distributions
In this paper, we introduce a distribution space that extends the framework of the Abelian theorems to the Mexican hat wavelet transform (MHWT) of distributions. We establish two Abelian theorems for the MHWT applied to compactly supported distributions and for locally integrable functions, providing new insights into their asymptotic behavior. 2025 John Wiley & Sons Ltd. -
Abjection and Intersecting Trans Women Identities: Examining Doing Gender through Malayalam Movies Ardhanaari and Njan Marykutty; [Abjeo e Identidades de Mulheres Trans Interseccionadas: Examinando Fazendo Gero atrav dos Filmes em Malayalam Ardhanaari e Njan Marykutty]; [Examinando hacer gero a trav de las pelulas en malayalam Ardhanaari y Njan Marykutty]
The non-confirmation to vexed societal gender norms places trans identities in an abjected state. Media, mainly cinema, plays an indispensable role in shaping, shunning, and promulgating such ideologies. To understand this discourse, the Malayalam films Ardhanaari (2015) and Njan Marykutty (2018) are taken to examine the question of abjection, a concept by Kristeva, and doing gender, by West and Zimmerman. The study argues that the abjection trans identities face forces them to perform their gender in accordance with cisnormative femininity. The study further argues that trans identities should embrace abjection and employ it as a political tool to disrupt the established hegemonic traditional gender structure and its definitions. 2023 Universidad de Guadalajara. All rights reserved. -
ABL as a linguistic pedagogy-An experimental study of acquisition of English language among primary school children
The history of English language teaching has been regarded as a pursuit for more effective ways of teaching and learning. The teaching profession has held constant discussions and debates over issues like framing effective learning strategies,techniques for teaching the four skills to the students, choice of curriculum frameworks, materials available for effective English language teaching, role of grammar and vocabulary in English classrooms, teaching English language using technology, improvement of fluency and accuracy during the process of language learning and so on. English language experts are constantly engaged in clarifying these areas including the search for an appropriate approach of teaching English language effectively in classrooms. -
Ableisms Impact on Body and Identity in Indra Sinhas Animals People
This study explores the link between ableism and identity formation through the discourse analysis of Indra Sinhas Animals People (2007). Set against the backdrop of a post-industrial disaster, the Bhopal gas tragedy in an Indian metropolis, the novel provides a powerful narrative about the marginalised. By analysing the social perceptions and structures that define ability, normalcy, and physical beauty, the study investigates how societal norms and cultural attitudes frame the protagonists experiences of exclusion and identity crisis. The study further investigates how the novel critiques the broader social and cultural dimensions of ableism in post-colonial contexts, revealing the intersection of power, disability, and identity in the social fabric of Asian societies. Social constructionist perspectives provide a framework for comprehending the social construction of disability, stigma, othering of disabled bodies, and cultural norms of beauty, normalcy, ability, and identity. Subjective and objective realities are discussed around the character, Animal. The study findings reveal the profound personal consequence of ableism on the self-image, body image, and self-perception of individuals with disabilities, the dehumanisation, marginalisation, and ultimately, an identity crisis of disabled individuals. 2025 International Islamic University Malaysia. All rights reserved. -
Abusive Words Detection on Reddit Comments Using Machine Learning Algorithms
Utilization of artificial intelligence contributes to the efficient examination of emotions, resulting in valuable insights into the psychological condition of users on a large scale. In this research endeavor, sentiment analysis is conducted on a dataset from Reddit, which was obtained through Kaggle. The feedback in this collection of data was divided into downbeat, neutral, and upbeat sentiments. Various machine learning techniques, like Random Forest, Extreme Gradient Boosting Classifier (XGB), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and Convolutional Neural Network (CNN), were detected and examined to assess their effectiveness in sentiment classification. The review of these techniques comprised performance criteria such as F1 Score, accuracy, precision, and recall. Additionally, confusion matrices were utilized to assess the algorithms' proficiency in identifying abusive language. The investigation's conclusions indicate that, when it comes to sentiment analysis, the random forest method performs better than any other strategy, with a maximum accuracy of 0.99 that is on par with the CNN model's accuracy of 0.98. Moreover, random forest proves to be the most effective algorithm in recognizing negative comments and abusive language. This study underscores the significance of employing machine learning algorithms in sentiment analysis, content moderation, social media monitoring, and customer feedback analysis, emphasizing their role in enhancing automated systems that aim to comprehend user sentiments in online discussions. 2024 IEEE. -
Acacia auriculiformisDerived Bimodal Porous Nanocarbons via Self-Activation for High-Performance Supercapacitors
Carbon nanomaterials derived from Acacia auriculiformis pods as electrodes for the electrochemical double-layer capacitors were explored. Four pyrolysis temperatures were set (400, 600, 800, and 1,000C) to understand the role of temperature in biomass pyrolysis via a possible self-activation mechanism for the synthesis of carbon materials. The carbon materials synthesized at 800C (AAC800) were found to exhibit a well-organized hierarchical porous structure, quantified further from N2 adsorption/desorption isotherms with a maximum specific surface area of 736.6m2/g. Micropores were found to be contributing toward enhancing the specific surface area. AAC800 exhibited a maximum specific capacitance of 176.7F/g at 0.5A/g in 6.0M KOH electrolyte in a three-electrode setup. A symmetric supercapacitor was fabricated using AAC800 as an active material in an organic electrolyte composed of 1.0M tetraethylammonium tetrafluoroborate (TEABF4) as a conducting salt in the acetonitrile (ACN) solvent. The self-discharge of the cell/device was analyzed from fitting two different mathematical models; the cell also exhibited a remarkable coulombic efficiency of 100% over 10,000 charge/discharge cycles, retaining ?93% capacitance at 2.3V. Copyright 2021 Bhat, Jayeoye, Rujiralai, Sirimahachai, Chong and Hegde. -
Academic Certificate Validation Using Blockchain Technology
Academic certificates are essential for an individual's career and hence they are more prone to being tampered. This paper proposes an idea of sharing certificates and verifying their authenticity using blockchain technology. Blockchain paves the way for secure storage and sharing of information. Its main focus is to maintain trust among users. This proposal focuses on designing and implementing a system that will prove to be a solution for addressing the issue of fake certificates using Hyperledger Fabric. The technology here is tamper-proof and maintains transparency. This system will have a database of academic certificates awarded by the University, which is recorded as a transaction using the Hyperledger Fabric, which further can be referred by other organizations present in the network to verify the authenticity of the certificates using the information provided by the students to the database. This system provides end to end encryption. 2022 IEEE. -
Academic leader behaviour influence tactics in relation to organizational commitment and work engagement of faculty in higher educational institutions
The importance of academic leader behaviour and influence tactics needs to be overemphasized, as these are the most essential components of practically every newlineeducational institution. A clear understanding of educational institution requires a thorough analysis of academic leader behaviour and influence tactics as main features. Academic leaders regularly acquire and use power. They do so newlinedeliberately and consciously as well as intuitively and unconsciously. Leadership newlineand power do differ in goal compatibility, direction of influence on one s newlinesubordinates and research emphasis. This study attempts to reduce drastically these newlinedifferences and focus on the positives of influence tactics and leadership processes newlineto be exercised by higher authorities for enhancing institutional effectiveness. newlineThe present investigation focused on understanding the leader s behaviour and influence tactics adopted by individuals when they hold power positions, how it hinders the growth of individuals and institutions goals. Faculty Organisational Commitment, Work Engagement and the intention of stay/leave the institution of both academic leaders and faculty members working for higher educational institutions (Engineering, MBA and MCA colleges) were involved in thorough newlineinvestigation. The dependent variables were work engagement and Organisational newlinecommitment. Five tools were adopted to collect data. Leader Behaviour Description Questionnaire (1962) developed by staff members of Fisher College of Business, Ohio State Leadership Studies, Influence Behaviour Questionnaire (2002) developed by Gary Yukl, Organizational Commitment Questionnaire newline(1991) developed by Meyer and Allen, Utrecht Work Engagement Scale (2003) developed by Schaufeli et al., and Intention to stay/leave tool developed by Dilyis Robinson. The Cronbach Alpha reliability for Leadership Behaviour Description Questionnaire (LBDQ) was 0.907; Influence Behaviour Questionnaire (IBQ) was 0.677 for academic leaders. -
Academic leader behaviour, influence tactics in relation to organisational commitment and work engagement of faculty in higher educational institutions
The importance of academic leader behaviour and influence tactics needs to be overemphasized, as these are the most essential components of practically every educational institution. A clear understanding of educational institution requires a thorough analysis of academic leader behaviour and influence tactics as main features. Academic leaders regularly acquire and use power. They do so deliberately and consciously as well as intuitively and unconsciously. Leadership and power do differ in goal compatibility, direction of influence on one’s subordinates and research emphasis. This study attempts to reduce drastically these differences and focus on the positives of influence tactics and leadership processes to be exercised by higher authorities for enhancing institutional effectiveness.