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Lithium photodisintegration with unpolarized photon beams at near threshold energies
The study of photonuclear reactions with lithium targets i.e. photodisintegration of lithium in addition to other photonuclear reactions is of considerable interest to the fields of nuclear physics, astrophysics, laser physics and several applications such as non - destructive testing of nuclear materials. We propose to study photodisintegration of lithium with unpolarized photon beams at near threshold energies. Our model independent theoretical approach, which makes use of irreducible tensor techniques, is well suited for making predictions on the spin observables as well as the differential cross section. In this paper we analyze the reaction channel 7Li + ? ? 6Li + n by using unpolarized photons. 2022 -
An Efficient Deep Learning Framework FPR Detecting and Classifying Depression Using Electroencephalogram Signals
Depression is a common and real clinical disease that has a negative impact on how you feel, how you think, and how you behave. It is a significant burdensome problem. Fortunately, it can also be treated. Feelings of self-pity and a lack of interest in activities you once enjoyed are symptoms of depression. It can cause a variety of serious problems that are real, and it can make it harder for you to work both at home and at work. The main causes include family history, illness, medications, and personality, all of which are linked to electroencephalogram (EEG) signals, which are thought of as the most reliable tools for diagnosing depression because they reflect the state of the human cerebrum's functioning. Deep learning (DL), which has been extensively used in this field, is one of the new emerging technologies that is revolutionizing it. In order to classify depression using EEG signals, this paper presents an efficient deep learning model that allows for the following steps: (a) acquisition of data from the psychiatry department at the Government Medical College in Kozhikode, Kerala, India, totaling 4200 files; (b) preprocessing of these raw EEG signals to avoid line noise without committing filtering; (c) feature extraction using Stacked Denoising Autoevolution; and (d) reference of the signal to estimate true and all. According to experimental findings, The proposed model outperforms other cutting-edge models in a number of ways (accuracy: 0.96, sensitivity: 0.97, specificity: 0.97, detection rate: 0.94). 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
FOPID controller tuning: A comparative study of optimization techniques for an automatic voltage regulator
This study evaluated a fractional order proportional-integral-derivative (FOPID) controller optimization with a fractional filter for an automated voltage regulator (AVR) system. For the suggested controller, a variety of different parameters can be changed. For the purpose of creating the optimum PID controller for an automated voltage regulator system, comparative analysis using multiple optimization methodologies is carried out. The Salp Swarm Algorithm (SSA), Ant Lion Optimization (ALO), and Particle Swarm Optimization algorithm (PSO) are the techniques that are being examined in this study. The settling time, rising time, and overshoot performance indices is being used. The transient responsiveness of the AVR system was increased by each of the recommended optimization techniques in a different way, and early results were optimistic. The comparison with the most ideally tuned FOPID controllers for the AVR system also serves to support the superiority of the suggested controller. 2023 Author(s). -
IRIS Data Classification using Genetic Algorithm Tuned Random Forest Classification
Optimising hyper-parameters in Random Forest is a time-consuming undertaking for several academics as well as professionals. To acquire greater performance hyper-parameters, specialists should explicitly customize a series of hyper-parameter settings. The best outcomes from this manual setting are then modelled and implemented in a random forest algorithm. Several datasets, on the other side, need various prototypes or hyper-parameter combinations, which may be time-consuming. To solve this, we offered various machine learning models and classifiers for correctly optimising hyper-parameters. Both genetic algorithm-based random forest and randomised CV random forest were assessed on performance measures such as sensitivity, accuracy, specificity, and F1-score. Finally, when compared to randomised CV random forest, our suggested model genetic algorithm-based random forest delivers more incredible accuracy. 2022 IEEE. -
LegalMind System and the LLM-based Legal Judgment Query System
LegalMind-GPT represents a notable advancement in legal technology, specifically tailored for the finance sector. This research paper introduces LegalMind-GPT, a system that integrates Large Language Models (LLMs) to develop a Legal Judgment Query System for financial legal contexts. The study focuses on the application of LLMs, particularly LLAMA-2, Claude AI, and FLAN-T5-Base, for interpreting and analysing complex legal documents in finance. The aim is to evaluate the system's effectiveness in providing accurate legal judgments and insights. The comparative analysis of these LLMs shows that LegalMind-GPT, powered by these models, significantly improves the accuracy and efficiency of legal analysis in the finance domain. 2024 IEEE. -
Road Accident Prediction using Machine Learning Approaches
Road accidents create a significant number of serious injuries reported per year and are a chief concern of the world, mostly in underdeveloped countries. Many people have lost their near and dear ones due to these road accidents. Hence a system that can potentially save lives is required. The system detects essential contributing elements for an accident or creates a link among accidents and various factors for the occurrence of accidents. This research proposes an Accident Prediction system that can help to analyze the potential safety issues and predict whether an accident will occur or not. A comparative study of various Machine Learning Algorithms was conducted to check which model can help predict accidents more accurately. The dataset used for this paper is the government record accidents that occurred in a district in India. Logistic Regression, Random Forest, Decision Tree, K-Nearest Neighbor, XGBoost, and Support Vector Machine are among the Machine Learning models used in this paper to predict accidents. The Random Forest algorithm gave the highest accuracy of 80.78% when the accuracies of the Machine Learning models were compared. 2022 IEEE. -
Low temperature synthesis of MoO3 nanoparticles by hydrothermal method: Investigation on their structural and optical properties
Molybdenum trioxide nanoparticles have recently achieved notable attention in optoelectronic and biomedical applications by virtue of their excellent structural, optical, electrical, and catalytic characteristics. The work presented here demonstrates the synthesis of orthorhombic MoO3 through the facile hydrothermal method at low temperature. Structural and optical characterization of the prepared sample was examined. XRD studies and Raman spectroscopy were carried out to study the structural behavior of the sample. The XRD peaks were concordant with the standard peaks of MoO3, which corresponds to the orthorhombic structure of MoO3. Micro-strain effects were also verified by the W-H method using UDM, UDEDM, and USDM. Raman spectroscopic data ascertained the orthorhombic phase of MoO3. From Tauc plot, a wide bandgap value (4.9 eV) was evaluated. In photoluminescence spectroscopy, peaks are related to the transition among the sub-bands of Mo5+ defects. Being a wide bandgap oxide semiconductor, MoO3 is a promising and worthy material for luminescence applications. 2022 -
Automatic Resume Parsing using Greywolf Algorithm Integrated with Strategically Constructed Semantic Skill Ontologies
The quest for finding the right candidate for their post has made the recruiters employ several methods since the beginning of corporate job recruitment. Apart from the skills and the quality of interview, a thing that matters the most and forms the basis of selection is the candidate's resume. Recruiters and companies have a tough time dealing with the several thousands resumes of the candidates which apply, as manually scanning them and finding the right selection can be tough most of the time. In this paper, Natural Language Processing(NLP) methods have been integrated with ontologies to improve the pace and quality of the recruitment process by proposing an automatic resume parser model. The resume of a candidate, along with his LinkedIn and GitHub profiles are weighted and using the Greywolf algorithm, the global maxima of the most deserving and qualified candidate are found and are recommended with a high accuracy of 96.13%. 2020 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0) -
Rough Set Based Ant-Lion Optimizer for Feature Selection
As the area of computational intelligence evolves, the dimensionality of any sort of data gets expanded. To solve this issue, Rough Set Theory (RST) has been successfully used for finding reducts as it requires only supplied data and no additional information. This paper investigates a novel search strategy for minimal attribute reduction based on rough sets and Ant Lion Optimization (ALO). ALO is a nature-inspired algorithm that mimics the hunting mechanism of ant lions, and this is inspired to find the minimum reducts. Datasets from the UCI repository are used in this paper. The experimental results show that the features selected by the proposed method are well classified with reasonable accuracy. 2020 IEEE. -
Delay Minimization Technique to improve the efficiency of Parameter Optimized Hysteretic Current Controlled Parallel Hybrid ETPA in Mobile Communication
This paper proposes a delay minimization technique to improve the efficiency of a parameter-optimized hysteretic current-controlled parallel hybrid envelope tracking power amplifier (etpa). In a hysteretic current-controlled hybrid topology, a linear amplifier operates parallel with a hysteretic current-controlled switching converter. Block level simulation of etpa is performed using the simulink tool. The traditional parameter optimization technique is first implemented, and its limitation is analysed. The proposed delay minimization technique helps to overcome the limitation of the traditional approach and has been proven to be valid for any input frequency. The proposed technique offers an efficiency improvement of 14.9% compared to the traditional technique for an input frequency of 20mhz and provides an average efficiency improvement of 6.26% for an input frequency range of 2mhz to 60mhz. 2024 IEEE. -
A model for analyzing the sustainability performance in educational institutions
During the past two decades innumerable international initiatives have emphasized that education is an imperative for societies to become more sustainable. Sustainable development is the current context in which higher education must begin to focus its action plans. But the present system heavily relies on archaic models which reduce learning and action to reductionist thinking and mechanistic interpretation. Campus sustainability is receiving growing attention and has become a well-established study field, even though campus sustainability itself has not become a reality yet in most universities. The paper then validates a pre-existing model using multiple regression models. The results validated the proposed model. A sustainability index could be developed for the education sector in future using this conceptual framework. The educational institutions can use the sustainability index to analyze their sustainability performance and take the necessary steps for achieving the same. This paper is an initial step in this direction which could be researched further to measure the sustainability performance in the education system. Grenze Scientific Society, 2020. -
Reconceptualizing Empowerment And Autonomy: Ethnographic Narratives From A Self Help Group In South India
The paper revisits academics' conceptualizations of women empowerment as stopping short of autonomy. It departs from the general observation that women empowerment movements by and large have failed to translate the new agency of women outside the domains of socio economy; that women empowerment movements' capacity to re-engage with patriarchal structures and ideologies is seriously contained. Through an ethnography of Kudumbashree, an SHG in the South Indian state of Keralam, we question the neat distinctions between empowerment and autonomy that prevail in the academic common sense. The transition of agency from the economic to the political domain is a subtle enterprise and is mediated by a number of factors including the economic independence, decision making capability and political participation. Socio -economic - political implications of women empowerment could be the first step in challenging and overcoming the relations of oppression in any society. The stereotypical assumptions can be negotiated by solely apportioning responsibilities and re-engaging with the system through everyday practices. The nuances of empowered women's re-engagement with local gender/power regimes lead to changes at the conceptual level that cuts beyond the individual and group level material transformations. The Electrochemical Society -
Model and Algorithm of Multimodal Transportation in Logistics Transportation Based on Particle Swarm Optimization
With the rapid improvement of market economy and modern logistics technique, the logistics distribution link is receiving more and more attention, and the logistics distribution path question in distribution has become the core question in logistics distribution. Study the optimization of logistics distribution path. Logistics distribution path optimization needs to find an optimal distribution route with less distribution vehicles and the shortest total length of the path, and has the rapidity of distribution. The traditional algorithm takes a long time to search the optimal route, which makes it difficult to find the optimal distribution route, resulting in high logistics distribution costs. In order to quickly find the optimal distribution route and improve the quality of logistics service, a logistics model based on particle swarm optimization algorithm is proposed. The group is composed of several non-intelligent individuals or groups of individuals. Each individual's behavior follows certain simple rules and has no intelligence; Individuals or groups of individuals can cooperate to solve questions through certain principles of message exchange, thus showing the behavioral characteristics of collective intelligence. After research, the algorithm in this paper is effective and suitable for wide application in practice. 2023 IEEE. -
A Systematic Review of Various Advancements Implementation in the Field of Crop (Plant) Production
An essential component of agricultural output is pest management, especially in fertigation-based farming. Although fertigation systems in Malaysia are beneficial for irrigation and fertilization, they frequently don't have effective pest control techniques. Because pests usually live beneath crop leaves, hand spraying is difficult and labor-intensive. Insect pests have the power to seriously harm, weaken, or even kill agricultural plants, which can lead to lower yields, worse-quality goods, and unsalable outcomes. Furthermore, insects may still cause harm to processed or stored items after harvest. Therefore, creating an autonomous pesticide sprayer specifically designed for chilli fertigation systems is the main goal of this research. The main goal is to create a sprayer arm that is flexible enough to reach under crop leaves. The goal of this project is to build an autonomous, unmanned pesticide sprayer. The goal of autonomous operation is to reduce the amount of dangerous pesticides that people are exposed to, especially in enclosed spaces like greenhouses. In addition, the sprayer arm's adaptability to different agricultural circumstances makes it a valuable tool in both greenhouse and outdoor settings. It is expected that the successful adoption of the autonomous pesticide sprayer would completely transform fertigation-based farming's approach to pest management. 2024 IEEE. -
Conceptual comprehension analysis of a student using soft cosine measure
Knowledge is the substantial wealth of a man and he possesses an innate thirst to acquire it. Knowledge embodies facts or ideas acquired through study, investigation and observation or experience. In this context, technology with its varied techniques comprising endless algorithms in natural language processing (NLP) plays an imperative role in the pursuit of knowledge. Inferences thus gathered are a clear pointer to the content teaching of students. Soft cosine measure algorithm is used in this analysis process to provide an answer regarding the grasping ability of each student with optimum learner participation and creativity. After each lecture, students have to upload their corresponding notes and this in turn would be compared with the teacher's lecture notes. The soft cosine computation gives individual results, on how much each student has comprehended a concept. This new methodology is a much awaited contribution of the educational field. 2022 Author(s). -
A parallel approach for region-growing segmentation
Image Segmentations play a heavy role in areas such as computer vision and image processing due to its broad usage and immense applications. Because of the large importance of image segmentation a number of algorithms have been proposed and different approaches have been adopted. In this theme I tried to parallelize the image segmentation using a region growing algorithm. The primary goal behind this theme is to enhance performance or speed up the image segmentation on large volume image data sets, i.e. Very high resolution images (VHR). In parliamentary law to get the full advantage of GPU computing, equally spread the workload among the available threads. Threads assigned to individual pixels iteratively merge with adjacent segments and always ensuring the standards that the heterogeneity of image objects should be belittled. An experimental analysis upon different orbital sensor images has made out in order to assess the quality of results. 2015 IEEE. -
Dynamic Load Scheduling Using Clustering for Increasing Efficiency of Warehouse Order Fulfillment Done Through Pick and Place Bots
The domain of warehouse automation has been picking up due to the vast developments in e-commerce owing to growing demand and the need to improve customer satisfaction. The one crucial component that needs to be integrated into large warehouses is automated pick and place of orders from the storage facility using automated vehicles integrated with a forklift (Pick and Place bots). Even with automation being employed, there is a lot of room for improvement with the current technology being used as the loading of the bots is inefficient and not dynamic. This paper discusses a method to dynamically allocate load between the Pick and Place BOTs in a warehouse during order fulfillment. This dynamic allocation is done using clustering,an unsupervised Machine Learning algorithm. This paper discusses using fuzzy C-means clustering to improve the efficiency of warehouse automation. The discussed algorithm improves the efficiency of order fulfillment significantly and is demonstrated in this paper using multiple simulations to see around 35% reduction in order fulfillment time and around 55% increase in efficiency. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Patient Monitoring System for Elderly Care using AI Robot
The use of robots in numerous industries has expanded in recent decades. Self-guiding robots have started to arise in human life, particularly in sectors pertaining to the lives of old people. Age-related population growth is accelerating globally. As a result, there is a rising need for personal care robots. The purpose of this requirement is to increase opportunities for mobility and support independence. To meet this demand, a robot with specific functionalities to help older people has been designed. The standard values of healthcare parameters are stored in the database by recording and comparing the current values the system will give an alarm and also sends a message to the doctor or caretaker so that a proper care would be given to the patients. We are including a preset distance value to monitor the elder people. Here we are using some sensors to detect the health parameters from the person. Robot have designed to intimate the family members if any changes occur in the health parameters. It helps the people to stay alone in home with safe manner. 2022 IEEE. -
Adopting Metaverse as a Pedagogy in Problem-Based Learning
Pedagogical practices vary from time to time based on the requirement of various academic disciplines. Course instructors are constantly searching for inclusive and innovative pedagogies to enhance learning experiences. The introduction of Metaverse can be observed as an opportunity to enable the course instructors to combine virtual reality with augmented reality to enable immersive learning. The scope of immersive learning experience with Metaverse attracted many major universities in the world to try Metaverse as a pedagogy in fields such as management studies, medical education, and architecture. Adopting Metaverse as a pedagogy for problem-based learning enables the course instructors to create an active learning space that tackles the physical barriers of traditional pedagogical practices of case-based learning facilitating collaborative learning. Metaverse, as an established virtual learning platform, is provided by Meta Inc., providing the company a monopoly over the VR-based pedagogy. Entry of other tech firms into similar or collaborative ventures would open up a wide array of virtual reality-based platforms, eliminating the monopoly and subsequent dependency on a singular platform. The findings of the study indicate that, currently, the engagements on Metaverse are limited to tier 1 educational institutions worldwide due to the initial investment requirements. The wide adoption of the Metaverse platform in future depends on the ability of the platform providers to bridge the digital gap and facilitate curricula development. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Application of XAI in Integrating Democratic and Servant Leadership to Enhance the Performance of Manufacturing Industries in Ethiopia
This study tests the conceptual model theorizing democratic leadership, servant leadership, learning organization, and performance of manufacturing industries using Structural Equation Modeling (SEM). The impact of democratic and servant leadership on learning organizations and the performance of manufacturing industries in Ethiopia is analyzed, and the role of learning organizations as a mediating variable is examined. Confirmatory Factor Analysis was performed, which includes a well-established Chi-square test, the Chi-square ratio to degrees of freedom, the goodness-of-fit index, the TuckerLewis index, the comparative fit index, the adjusted goodness-of-fit, and the root mean square error of approximation. Further, the performance of manufacturing industries has been assessed using XAI which helps in having a higher clarity on understanding the complexities in production. Based on linear regression, two methods SHAP and LIME have been used for precise predictions and forecast for future production plans in the manufacturing industry. This research contributes to the existing body of knowledge by dissecting the nuanced relationships between the two leadership styles and learning organization and further, their implications for an organizations performance. The findings of the study would provide insights for policymakers and practitioners to improve the performance of manufacturing industries. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.