Constructing a system that is impervious to environmental circumstances, such as an averse chemical environment and severe events like earthquakes and cyclones is one of the primary objectives of structural design. Conventional construction materials like steel and concrete may function inadequately under these conditions. Shape Memory Alloys (SMAs) are new metals with unique properties that have the potential to fill construction gaps and improve a structure's ability to withstand impacts. The focus of this paper is to do modal analysis of Shape Memory Alloy. The element under investigation is a beam with fixed-free and fixed-fixed boundary conditions. We have designed different cross section beams models in ANSYS design modeler. Finite Element Analysis conducted, in order to find out beam deflections, frequency of Vibrations and also different mode shapes. Theoretical and FEA findings is compared and presented.

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In this paper, we evaluated the performance of four distinct models namely, Logistic Regression, Naive Bayes, Random Forest, and Long Short-Term Memory (LSTM), all trained on the same dataset. Notably, the LSTM model outshone its counterparts, exhibiting remarkable proficiency in figuring out the subtle feelings and moods in the language. Its ability to grasp complex contextual relationships surpassed traditional Machine Learning models like Logistic Regression, Naive Bayes, and Random Forest. This highlights the evolving landscape of sentiment analysis, where the depth and sequential understanding offered by Deep Learning models, especially LSTM, prove invaluable in capturing the intricacies of human expression. These findings prompt a revaluation of the balance between traditional and advanced techniques, emphasizing the importance of leveraging sophisticated models for tasks requiring a nuanced comprehension of sentiment.

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Seismic retrofitting is mainly done to meet the seismic safety requirements. The planning of modifications to existing buildings contrasts from new planning through a significant condition; the existing development must be taken as the premise of all planning and building activities. India is one of the most earthquake inclined nations on the planet and has encountered a few significant or moderate earthquakes during the most recent 15 years. Around 50-60 % of the absolute zone of the nation is defenseless against seismic movement of shifting forces. In this paper we have reviewed articles related to retrofitting of an old structure, analysis tools and non-destructive testing.

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The main reason which has led, business traffic as a 'worldwide' miracle over the last many decades is the increased population growth and the number of motor vehicles. With the help of communication, the system of operation of road business has been significantly bettered. The ant colonies and the principles of mass intelligence have enlightened the system of road business operation which has been proposed in this paper. Camera is fixed in every part of the bend. The detector which is of ultrasonic in nature is used to find if there's any handicap that's transmitted through the RF transmitter and the receiver section fixed in every auto receives the information and that's blazoned in speaker and if any abnormalities the buzzer will warn the motorist and these are displayed in the TV display. Ultrasonic detectors are used to find the distance of the neighbor vehicles and transfer the acclimated data toreceiver.IR is used to find the number of incoming vehicles. In this paper we've proposed the time- grounded system for waking the motorist against other vehicle at hair leg bends to avoid accidents causing loss of mortal life. This system is designed to give safe trip to the original residers and excursionists by waking them against eyeless spot angles and also reduced the possibility of accident at eyeless angles. This paper is concentrated on the designing of automated system with the arrival of new technology and up gradation. The proposed automated system would remarkably reduce the mortal sweats by exercising the PLCs. Automated system is taking place of mortal due to increased effectiveness and quality affair. Therefore, the proposed system adopts the rearmost automated technology including PLC to develop motorist alert system which has operation on the hair leg bends of hilly roads.

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The objective of this study is to investigate the application of a Unified Power Quality Conditioner (UPQC) in mitigating energy efficiency issues and harmonics resulting from non-linear loads in power networks. In this analysis, the UPQC is augmented by fuzzy-controlled Photovoltaic (PV) and Battery Energy Storage Systems (BESS). Typically, the photovoltaic (PV) device is responsible for producing active power that is utilised in various applications. In the event of insufficient electricity generation by the photovoltaic system, the Battery Energy Storage System (BESS) is activated to supply energy, particularly during prolonged voltage disruptions. The hybrid PVBESS system is considered to be more reliable than the single PV-UPQC system due to the latter's inherent instability and substantial dependence on environmental factors. Consequently, the Battery Energy Storage System (BESS) will exhibit enhanced voltage support capabilities as it progresses, diminish the power consumption associated with the DC-link voltage control algorithm, and sustain the provision of renewable energy. The Fuzzy STF-UVG employs a series of sequence synchronisation stages and use an Active Power Filter (APF) rectifier instead of a Phase-Locked Loop (PLL) to deactivate the UPQC controller. In conclusion, the suggested method of utilising a fuzzy approach in the proposed STFUVG is evaluated against the conventional SRF-PLL reference frame of the UPQC-based classical system to showcase its effectiveness. The MATLAB-Simulink software is employed to verify the outcomes through the utilisation of many case studies.

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This research paper investigates the intersection of behavioural analytics, risk assessment, and edge cloud intelligence within the context of the automobile industry. The primary objective of this study is to explore how the integration of behavioural analytics and real-time risk assessment, facilitated by edge cloud intelligence, can enhance safety, efficiency, and decision-making in vehicular environments.

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Pneumonia affects millions globally and requires prompt and precise diagnosis. Chest X-rays are commonly used but manual analysis is time-consuming and prone to error. We developed an automated method using CNNs for pneumonia identification through chest X-ray interpretation. We utilized a large dataset comprising thousands of chest X-ray images from both pneumonia-positive and pneumonia-negative patients. By creating, training, and analyzing CNN models, we were able to detect subtle patterns and features indicative of pneumonia. To optimize the performance of our models, we tested multiple CNN architectures such as the baseline CNN, CNN using Keras, transfer learning, and transfer learning using FT, on the same dataset[1], using evaluation criteria such as testing accuracy, training accuracy, and Validation accuracy. Our results demonstrated that the CNN-based technique outperforms standard computer-aided diagnostic (CAD) systems and human radiologists in terms of detection accuracy. Our method achieved an accuracy of 73%, 84%, and 92.1% The model Transfer learning using ft showed the highest accuracy of 92.9 %. Our deep-learning CNN models enable accurate detection of pneumonia, even from low-quality, low-dose X-ray images, improving patient care.

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The agricultural industry is on the brink of a transformative era with the introduction of TerraGuardian, an autonomous agricultural robot engineered to redefine modern farming practices. This pioneering research paper delves into the development, capabilities, and potential impact of TerraGuardian on agriculture. The primary objectives of this research include enhancing crop management, streamlining farming operations, and promoting environmental sustainability. TerraGuardian's multifaceted design encompasses autonomous and remote-control capabilities, a state-of-the-art weather station, a dual-camera system for crop surveillance and disease detection, and a robotic hand for sample collection. Its advanced Battery Management System, coupled with solar panels, ensures efficient energy utilization, minimizing downtime. Redundancy in componentry guarantees uninterrupted functionality even in the face of technical issues. TerraGuardian's rugged off-roading capability and safety features further augment its utility. The benefits of TerraGuardian are far-reaching, promising increased farming efficiency, data-driven decision-making, swift crop disease detection, and a commitment to sustainability. Key success criteria include improved crop yield, efficiency gains, and positive user feedback. In the ever-evolving landscape of agriculture, TerraGuardian emerges as a symbol of progress, offering farmers a technological edge that promises to revolutionize the industry. This research paper is a comprehensive exploration of TerraGuardian's potential to reshape farming practices, fostering a more sustainable and productive future.

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In a comprehensive study on malware detection, we evaluated five machine learning models: multinomial naive bayes, random forest classifiers, K-nearest neighbours, decision tree classifiers, and support vector classifiers, using a diverse dataset with file feature extraction. Results showcased varying performances, with the Random Forest Classifier standing out with a flawless accuracy score of 1.00, excelling in distinguishing between malware and legitimate files. The Decision Tree Classifier and K-Nearest Neighbours Classifier followed closely, both achieving a high accuracy of 0.99. This research underscores the effectiveness of machine learning models, especially Random Forest, in enhancing cybersecurity by accurately identifying potential threats amidst digital files.

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The always-advancing technology has made our day-to-day lives easier. Since every coin has two sides, technology has its benefits as well as disadvantages. The rise of technology has increased, and this technology has increased the number of road accidents, which have resulted in some major loss of life. Our country has emergency facilities and still places emphasis on them, but they are of no use if they don't reach the place in time. Of emergency, and that's why our research paper is provided for this project. When there is a vehicle accident or any alcohol addiction, the sensor in the car will detect the location of the vehicle and immediately send that message to the microcontroller. Then the microcontroller sends an alert message with the help of a GPS model to the police control room or rescue team with the help of a GPS model to locate the location, as well as the location of the accident with the help of the hospital and relatives of the victim. There have been many research papers on this technique before, but they have not been perfect, so I have chosen this one.

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This research delves into the process of translating song lyrics into Marathi, a regional language spoken in Maharashtra, India. Music is a universal language, and its translation into other languages provides a unique opportunity to examine linguistic and cultural adaptation. The significance of this research lies in its artistic and academic implications. From an artistic standpoint, it fosters a deeper connection between music and Marathi-speaking audiences, while on an academic level, it contributes to translation theory and intercultural communication. The objectives of this research encompass linguistic analysis, cultural adaptation, impact assessment on Marathi music, and the examination of translation techniques. We explore the linguistic challenges faced by translators, the adaptation of cultural references, and the impact of translated songs on the Marathi music industry. Additionally, we analyze the techniques employed to capture the intended meaning and emotions of the original songs. The potential impact of this research spans across various domains. It enriches Marathi music by introducing new songs and genres, promotes cross-cultural understanding, and showcases the adaptability of the Marathi language. Furthermore, it contributes to language preservation efforts by demonstrating the versatility of Marathi. In conclusion, this research endeavors to unravel the intricate process of translating song lyrics into Marathi, shedding light on the complexities of linguistic and cultural adaptation while enriching the cultural and artistic landscape of Marathi-speaking communities.

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The purpose of the study is to explore the issues of racism and exploitation of black women. Beloved is a historical novel discusses about the racism and harassment on women. It also discusses about the haunted house and haunted slavery in which the protagonist decides in the extreme level and chose infanticide. Beloved represents the true story of a woman who struggled a lot to save their children from being slaves. It exposes the real nature of dehumanization on women and men who work as slaves for financial support. The symbolization of scars on the body of a protagonist with past is a remarkable awakening to the readers. It talks about the regaining the consciousness of past in the present time. Toni Morrison, an author of this novel also faced racism in her childhood and this became one of the themes of her writings. She won Pulitzer Prize for this novel. Morrison uses different elements to sensitize the issues of women especially black women who being the domestic workers and at the same time protecting their family against the evils of slavery.

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In the past few decades urbanization has seen an exponential growth. This gave rise to idea of 'Smart Cities'. The proposed model includes deployment of a working Parking System to determine the available parking slot. This model is a small scale prototype of actual model which runs on an IOT-based framework, which helps users to find empty slots efficiently and makes their search time less. It particularly works on 'FIRST COME FIRST SERVE' method. The system runs with help of IR sensors & a LCD screen connected to Arduino which displays the information.

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In this survey paper discuss the various coding technique in MEMO-OFDM and discuss the multiple filters. A multicarrier modulation in which the transmitted bit- stream is partitioned into various sub-streams is known as an OFDM system. MIMO system can make use of multipath components to a considerable extent. Also, it can be anti-multipath fading system. Recently, there are two schemes to solve the problem of frequency selective fading namely Equalization, and OFDM. Although OFDM is critical for the B30&40 broadband wireless communications, its ability to improve the spectrum utilization is limited. Contrarily, MIMO-OFDM provides higher data transfer rate by the developing space resources in OFDM.

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This research delves into the omnipresent influence of artificial intelligence (AI) in the lives of college students. AI, the culmination of human intelligence and technological innovation, encompasses a wide array of capabilities rather than a singular technical approach. Divided into two distinct branches, automation-oriented and application-oriented learning, AI serves as an undeniable force poised to transform the labor-intensive landscape. Its power lies in the symbiosis between vast data sets and predictive algorithms, impacting productivity, remote work, and fostering competition. Furthermore, AI holds profound implications for education, significantly shaping the student community it serves. By offering personalized learning experiences, efficient grading, and insightful educational recommendations, AI bolsters student engagement and academic success. It offers flexibility, accessibility, and adaptability, making learning more engaging and personalized than ever before. In this comprehensive research, we employed a carefully designed Google Form survey to gather responses from 134 students spanning various educational levels, from undergraduates to postgraduates. By scrutinizing the diverse responses, we aimed to uncover how AI technologies intertwine with the daily routines of students. This exploration promises a deeper understanding of AI's role in education, adding nuance to the narrative our methodology, based on a broad sample and extensive questionnaire, paves the way for a comprehensive comprehension of AI's place in the lives of students. The study addresses both the opportunities and potential concerns posed by the increasing integration of AI into education, offering insights that hold far-reaching implications for education policymakers and technology developers. This interdisciplinary approach promises valuable contributions to our understanding of the transformative influence of AI on students' lives and their learning experiences. This research is not only timely but also offers a rewarding journey for those interested in the intersection of technology, education, and AI. It underscores the importance of leveraging AI's potential while remaining vigilant about ethical considerations and data privacy, ultimately shaping the future of education.

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This study examines how social anxiety is perceived and experienced by students in a classroom context. It reveals a complex picture of social anxiety's presence and effects on numerous areas of students' life by analyzing data gathered from 63 respondents. The results show that social anxiety is a common occurrence among students, with more than 50% reporting sporadic worry and a notable number reporting frequent or ongoing social anxiety. It's significant that this worry has a variety of implications on academic participation, from minor obstacles to more obvious difficulties in class activities and assignments. There are many different ways to cope, including avoidance, encouraging self-talk, getting help, deep breathing, and meditation. There is a lack of awareness of university support services for social anxiety, indicating the need for more outreach to make sure students are aware of the options. Students frequently talk about their social anxiety with their family and friends, underscoring the value of these social support systems. Students exhibit optimism about long-term management and recovery from social anxiety, which is crucial. However, the fact that public speaking is a substantial anxiety trigger highlights the significance of focused therapies in this situation. In conclusion, this study emphasizes the importance of treating social anxiety in the student population, pushing for more people to be aware of the support resources that are available, and encouraging knowledge of practical coping strategies. Academic institutions can improve students' general wellbeing and academic success by doing this.

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Personality traits analysis in a wide variety. It generate an important life outcomes. personality traits can do change in response to physically mature and life experiences. However, psychologists have only recently taken interest in whether personality traits might also be able to be changed and whether in difficult situation how trait changes can translate into improvements in relevant life outcomes. This chapter overviews modern theories and data on how and why personality is thought to naturally change across time and whether interventions might be able to successfully change people's traits. Future research directions are discussed.

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Depression is a widespread illness that has potentially devastating consequences. With the rapid growth of social media and internet users, timely identification of emotional responses has become critical. Mental diseases are extremely dangerous, affecting about 300 million people. Therefore, this is why research is being conducted on the subject. With advances in machine learning and the availability of sample data related to depression, there is the prospect of building an early depression diagnostic system, which is critical to reducing the number of people suffering from depression. In this paper, the application of sentiment analysis and deep learning methodologies to depression and stress detection and monitoring are discussed. In addition, a fundamental plan of an incorporated multimodal framework for stress and depression checking, that incorporates estimation investigation and deep learning processing strategies, is proposed. In particular, the paper traces the fundamental issues and moves comparative with the structure of such a framework.

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