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Career Counselling Chatbot on Facebook Messenger using AI
We have increasingly seen that new graduates have difficulty finding a job, which they often work in different jobs than the ones they graduated from, and that employees are not satisfied with their career choices. One reason for this may be the lack of practical, useful education when a person is in college or has just graduated from college. Chatbots are very useful and a topic of interest in the field of computer science and artificial intelligence due to their ability to emulate experts in different applications and replicate human interaction on multiple levels. Research shows that using chatbots to provide career advice can be an effective way to provide these services in an environment where counselors work. The lack of good and appropriate vocational training means that young people begin to look for jobs that their parents have chosen for them, or jobs chosen only for high wages. These decisions will be made regardless of whether; they are in the person's best interests and interests. This can cause a person to be dissatisfied with their job, which affects not only their personal health but also the overall efficiency of their work. Therefore, the development of chatbots will better inform users and help them choose jobs. This will enable them to consider jobs that they do not expect to be more fulfilling and fulfilling than jobs that do not align with their interests. Research and development software was used in this study. According to the research method, the research was conducted to collect information about people's thoughts about their career choices and what kind of job training they want from the system. In addition, the latest research on career guidance was taken into account when creating the basis of the survey. This information is used to develop a chatbot on the Facebook Messenger platform using objects powered using tools such as the Facebook SDK, Messenger Platform API and JavaScript, as well as the Wit.ai API that supports natural language usage smart technology. Chatbots can understand user input and provide relevant and appropriate responses reliably and instantly. Overall, it is hoped that the results obtained will create a positive impression of the implementation of the system and thus become a valuable asset for any school or institution wishing to use the system.
Scalability Challenges in Blockchain Networks: A Comparative Analysis
Blockchain technology, originating with Nakamoto's seminal whitepaper in 2008, has evolved into a transformative force, promising decentralized, transparent, and secure digital transactions. However, the burgeoning impact of blockchain is accompanied by inherent scalability challenges that impede its widespread adoption and efficiency. This study conducts a comparative analysis of scalability challenges within prominent blockchain networks Bitcoin, Ethereum, and Binance Smart Chain. Rooted in a holistic understanding of blockchain evolution, the analysis focuses on transaction throughput, latency, consensus mechanisms, network congestion, and resource utilization. Consensus mechanisms, serving as the bedrock of blockchain networks, play a pivotal role in scalability dynamics. The study dissects the implications of diverse mechanisms Proof of Work (PoW), Proof-of-Stake (PoS), and Delegated Proof-of-Stake (DPoS) to unravel their impact on transaction efficiency. Network congestion emerges as a critical hurdle, affecting transaction confirmation times and overall system efficiency. Balancing this congestion requires a nuanced understanding of network dynamics, block sizes, and transaction prioritization. Resource utilization, particularly in terms of computing power and memory, becomes a focal point for sustainable growth. The study scrutinizes the efficiency of resource allocation and its implications for scalability, acknowledging the delicate balance between network performance and ecological considerations. By juxtaposing these blockchain networks, the comparative analysis aims to discern patterns, evaluate the effectiveness of consensus mechanisms, and provide actionable insights to address scalability challenges. Ethical considerations underpin the study, ensuring responsible data usage, user privacy, and adherence to ethical standards in blockchain research. This exploration into scalability challenges within blockchain networks contributes to the broader discourse on decentralized technologies. The findings are poised to inform future developments, providing a roadmap for enhancing scalability while maintaining the core tenets of decentralization, security, and sustainability.
Quantum Hardware Development: A New Era of Computing in the Quantum Fields
Quantum computing represents a revolution in the ability to process information, and quantum hardware technology underpins its implementation. This work examines the current landscape of quantum hardware development, focusing specifically on three main technologies: superconducting qubits, trapped ions, and topological qubits. This study provides an in-depth analysis of the current state of each technology, taking into account its strengths, challenges and opportunities. Research is aimed at identifying current limitations and inefficiencies in this quantum hardware. Additionally, this study aims to present new developments and new methods to solve the identified problems. By combining theoretical knowledge and empirical tests, this research aims to handle to the ongoing debate on the development of quantum hardware that pushes the limits of quantum computing capabilities. The results of this work have important implications for the broader quantum computing community and provide insight into the complexity of superconducting qubits, trapped ions and topological qubits. The improvements and new techniques presented lead to the continued development of quantum hardware and help move the field closer to the realization of practical, scalable quantum computers.
Performance Analysis of Share Market by using ML Technique
The main indicator that measures the economic development and power of the country Inflation and expansion affect the economy. Prices are difficult to predict due to many factors. Predicting changes in stock prices is one of the most difficult aspects of financial forecasting. Predicting stock prices accurately can make investors profitable. to the complexity of the data in the Forecasting stock market trends presents a formidable challenge due to its inherent unpredictability. The primary objective of this article is to offer insights into forecasting market prices, aiding investors in making well-informed decisions. The methodologies to be employed include statistical analysis, machine learning techniques, natural language processing, and sentiment analysis achieve research objectives. In this study, a mixed methods model was developed using qualitative and quantitative data.
Forest Fire Detection System for Preventing Flora & Fauna from Fire Using Computer Vision
Forest fires present a significant threat to ecosystems, endangering both human and animal life while causing irreversible environmental damage. To address this pressing issue in this research paper introduces an innovative & Affordable forest fire detection system that utilises the HSV algorithm and various computer vision libraries with the use of AI. Our advance system aims to detect & respond to forest fires in real-time, mitigating potential harm to flora and fauna. Our proposed System employs deploy camera sensors distributed at the Sensitive Parts of the forest, continually monitoring the environment. The HSV algorithm enhances the system's capability to accurately identify fire-related hues, ensuring reliable fire detection. Upon detecting a fire, an automatic alarm is triggered, initiating a series of measures to protect the ecosystem proactively. In response to a detected fire, the system automatically reroutes animal pathways to minimize their exposure to danger. Concurrently, the system sends immediate notifications to the local fire department and forest authorities via direct email, providing vital information on the fire's location and severity. This rapid communication facilitates timely intervention and improves the chances of successful firefighting efforts. We can put our system at Jim Corbett National Park & at different Sensitive forest Regions. Furthermore, the system features live monitoring capabilities, enabling authorities to observe the fire's real-time progression. This live feed supports informed decision making and facilitates the swift deployment of resources to contain and extinguish the fire. Through the integration of advanced computer vision technology, our system aims to redefine forest fire detection & response mechanisms, ultimately safeguarding the delicate balance of flora and fauna in ecosystems.
Beyond the Firewall: Understanding and Mitigating Cloud Security Challenges
In the ever-evolving landscape of digital infrastructure, the adoption of cloud computing has ushered in unparalleled opportunities for innovation but has also introduced complex challenges in securing sensitive data. This research, titled "Beyond the Firewall: Understanding and Mitigating Cloud Security Challenges," embarks on a comprehensive exploration of the multifaceted dimensions surrounding cloud security. The study delves into the intricacies of cloud security, transcending traditional boundaries set by firewalls. From elucidating the dynamics of shared responsibility models to dissecting the nuances of identity and access management, the research unravels the complexities inherent in safeguarding data within the expansive digital cosmos. It addresses elusive threats like data breaches and underscores the critical importance of adaptive strategies to navigate the ever-changing threat landscape. This research goes beyond theoretical discourse, offering actionable insights for organizations seeking to fortify their cyber defenses. It emphasizes the need for a paradigm shift in understanding security challenges in the cloud era and advocates for a proactive approach to mitigate potential risks. By illuminating the path "Beyond the Firewall," this study aims to empower stakeholders with practical knowledge, enabling them to establish robust security frameworks that resonate with the dynamic nature of modern digital ecosystems. Join us on this expedition into the heart of cloud security challenges, as we decipher the complexities and chart a course towards a more resilient and secure digital future.
Student Feedback Mining System Using Sentiment Analysis
In education, the importance of gathering and understanding student feedback cannot be over stated. Traditional methods of gathering feedback through surveys and assessments often suffer from limitations such as low response rates and subjectivity. This paper proposes a new approach to improve the feedback collection process by implementing a Student Feedback Mining System using sentiment analysis. The goal of this system is to automatically analyze and extract sentiment from student feedback and provide educators and institutions with valuable insights into students' overall satisfaction and feelings toward various aspects of their academic experience. The system uses natural language processing (NLP) techniques to process unstructured text data, identify sentiments, and categorize feedback into positive, negative, or neutral feelings. Collecting student feedback through various channels such as online surveys, discussion forums and other feedback mechanisms. Cleaning and preprocessing the collected text data to remove noise, irrelevant information and standardize the format for analysis. Using state of the art NLP algorithms and sentiment analysis techniques to determine the emotional tone of feedback. This includes classifying sentiment as positive, negative, or neutral. Grouping feedback based on specific criteria such as course content, teaching methods, facilities and overall learning experience to provide a comprehensive understanding of the various aspects affecting student satisfaction. Presenting analyzed data through intuitive visualizations and reports, making it easy for educators and administrators to interpret and respond to feedback. Creating feedback that enables continuous improvement by providing actionable insights for educators to improve their teaching methods, curriculum and overall learning environment. The proposed system aims to overcome the limitations of traditional feedback collection methods by automating the analysis process, providing real-time insights and supporting a data-driven approach to improving education. Using sentiment analysis, educators and institutions can proactively solve problems, improve the quality of education, and create a more positive and engaging learning environment.
Object Recognition Using Pattern Analysis
The proposed research paper describes an object System of recognition based on human perception, which is achieved by feature extraction (components) and pattern analysis. Both strategies are applied to each subpart. Feature extraction is done using shaping and pattern analysis is achieved by creating statistical bins for each pattern of vehicle subparts. Each bin contains general and specific measures, and each measure has a certain weight that contributes to the analysis of each bin to decide on similarity or dissimilarity. Object recognition is an important task in computer vision, with applications in robotics, driverless cars, medical image analysis, and many other applications. This article explores the concept of product analysis using benchmarks, highlighting key concepts, methods and recent developments.
Facial Recognition Based Automated Door
To ensure the accuracy and efficiency of intruder identification, the proposed method is combined with Haar clssifier technology for face detection. When someone comes to the door, the Pi camera captures the image and starts the face detection process. In this research, we implement a facial recognition component to capture human images, comparing them with stored data in a database. Upon a match with an authorized individual, the system unlocks the door through an electromagnetic lock. The demand for a rapid and accurate face recognition system persists, continuously evolving to swiftly identify intruders and restrict unauthorized access to highly secure areas, thereby reducing human error. Facial recognition stands as a crucial component within secure systems, surpassing biometric pattern recognition methods, and finds widespread application across various domains.
On the Robustness to Doppler Rate of the Modified LoRa using two Spreading Factors
In [1], we proposed a modified LoRa using two spreading factors (SF’s) in order to independently set the frequency difference of adjacent LoRa symbols and the LoRa symbol period, and demonstrated that the use of two spreading factors could make it less sensitive to sampling frequency error while enabling low power transmission. In this paper, as an extension of [1], we will show that our scheme has also robustness to the Doppler rate through computer simulations.
Analysis of Epoxy Fiber Composite Clamp with Variable Slotted Holes for Electric Overhead Crane Mounting
Composite materials offer an excellent solution for structural applications requiring a high strength-to-weight ratio and stiffness-to-weight ratio. This is particularly beneficial in weight-sensitive structures like aircraft and spacecraft, where the cost-effectiveness of composite materials becomes evident. The exploration of composite materials encompasses various aspects, such as manufacturing processes, anisotropy, the elasticity strength of anisotropic materials, and micromechanics. This study focuses on the investigation of a novel glass epoxy composite material as a substitute for expensive, heavy, and corroding metals in applications involving tensile loadings. The objective is to experimentally analyze and employ finite element methods to understand the mechanical behavior of composite components, specifically those featuring slotted holes. Furthermore, the study aims to validate these findings using the finite element analysis (FEA) method.
Tribological Performance of Automobile Brake Pads by Using Metal Matrix Composites
In today's ambitious world, there's a growing demand for high-performance components that are strong yet lightweight, durable, and energy-efficient, especially in industries such as automotive, aerospace, military, and sports. Aluminum, known for its low weight and excellent dimensional stability, is a popular choice. It is often reinforced with materials like SiC (silicon carbide) and Ni-Gr (nickel-graphite) to enhance wear and temperature resistance, making it suitable for various automotive applications, including cylinder fins, heat sinks, and piston materials. Additionally, common filler materials like silicon nitride (Si3N4), boron carbide (B4C), boron nitride (BN), aluminum oxide (Al2O3), aluminum nitride (AlN), titanium carbide (TiC), and titanium oxide (TiO2) tend to exhibit better compatibility with aluminum when it is in alloy form. Composites, especially metal matrix composites made of aluminum, offer superior mechanical properties compared to traditional metals. These composites are characterized by high specific strength, low weight, hardness, and wear resistance. Unlike unreinforced alloys, metal matrix composites possess exceptional tribological properties, allowing them to effectively compete with conventional materials in various tribological applications. These aluminum particle-filled metal matrix composites are actively pursued across sectors, including aerospace and automotive, due to their improved physical and tribo-mechanical properties.
Teaching Robot Skills Through Demonstrations
Robotic arms can be designed to lift defined payloads precisely and these robotic arms are used in operations like pick and place, welding, painting, precise motion, etc. These robots are programmed using high-level programming, teach pendent, or using graphical programming. If there is any change in the task, then it has to be reprogrammed for the new task, and it’s a tedious job. To address this problem this article focuses on the development of a learning model to teach the robot skills through demonstrations to a robotic arm (xArm) by interfacing with the Oak - D camera. The reach of the xArm robotic arm is 700mm and fitted with a two-jaw gripper. The Oak - D camera has some significant features such as depth sensing, AI capability, and real-time processing. The experimental setup consists of xArm, an Oak-D camera, and a camera mount. The installation of a robotic arm is through xArm python SDK, and the setting up of a camera is through DepthAI python SDK. Initially, color template files for block detection are created using color segmentation technique, later camera matches the color template with the real-time image to get the coordinates with respect to the camera view. In the learn mode the blocks are detected and the coordinates are calculated with respect to the camera and then coordinate transformation is carried out to get the coordinates of the block with respect to xArm. This coordinate is matched with feedback from the xArm and the color of the block is logged to learn file. Through this, a learning file is created. In the demonstration phase first, the coordinates of the block are calculated with respect to the camera view then coordinate transformation is carried out to get the coordinates of the blocks with respect to the xArm, now the controller commands the xArm to sort the blocks using the learn file. It can be concluded that the robot arm sorted the blocks in the same order as taught in the learn mode based on color templates and also the robot arm was able to adapt to the changes made in the environment.
Study of Machine Learning Techniques in Autism Spectrum Disorder Detection
ASD is a neurodevelopmental disorder that affects how people communicate, interact, and behave. It is not a single condition, but a spectrum of different symptoms and challenges that can vary from person to person. To diagnose ASD, clinicians and experts use various tests and observations, but these methods can have some drawbacks. They can be subjective, expensive, and time-consuming, and they may not work well for different cultures and regions. There is also no clear biological or genetic marker for ASD. ML is a type of artificial intelligence that can learn from data and make predictions or decisions without being programmed. ML can help with ASD diagnosis in different ways, such as screening, feature extraction, classification, and outcome prediction. Screening is the process of finding out who may have ASD and need more tests. Feature extraction is the process of getting useful information from the data, such as brain scans, speech patterns, or eye movements. Classification is the process of putting people into different groups or categories, such as ASD or non-ASD. Outcome prediction is the process of estimating the future situation or performance of people, such as their thinking skills or well-being. In this paper, we review the latest ML methods for ASD diagnosis and the challenges and limitations of these methods. We also talk about the ethical and social issues of using ML for ASD diagnosis and suggest some ideas for future research.
Thermal Performance Analysis of Liquid Cooling System in Lithium-ion Battery
Electric vehicles (EV) have been rapidly developing and are becoming increasingly popular due to its zero emission, low operating cost and noise free. However, there are some factors that limit the development of electric vehicles, such as the thermal performance, cost, lifetime and safety of the battery. To address this difficulty integration of liquid cooling system is the promising solution. The battery cooling technologies like air cooling system, liquid cooling system, direct refrigerant cooling system, phase change material (PCM) cooling system are used in e-vehicle. In this research, the main goal is to better understand how to improve the consistent temperature distribution in battery packs. The study provides general strategies to reduce thermal unevenness or achieve almost no differences in temperature within lithium-ion battery modules. These modules are cooled using channelled liquid flow, where a liquid circulates through channels designed for thermal management. To demonstrate the effectiveness of the proposed strategies, the research numerically analyzes two cases: one with multiple short channels and another with orderly magnified contact areas. Two of the methods demonstrate significant improvement in the overall thermal uniformity of the battery pack.(a) Shortening flow paths with multiple serpentine channels, and (b) enhancing contact areas between batteries and serpentine channels along the flow path in the stream wise direction, both show considerable promise in improving pack thermal uniformity.The study investigates the thermal effects of varying liquid flow rates in a computational fluid dynamics model for an 71 18,650 battery pack discharged at 5C. A three-dimensional model is built in ANSYS and the mesh is generated using Fluent Meshing. The maximum temperature differences observed across the battery module during a 5C discharge are 2.2 K for one strategy and 0.7 K for the other. This information highlights the effectiveness of the second strategy in achieving lower temperature variations, indicating better thermal uniformity during the discharge process compared to the first strategy.
Accountability of Agri-Farming in India Prosperity of Financial System
The Agriculture in India is basically a part of life; it is a system of work and culture. None of the farmer of the country likes to keep out land fallow, whether it is profitable or not. Farmers spend maximum time in the field. Earlier the country was self-dependent and was termed as ‘Golden Bird’. However, the situation changed with the arrival of Mughals and Britishers and their colonial policies. After independence our main problem was how to feed to people. Then our planners and policy makers, agricultural scientists and economists decided to produce more foodgrains in order to achieve self-sufficiency. The period of late mid sixties is known as the green revolution period. Dr. Swaminathan and Dr. Borlag along with other agricultural scientists took a challenge to enhance the production and they succeeded. After overcoming the food problem by producing sufficient paddy and wheat, the next aim was to become self sufficient in pulses, oilseed and milk, with quality products. Currently the country is in a position to export the agricultural produce and products. Now the country is economically sound as the per capita income of the people has increased but still it is not fully satisfactory. The farmers are yet to undertake Farming in a professional way. Many industries have come up with new agriculture products. Now the question is how to make the best use of alternative available.
Impact & Causes of Urinary Stone: A Review
The process of stone formation in the kidneys, bladder and/or urethra (urinary tract) is called urolithiasis. Men are twice as likely to produce stones as women. Stone obstruction of the ureters or renal pelvis is characterized by excruciating, intermittent pain radiating from the flank to the groin or genital area and the inner thigh. Rock types are named according to their mineral composition. The most common minerals are struvite (magnesium ammonium phosphate), calcium oxalate, urate, cystine and silica. The most abundant rock on Earth contains calcium. Protection measures depend on the type of stone.
Herbal Drugs in the Treatment of Psoriasis
Psoriasis is an inflammatory skin condition characterized by scaling with inflammation, resulting in regions of thick, red skin covered in silvery scales. It can be itchy or painful. Systemic treatment, topical therapy, and phototherapy are used to treat psoriasis, but they have negative and potentially fatal side effects. Patients with psoriasis are more likely to acquire other conditions such as psoriatic arthritis, anxiety and depression, cancer, metabolic syndrome, cardiovascular disease, and Crohn's disease. Psoriasis is estimated to be around 2-3% of the general population. In Europe, the prevalence ranges from 0.1% to 3.2%, depending on the country. In Asia, the prevalence is generally lower, with estimates ranging from 0.1% to 1.5%. The study aims to highlight these plants, herbal formulations, and associated therapies to develop safer and more effective treatments for psoriasis. This review summarizes the current knowledge on herbal products used topically for psoriasis treatment, including their mechanisms of action such as inhibition of keratinocyte hyperproliferation, immune-inflammatory reaction. It also discusses the penetration of herbal products through the psoriatic skin barrier, novel drug delivery systems, and possible adverse effects of herbal therapy. Here, we provide a comprehensive review of the background of EP, assess the available clinical data on the efficacy of targeted therapies, and aim to provide a foundation for clinical decision making for this rare form of psoriasis.
Cardioprotective Effect of Traditional Medicine :A Review
Plants have played an important role in meeting the nutritional and other various needs of humans. As humans advanced in medicine, plants became essential to people's health and well-being. The development of technology has pushed people to make greater progress in a short time, causing many heart diseases due to rapid progress, and the use of many drugs has improved and cured the heart. In order to save and protect people's heart health from the effects of drugs, people have returned to nature and used many bioactive substances found in plants to protect the heart, such as antioxidants, flavonoids, anthocyanins, tannins, ellagic acid, terpenes, carbohydrates. . It has been shown to improve heart protection and thus reduce the risk of heart disease. The purpose of this review article is to provide people with information and education about naturally occurring drugs and include them in their daily diets to improve heart health and eliminate problems caused by synthetic chemicals.
Floating Drug Delivery System- A Review
Floating drug delivery system (FDDS) helps to improve the buoyancy property of the drug over the gastric fluids and hence maintain the longer duration of action. It is helpful in minimizing the dosing frequency. The density of dosage form must be less than the density of gastric contents (1.004 gm/ml) in FDDS. It may effervescent or non-effervescent system. The drugs having narrow absorption window in GIT is good candidate for the floating drug delivery system. The main objective of writing this review article is to compile the recent literature with special focus on classification, approaches to design single-unit and multiple-unit floating systems, and their classification and formulation aspects are covered in detail.