ISSN: 2455-1872 Scientific Journal Impact Factor (SJIF): 5.999 Index Copernicus (ICV: 57.57) Journal DOI no.: 10.37591/JoARB
STM JOURNALS ( Consortium eLearning Network Pvt Ltd)
Accounting and Finance with Robotic Process Automation (RPA)
This study explores the potential of robotic process automation (RPA) to transform finance and accounting (F&A) operations. RPA involves utilizing software robots to mimic human interactions with applications, specifically targeting rule-based and repetitive tasks across various financial processes. Key findings: Boosted productivity and efficiency: RPA may result in considerable efficiency improvements by streamlining repetitive operations and freeing up F&A employees to concentrate on more strategic, higher-value work. Enhanced accuracy and compliance: RPA improves reporting accuracy and compliance by ensuring consistency and conformity to complicated financial laws by removing human error in data entry and computations. Reduced costs: Streamlining processes through the automation of time-consuming tasks may result in reduced resource consumption and operating expenses. Improved employee morale: Staff members are more engaged and satisfied at work when they are freed from boring duties, which enables them to work on more rewarding projects. Examples of RPA applications in F&A: Accounts payable/receivable: Creating client statements, initiating payments, processing invoices, and entering data automatically. Bank reconciliation: Simplifying account reconciliation and cutting down on mistake rates and human labour. Financial reporting: Executing duties related to regulatory compliance, report creation, and data aggregation automatically. Payroll processing: Automating timesheet processing, tax preparation, and payroll computations. Tax preparation: Automating the filing of tax returns, data collecting, and tax computations. Challenges and considerations: Initial investment: Software development and licensing expenses are required for RPA implementation. Successful implementation of change management requires meticulous planning, support for staff, and comprehensive training. Process standardization: Automation can only be successful if processes are well-defined and standardised. Security and integration: Important things to consider are security protocols and smooth system integration.
Copyright (c) 2023 JOURNAL OF ADVANCEMENTS IN ROBOTICS
Comparative Analysis of Supervised Learning Algorithms for the Traffic Accident Prediction Under Rural and Urban Areas
The features of different mechanisms for safety to traffic accidents are the biggest challenge for the automobile manufacturing industry in rural and urban areas. The objective of this work is to address the challenge of safety in traffic accidents by developing an accurate prediction model for identifying patterns in the different scenarios for preventing traffic accidents using accurate prediction. The machine learning algorithm is used to easily predict the traffic accident scenario and automatically identify data and patterns. By using an ML-based model, a cost-effective approach for safety measures was built. The aim of this prediction model is to improve the accurate prediction for preventing traffic accidents in security measures. In this prediction model, three ML-based algorithms namely random forest, decision tree and SVM were used to predict the data of traffic accidents with low-budget scientific measures for reduction of maximum possible accidents. The focus of this study is to achieve accurate data on traffic congestion. The random forest algorithm is a category of supervised learning algorithms in which part of the machine learning algorithm is best for the prediction of traffic accidents due to a higher accuracy rate when compared to other proposed algorithms such as SVM and decision tree, as concluded in this research work. In this work, large amounts of data related to traffic accidents on behalf of the time accident location, road features and weather conditions were collected. The target of this study is reducing traffic congestion and preventing traffic accidents.
Intrusion Detection System with Machine Learning Algorithms
Machine learning has become increasingly relevant in recent years, including in IT security. Algorithms are used to train intrusion detection systems to be able to react to new attack vectors. In this work, the basics of machine learning are explained and the results of two research projects are presented in order to investigate which algorithms are suitable for training a machine-learning intrusion detection system. In addition, the software library Scikit-Learn and the software Weka, with which the implementations take place, are presented.
Visionary Fusion: Empowering Robot Sight with Model-driven Multi-band Enhancement
Single image enhancement is crucial for improving visual quality in applications like robot vision. This study proposes a novel topic-model assisted multi-band image fusion method to enhance a single input image while preserving semantic information. The key innovation is using topic models for adaptive band fusion based on image content. Comparative experiments on benchmark datasets demonstrate that the proposed technique outperforms state-of-the-art methods in quantitative and qualitative evaluation. This study also provides real-time demonstrations and case studies in robot navigation, highlighting the efficacy of the proposed fusion strategy. The fused images exhibit finer details and color consistency leading to improved navigation and obstacle avoidance. This technique has wide-ranging applications for computer vision related tasks in autonomous systems.
Application of AI Driven Robots and Their Future Direction
AI-driven robots have found applications in various domains, including manufacturing, healthcare, logistics, agriculture, and more. Robots with computer vision and machine learning skills are improving accuracy, productivity, and quality control in manufacturing. In healthcare, robotic surgery and patient care assistance are becoming increasingly common. Logistics and warehousing benefit from AI-driven robots for tasks such as autonomous navigation and inventory management. Within the field of agriculture, robots find application in activities such as seeding, gathering crops, and overseeing crop conditions. These applications illustrate the versatility of AI-driven robots across industries.
Elevating Patient Care: Unveiling the Impact of Artificial Intelligence in Healthcare Research
Access to healthcare is crucial for a healthy start in life. Nevertheless, discussing health issues with a doctor can be challenging. Chatbots present a solution by facilitating communication through text or voice interfaces, using artificial intelligence to generate responses. These automated programs can consistently provide predefined responses or vary their replies based on specific keywords. Furthermore, machine learning can be employed to customize responses according to individual situations. Increasingly, hospitals, nursing homes, and private facilities are incorporating online human service chatbots into their websites. These chatbots engage with potential patients visiting the site, identify specialists, schedule appointments, and ensure the provision of suitable treatment. However, the application of artificial intelligence in sectors that impact people's lives raise individual apprehensions. The debate revolves around whether tasks like those mentioned should remain within the purview of human staff. This healthcare chatbot system aids hospitals in delivering round-the-clock online healthcare assistance, addressing both intricate and routine inquiries.
An Integrated Platform for Remote and Collaborative Augmented Reality and Virtual Reality of Artificial Intelligence Using AI Tools
In the digital age, virtual reality (VR) and augmented reality (AR) have emerged as transformative technologies, fundamentally reshaping the way individuals engage with both the physical and digital realms. This study provides a thorough and in-depth analysis of these immersive technologies, illuminating their complex features, cutting-edge uses, and evolving consequences. The study begins by clarifying the key distinctions between augmented reality (AR) and virtual reality (VR), as well as the range between reality augmentation and total virtualization. It explores the cutting-edge technologies that underpin augmented reality and virtual reality, exhibiting the progression of hardware, software, and sensory interfaces, including ground-breaking innovations like augmented reality glasses, spatial computing, and AI-driven experiences. The major theme is immersive user experiences, which is thoroughly examined through an investigation of advanced design principles. This study invites readers to embark on a deep journey into the heart of augmented and virtual reality, uncovering their profound implications across industries, their transformative potential, and the ethical considerations that accompany these immersive technologies. It is a testament to the ever-expanding landscape of AR and VR, offering a comprehensive view of their present state and the exciting possibilities that lie ahead.
Simultaneous Localization and Mapping (SLAM)
The necessity for user-friendly and intuitive control systems has been brought to light by the growing use of drones in a variety of businesses and by the general public. However, existing drone piloting techniques frequently need intricate manual manoeuvres and technical know-how, creating difficulties for novice and inexperienced pilots. The main goal of this effort is to create a ground control unit (GCU) that, instead of using a traditional radio remote controller, allows users to operate a quad copter using hand gestures and motions. The GCU reads the pilot’s motions and converts them into the appropriate flight orders for the drone using computer vision techniques and gesture recognition algorithms. This study focuses on the difficulties that novice and general drone pilots must overcome, such as the steep learning curve, challenging manual controls, and the danger of accidents in the early phases of flying. The suggested approach intends to streamline the learning process and increase the accessibility of drone piloting to a larger audience by utilising natural interaction modalities, such as gestures. For rookie pilots and amateurs who might not have prior experience or technical understanding, this is especially helpful. The significance of this work lies in its ability to enhance both the usability and security of drone operations. The system lessens the cognitive strain on the pilot and improves their overall control of the drone by doing away with the necessity for complicated manual controls and substituting them with intuitive gestures. Additionally, the suggested system’s long-range capabilities broaden the scope of possible drone applications by permitting operations beyond lines of sight and streamlining operations in arid or difficult areas. By offering a simple and intuitive control method, this work hopes to aid in the development and use of drones. By addressing the difficulties early and general public pilots encountered, it will become more usable by a wider spectrum of users. As a result, more drones may be used in more industries, drone operations may be safer, and new and creative uses for drones may be investigated.
Harnessing the Role of Chatbot in Enhancing Educational Accessibility and Engagement
Chatbots in education offer a valuable means to enhance accessibility and provide immediate, 24/7 support, assisting students and educators in accessing information and solving queries efficiently. They also contribute to personalized learning experiences, engaging students in interactive and informative interactions, ultimately fostering a more dynamic and effective educational environment. This research work delves into the creation and deployment of a chatbot, with a specific emphasis on its use within the CSIBER Institute in Kolhapur. Chatbots are sophisticated software applications created to engage in human-like language interactions, supplying information, responding to queries, and automating various tasks. They employ natural language processing and have found applications across various industries. The ‘Chatbot for CSIBER Institute, Kolhapur’ is presented as a sophisticated solution to streamline access to information about the institute, enhancing user accessibility, engagement, and satisfaction. The chatbot, built on Python Flask and BeautifulSoup, serves as a user-friendly platform for information retrieval, admission guidance, and more. The research emphasizes the need for enhanced information access, user engagement, accuracy, and convenience. The implementation includes specific functionalities, Python packages, and classes. The chatbot's testing on different devices, deployment on the Render platform, and usage insights are discussed. The complete source code is available on GitHub, opening possibilities for future research and improvements in chatbot technology.
Role of Medical and Assistive Robotics for Next Generation Healthcare
The integration of medical and assistive robotics into healthcare systems is revolutionizing the landscape of patient care and quality of life, heralding the dawn of Next Generation Healthcare. This study explores the multifaceted role of these innovative technologies in reshaping the healthcare paradigm, from diagnosis and treatment to rehabilitation and daily living assistance. Medical robotics, a rapidly evolving field, has led to the development of precision surgical tools, telemedicine platforms, and robotic exoskeletons. These applications enhance surgical accuracy, extend the reach of medical expertise to remote areas, and empower individuals with physical disabilities to regain mobility. Leveraging artificial intelligence and machine learning, medical robots can analyse vast datasets to facilitate early disease detection, personalized treatment plans, and predictive healthcare models. Simultaneously, assistive robotics are enabling people with disabilities and the elderly to live more independently. Devices such as robotic wheelchairs, exoskeletons, and robotic prosthetics are fostering autonomy and improving the overall well-being of users. These robots assist with mobility, activities of daily living, and rehabilitation, promoting physical and emotional recovery. However, the integration of medical and assistive robotics into healthcare presents unique challenges and ethical considerations, including privacy concerns and the need for clear regulatory frameworks. This study addresses these issues and offers potential solutions. Looking ahead, the role of medical and assistive robotics in Next Generation Healthcare is poised for exponential growth as technology continues to progress, these robotic systems will not only improve the efficiency and effectiveness of healthcare delivery but also create new opportunities for research, innovation, and enhanced patient results. This study serves as a comprehensive exploration of the transformative potential of medical and assistive robotics, highlighting the pivotal role they play in the evolution of healthcare toward a more patient-centric and inclusive future.
Digital Camera-based Pallet Detection System Using YOLOX
The automation of pallet recognition and localization is a widely investigated and sought topic, notably for forklift robots and pallet-picking instruments. Pallets are essential in warehouses and retail establishments. However, pallet recognition and pallet localization can be time-consuming and prone to errors, which can result in reduced efficiency and safety hazards. To address this, we develop the Pallet Detection System, a computer vision mechanism that detects and locates the coordinates of pallets in the environment, reducing errors and increasing warehouse efficiency. To achieve this, we are using computer vision algorithm YOLOX which is trained by large number of dataset images of pallets and non-pallet objects to optimize its accuracy in recognizing pallets in real time object. We have used a Python framework to support the pallet detection process and connected external webcams to obtain the location of pallets. This system reduces risk in warehouse, which increases the safety, reduces timecomplexity and optimizes the warehouse operations.
A Chatbot Based on Artificial Intelligence and Machine Learning
Artificial Intelligence (AI) has the potential to outperform human beings and to ultimately solve any problem one could think of and obtain high-productivity. AI depends heavily on both machine learning (ML) and deep learning (DL). Various studies show that people nowadays specially, the Gen-z are depressed out of loneliness. It is simple to recognize this reality as we take in our surroundings. Well, of course no one wants to be lonely, but one needs to have someone on their side to make it possible not to be lonely. But the problem arises that who has got the time and energy to interact with each other. After a long day, do you think one would have any energy left to give in these efforts, well you may not have that, but this AI will, that is for sure. Using the basic concepts of ML, this AI will make everything a lot easier for you.
Deep learning (deep structured learning)
Today, a wide variety of counsellors are accessible who provide individualized counselling to each student to assist them in making a well-organized career choice. Numerous websites offer online domain, personality, aptitude, and interest tests to determine which professional sector a particular student should select. A very challenging step in the career advising process is precisely identifying the most suited field and comprehending the current educational trends. Students do not seem to be content with the current method due to inconsistent results; so, a programme that might produce much more accurate results is needed. Therefore, a survey was conducted to learn about their issues so that we could try to find solutions for the next generation. We tested several machine learning algorithms after implementing them, and the one that produced the most accurate results was chosen.
User Personality: Prediction and Question Answering System
Personality refers to the general traits of a person based on his or her psychological and individual characteristics that reflect into a person’s overall nature and behaviour. Among the featured measures of this system are the MBTI (Myers Briggs Personality Type) which divides individual personalities into 16 distinct types across four axes. To predict the personality of an individual to get a hint about the individual, a personality prediction system is used. The focus of this research work is on fundamental machine learning and natural language processing methods for predicting personality from user descriptions. In personality prediction, personality prediction algorithms identify personality traits by extracting features from the descriptive text about the user. The MBTI dataset from Kaggle was used as the training and evaluation data for the model. The UI displays the profile cards of individual users and displays a prompt for the question-answering system. This system is implemented using Django, a python framework, transformers pipeline, NLP, and Machine learning algorithms.
Chatbot Using Natural Processing Languages: A Review
A chatbot is an artificial intelligence programme that imitates human communication through text or voice exchanges. On the basis of pre-established principles or machine learning algorithms, chatbots are designed to comprehend normal language and react to user queries or offer assistance. Using chatbot as a service interface is becoming more popular. Natural language understanding has practical uses across a variety of industries. These bots can be incorporated into many different platforms, including webpages, speech assistants, and chat applications. Chatbots' flexibility is an additional advantage. Chatbots do not have the same limitations as human support staff because they are softwarebased and can manage an almost infinite number of contacts at once. As a result, robots are a great way for businesses to increase their marketing or customer support efforts without having to pay a lot more money. Chatbot is basically used in communication. chatbot is a way of connect between computer to humans easily. Chatbots use AI, ML, and NLP languages to communicate. "Natural Language Processing" (NLP), a branch of artificial intelligence, focuses on the interaction between machine and human language. The main aim of creating a chatbot is to facilitate easy interaction with a computer interface. NLP improves understanding and produces intelligent responses. For a decent answer, a chatbot must accurately evaluate and comprehend human input.
The creation of humanoid robots has garnered a lot of attention in recent years. These human-like robots are frequently utilized in research, entertainment, and a variety of other fields. Ameca is a fascinating innovation that is grabbing the interest of academics, scientists, and the general public alike as one such humanoid robot. The sophisticated AI technology offered by Ameca is among its most amazing characteristics. This study provides an overview on the designing of Ameca and discusses the modelling with few developed humanoid robots. The development of humanoid development is an advanced technology in the field of AI, robotics, automation and electrical engineering. This study provides a summarized review on the Ameca including the future aspects and significance in different domains.
Robotics, automation technologies, humanoid robots, Ameca robot, deep learning algorithm
Copyright (c) 2022 JOURNAL OF ADVANCEMENTS IN ROBOTICS
Literature Review on Intelligent Attendance Systems
Every organization that relies on people today must, as a first step, account for its employees. As a result, the various organizations must spend a sizable sum of money on developing and maintaining an appropriate management system. Government agencies and educational institutions frequently use paper-based systems to record attendance. For instance, it requires quite some time to reach out to each student by calling their name at the start of the class in order to keep track of their attendance. Additional issues include fake signatures, names mislaid from spread-sheets, manually entering data into the system, and the potential for fake or so-called proxy attendance. These methods have a few issues that have gotten worse over time. It is essential to replace these antiquated procedures with contemporary ones in order to track attendance. As a result, using modern technologies, a numerous frequency of research and work has been produced in this area. Researchers are particularly interested in the automatic identification of a particular individual based on identifying traits like a QR code, fingerprint, an ID and password, or a face. This study reviews recent progress on advanced automation attendance monitoring systems. Our critical evaluation has concentrated on readings from the body of literature with regard to technology, application domain, and key findings.
Attendance system, automated, biometric recognition, face recognition
The Convergence of IoT with Virtual Reality and Telepresence
Telepresence is the fastest growing technology combining the Internet of Things (IoT) and Virtual Reality (VR). It is a robotic remote control that allows humans to sense their current location, VR provides vision, and IoT provides touch and control. Before telepresence, we could only imagine virtual worlds, not feel them. Ultrasonic sensors used in telepresence allow for this. This study is primarily about how VR is used in various fields. For example, it can be used for agricultural and educational development. It can create intelligent virtual environments such as virtual telepresence robots in smart agriculture and virtual inclusion with telepresence robots in classrooms, allowing users to experience virtual reality through (VR) headsets. As mentioned earlier, telepresence can also be used in a variety of areas, such as medical education, mine maintenance, and distance learning.
Artificial Intelligence and Automation: Driving Business Transformation through Integration
Artificial intelligence (AI) development is accelerating, and the combination of AI and automation is beginning to alter the corporate environment. Companies and businesses are concentrating their attempts to combine existing AI with automation processes to attain higher levels of productivity and excellence. The study illustrates robotics and AI, and it attempts to show the audience how the two are connected, and how they may be more successful when used together to provide a competitive edge.
2D LiDAR Based Obstacle Detection System for Obstacle Avoidance in 3D Space
Obstacle detection is an important feature in autonomous systems to enhance safety and to improve reliability of the operations in the unknown environment. Various techniques are adopted to detect the obstacle using passive and/or active sensors. Fusion of active and passive sensor techniques are also considered in some cases for better detection of obstacles and improve the accuracy of the detection with safe distance especially in an unmanned aerial robotic system. In this study, robust method of detecting the obstacle in 3D space using 2D LiDAR system is presented. The experiments conducted indoor in lab environment and outdoor showed excellent results in detecting the obstacle within the safe distance predefined based on the speed of the autonomous systems.
Digital Forensics: Fundamentals and Awareness to Society against Cybercrime
This study presents the brief about the digital forensics which is mainly required to the non-technical people for general knowledge. Through this, people will know the role and importance of Digital Forensics. The main purpose is to aware people against hacking, stealing their information, fake email, e-payment fraud, fake messages and call on social networking such as Facebook, Instagram, LinkedIn and other online apps available for chatting and entertainment purposes. Digital forensics focuses on the evidence gathering, data recovery, data examination and their analysis related to cybercrime. Cybercrime is any crime committed using computers and the internet. Cybercrime also includes data breaching which leads to security violation of user’s information without their concern. Today, the rate of cybercrime has increased expeditiously, and the laws are still not sufficient to control it. This shows the requirement of standard digital forensics laboratory. This laboratory will be responsible for implementing and monitoring the guidelines for digital forensics investigations, data integrity, and evidence persevering against civil or criminals.
Pipeline Inspection Robot Monitoring System
Nowadays, pipelines are the most commonly and widely used method of transporting fluids and gases. Regular inspection is necessary for the pipelines to work correctly. Humans must not enter potentially dangerous environments to inspect these pipelines. As a result of this, pipeline robots were created. These pipe inspection robots help in pipeline inspection, protecting numerous people from harm since human beings cannot enter the pipes and inspect them in case there is any such or kind of damage that requires repair. Despite several advancements, pipeline robots continue to have a number of limitations. The introduction of this in pipe inspection robots helps to solve many problems, such as leakage of the gas or fluid pipelines, rustiness, and also if the pipe is broken from any part.
Human Age and Gender Determination by Using Fingerprints
We present a review of technologies to determine human age and gender using fingerprints. Broadly, two methodologies are reviewed, i.e., ridge based human age and gender determination and Image based human age and gender determination. Ridge based technique uses ridge information with its variant statistics measures for classification of a human fingerprint into different classes of age groups and male/female distinction. These methods do not involve separate classifiers for classification rather determine different thresholds for different classes for classification. On the other hand, Image based techniques uses image processing concepts both in spatial and frequency domain like image transformation in frequency domain. These methods also need separate classifiers like KNN (K-Nearest Neighborhood) classifier, SVM (Support Vector Machine) etc. to determine age and gender of a specific human fingerprint. Considering the two approaches, different research papers are reviewed and their accuracy is stated for the difference. In the end, advantages and disadvantages are concluded for both methodologies along with the future scope in this field.
It is essential that the data be handled with accuracy, and certain exercises are carried out so that the computers can think and act on their own, which is where the advanced time of Deep Learning comes in. A computer needs access to earlier information and interactions in order to think. The use of Deep Learning in diverse geographies will be discussed in this study. Many experiments have been conducted to increase the intelligence of machines. It is possible that learning is a human trait that has been adopted by robots as well. Many different systems serve the same objective. ANN, CNN, and RNN are some of the important algorithms for Deep learning applications. Deep learning is a subset of AI. Until yet, just a few applications of substantial knowledge have been studied. With Deep learning, it is possible to solve problems in hitherto untapped application areas and sub-areas. This study provides an overview of historical and future Deep Learning application domains, sub-areas, and applications. Using brain organization layers, deep learning aids in faster and more accurate adaptation to changes in the environment.
Cyber-physical System Used in Fruit Harvesting
Harvesting machines have recently crossed a large area in one of the technologies for a few workers who are expanding. Due to substantial developments in this sector, it is still unclear when picking robotics will be widely used in a controlled orchard of fruit trees. This technique analyses the network systems, sensory perception strategies, fruit detachment techniques, and terminal displays of the most advanced intelligence fruit picking machines in highlighting the difficulties and identifying research areas. Research into very probable causes about the remarkable outcomes produced by modern picking systems outcomes in the development of unique map data, thorough maps of challenges, and future job routes that take into consideration coupled biological roots and project scope. There are far fewer fruit producers than there used to be due to the decreased interest of the labour force in agriculture. A shortage of agricultural labourers caused by current restrictions on international travel during the pandemic has significantly decreased agricultural productivity, which also has made the matters harder. Tons of replanted fruits and veggies died away on land where growers had previously hired temporary outsider agricultural workers.
Prediction of Disease by Symptoms Using Machine Learning
Machine Learning and data mining, both play the most popular role in analyzing and prediction in the field of health organization. It also helps to uncover different and new patterns in medicinal science. This project is around prediction of disease with help of the symptoms. This project aims to propose a model which will be strongly trained in detecting common diseases so that few of the common diseases can be easily tackled. In the back it is using data mining and classification in detail. In this project many algorithms like Random Forest, Naive Bayes, Logistic Regression, and many more can be utilized for health care diagnosis. Then at last, the score table will be designed and classifier with highest score will be picked.
RPA is a software-based solution that automates rule-based human tasks or business processes that involve repetitive, routine tasks, structured data, and deterministic outcomes. According to recent studies, the use of RPA improves productivity, costs, speed, and error reduction. While robotic process automation (RPA) is a popular topic in the business world, academic research on the subject lacks a theoretical and synoptic analysis. This study provides a systematic yet concise description of RPA, including its origin, development lifecycle, bot types, business applications, and RPA benefits. RPA tools automate the execution of a variety of activities and transactions across multiple software systems. This type of automation uses rule-based software to perform high-volume business process activities, freeing up human resources for more complex tasks. RPA enables CIOs and other decisionmakers to accelerate their digital transformation efforts while increasing their employees' return on investment (ROI). The purpose of this study is to explain the term Intelligent Automation (IA) as well as the differences and similarities between Artificial Intelligence and Robotic Process Automation. Finally, the study discusses the top three market leaders in RPA Tools for automation development.
Smart Irrigation Water Management Using Internet of Things
Irrigation is mainly useful in areas where there is shortage of rainfall. Irrigation therefore is provided for the better yield of crops which hitherto would have died or withered away as a result of water nutrients. The smart irrigation water management scheme of agricultural is required for an improved farming as especially where farming is the backbone of the economy. For two decades, IoT has become so popular because of its significant effect on human activities and its innovative services in agriculture such as smart irrigation farming, fresh water management, cost control, accuracy in farming sensor networking, rainfall and climate change control and so on. This study has evaluated Internet of Things-Enabled Irrigation (IoTsEI). It evaluated smart irrigation water management, methods of irrigation, types of things in IoT for smart irrigation water management, challenges of smart irrigation water management and architecture of IoT. The study reviewed five types of irrigation; Surface irrigation, Localized irrigation, Drip irrigation, Sprinkler irrigation, and Manual irrigation. Two major irrigation methods were identified; the traditional and IoT-based methods. The Traditional method has as its sub-methods, the; Check Basin, Furrow, Strip, and Basin methods. The IoT-based has as its sub-methods as, the; Physical things and Virtual things. The study recommends the virtual IoT water irrigation because it is capable of sharing, accessing, storing and processing data or information on the cloud interfaced with software application, multimedia contents and so on.
A biometric system is described in this work. A new age of engineering science has emerged in recent years, with items that are poised to establish a massive bazaar in the near future. “Biometrics” is what it is called. The creators of this new domain intend to develop ways that would allow a person’s credentials to be verified based on “biological” characteristics such as voice sound, diminuendos of motions, features of expression and other parts of the body, optic nerve or sword lily pattern. We begin by introducing contemporary biometrical attributes, particularly fingerprint recognition, and discussing their benefits and drawbacks when applied to biometric systems. We have discussed the technologies that might be utilized to ensure that there are no security breaches and that it is customized to the individual and totally unique. The general biometric security systems are discussed in this study along with the changes that could occur in the realm of biometric systems.
An Overview of Spam Detection Techniques
Spam is also known as unsolicited commercial email (UCE) has become a major worry for the internet's and worldwide commerce's long-term viability. Fake emails and other forgeries, such as phishing, are examples of spam emails. Which aim to collect confidential personal information about users on the network or to act illegally against authority. Spam produces a variety of issues, which can result in financial losses. Spam creates bottlenecks and traffic congestion, limiting memory space, processing power, and speed. Therefore, spam can be classified as one of the most common problems faced by an internet user. Many techniques have been developed to overcome spam. Several spam detection techniques are discussed in this document; so, a solution has been proposed to avoid this problem. This document aims to analyze existing research work on spam detection strategies and approaches, the state of the art, the phenomenon of spam detection, explore the basics of spam detection, the proposed detection scheme and possible mitigation schemes.