Zinedine Sayad
Cyber security plays an important role in the field of information technology. Securing the information have become one of the biggest challenges in the present day. Whenever we think about the cyber security, the first thing that comes to our mind is ‘cybercrimes’ which are increasing immensely every day. Various Governments and companies are taking many measures in order to prevent those crimes. Besides various measurements are taken seriously to stop the growth of those crimes. Cyber security is still a very big concern to many companies. This paper mainly focuses on the challenges faced by cyber security on the latest technologies. It also focuses on the cyber security techniques, ethics and the trends changing the face of cyber security. This paper will focus on firewalls, intrusion type, encryption and hashing, VPN, attacks, and Security.
Dr. Muhammad Shafique
Gigantic rates of data production in the era of Big Data, Internet of Thing (IoT), and Smart Cyber Physical Systems (CPS) pose incessantly escalating demands for massive data processing, storage, and transmission while continuously interacting with the physical world under unpredictable, harsh, and energy-/power- constrained scenarios. Therefore, such systems need to support not only the high-performance capabilities under tight power/energy envelop, but also need to be intelligent/cognitive and robust. This has given rise to a new age of Machine Learning (and, in general Artificial Intelligence) at different levels of the computing stack, ranging from Edge and Fog to the Cloud. In particular, Deep Neural Networks (DNNs) have shown tremendous improvement over the past 6-8 years to achieve a significantly high accuracy for a certain set of tasks, like image classification, object detection, natural language processing, and medical data analytics. However, these DNN require highly complex computations, incurring huge processing, memory, and energy costs. To some extent, Moore’s Law help by packing more transistors in the chip. However, at the same time, every new generation of device technology faces new issues and challenges in terms of energy efficiency, power density, and diverse reliability threats. These technological issues and the escalating challenges posed by the new generation of IoT and CPS systems force to rethink the computing foundations, architectures and the system software for embedded intelligence. Moreover, in the era of growing cyber-security threats, the intelligent features of a smart CPS and IoT system face new type of attacks, requiring novel design principles for enabling Robust Machine Learning.
In my research group, we have been extensively investigating the foundations for the next-generation energy-efficient and robust AI computing systems while addressing the above-mentioned challenges across the hardware and software stacks. In this talk, I will present different design challenges for building highly energy-efficient and robust machine learning systems for the Edge, covering both the efficient software and hardware designs. After presenting a quick overview of the design challenges, I will present the research roadmap and results from our Brain-Inspired Computing (BrISC) project, ranging from neural processing with specialized machine learning hardware to efficient neural architecture search algorithms, covering both fundamental and technological challenges, which enable new opportunities for improving the area, power/energy, and performance efficiency of systems by orders of magnitude. Towards the end, I will provide a quick overview of different reliability and security aspects of the machine learning systems deployed in Smart CPS and IoT, specifically at the Edge. This talk will pitch that a cross-layer design flow for machine learning/AI, that jointly leverages efficient optimizations at different software and hardware layers, is a crucial step towards enabling the wide-scale deployment of resource-constrained embedded AI systems like UAVs, autonomous vehicles, Robotics, IoT-Healthcare / Wearables, Industrial-IoT, etc.
Richard Lynn
The After the successful completion of artificial intelligence conference series, we are pleased to welcome you to the “Cyber Security and Ethical Hacking." The congress is scheduled to take place on November 18-19, 2021 in the beautiful city of Paris, France. Cybersecurity-2021 Conference will give you exemplary experience and great insights in the field of research.
Meetings LLC Ltd Organizes 1000+ Conferences Every Year across over USA, Europe, and Asia with assistance from 1000 progressively sensible social requests and Publishes 700+ Open access journals which contain in excess of 100000 well-known characters, assumed scientists as article board people.
Cyber Security is becoming a crucial driving force behind innovation in robotics and Lot, with a range of advances including Cloud Security & Virtualization, Cyber Crime and Fraud Management, Cyber Vulnerabilities, Incidents, And Mitigations Ethical Hacking and Countermeasures, Future Of Cyber security, Hacking & Hacking Tools. Universities also have begun to offer dedicated Internet of Things programs. Robotics will be getting to be progressively prevalent these times around learners. Actually, if you follow again of the Inception about Artificial Intelligence, you will discover that many technologies likewise have robotics that fills done the individuals minor cracks more successfully Furthermore provides for you a shinier vehicle. There need aid likewise robotics technology items accessible with stay with your eyewear What's more different optical units What's more that's only the tip of the iceberg tough.
Sami Fakhfakh
Ransomware is a type of malware. It restricts access to the computer system that it infects or the data that it stores (often using encryption techniques), and demands a ransom be paid to the creator(s) of the malware. This is in order for the restriction to be removed. Some forms of ransomware encrypt files on the system's hard disk. Others may simply lock the system and display messages intended to persuade the user to pay. Ransomware first became popular in Russia. Now the use of ransomware scams has grown internationally. In June 2013, McAfee said it had collected over 250,000 unique samples of ransomware in the first three months of 2013. This is more than double the number of the previous year. Crypto Locker, a ransom ware worm that surfaced in late-2013, had collected an estimated $3 million USD before it was taken down by authorities. In May 2017, a piece of ransom ware called WannaCry spread around the world. It lasted four days and affected over 200,000 computers in 150 countries.[4] Only about $130,000 (USD) was ever paid in ransom, but the attack affected a lot of large companies and organizations. The United Kingdom's National Health Service (NHS) was hit hard by WannaCry. Hospitals could not access their files, and so many surgeries were cancelled and patients had to be turned away.[5] The NHS was especially at risk because it was using a version of the Windows operating system called Windows XP that Microsoft no longer supported.[6] This meant that Microsoft had not been sending out security updates for this version of Windows, leaving it open to the WannaCry virus. Other systems were affected even though they were running newer versions of Windows, because their users had not yet installed the most recent security updates. Even though it was not designed to actually damage computers or their files, WannaCry led to a lot of wasted time and money, showing how vulnerable the world still is to ransom ware attacks. Nowadays, several companies, organizations or individuals are affected by a ransomware attack. in the philosophy of learning defense by attack. we will explain how this malware works, do a live code review example, test it on live and teach you how to protect yourself from it..
Yask
Operational technology or OT is a category of computing and communication systems to manage, monitor and control industrial operations with a focus on the physical devices (also known as Cyber Physical Devices) and processes being used by these Cyber Physical Devices (or Systems). OT often control essential services which affect people at large, such as water and power supply, oil & gas extraction to supply, mostly all large manufacturing units etc. Additionally, operational technology is also used to monitor these critical services to prevent hazardous conditions. Manipulation of these systems and processes could have extreme impacts on the end users of these services as well as workers within operational environments.Cyberattacks on critical infrastructure and strategic industrial assets are on the rise for some years now and is now believed to be among the top five global cyber risks. The cyberattacks have cost companies millions of dollars through the disruption of services and critical operations. To keep critical systems running and protect the financial results and reputation of any organization that includes industrial processes, it’s essential to improve industrial cyber security. However, securing OT environments, assessing them to determine remediation plans and strategies, and gaining visibility into them is challenging and requires different approaches than traditional IT environments.The IT environment is fairly protected and well-guarded by a Security Operations Center which keeps a constant vigil on the activities of the IT ecosystem under watch. The SOCs across the world have evolved and have reached a certain maturity in operations. However, for an OT environment, the SOC is still a new concept – primarily because the objectives of SOC of OT are different from those of IT. The mission and objectives of newer SOCs of today is about having an integrated security information and event management (SIEM) with a big data platform — complemented by workflow, automation and analytical tool. To create a SOC for OT would require re-engineering some of the OT processes, which because of being heavily dependent on the OT vendors result in a major task.Hence, there is a need to create a framework for OT SOC which helps organizations define a clear mission and objective statement for a fully operational OT SOC. The framework needs to define the roles (give directions) of the SOC team, the MSSP (if any), the OT vendors and the customer.
Nancy Leo
We are pleased to welcome you to the “International Conference on Robotics and Artificial Intelligence” after the successful completion of the series of Artificial Intelligence 2020. The congress is scheduled to take place in the beautiful city of Miami, USA on November 12-13, 2021. This Artificial Intelligence 2021 conference will provide you with an exemplary research experience and huge ideas.
The perspective of the Artificial Intelligence Conference is to set up transplant research to help people understand how treatment techniques have advanced and how the field has developed in recent years.
Longdom proffers our immense pleasure and honour in extending you a warm invitation to attend Artificial Intelligence 2021 in Miami, USA on November 12-13, 2021. It is focusing on “Innovations and Advancements in Robotics and Artificial Intelligence”, to enhance and explore knowledge among Artificial Intelligence community and to establish corporations and exchanging ideas. Providing the right stage to present stimulating Keynote talks, Plenary sessions, Discussion Panels, B2B Meetings, Poster symposia, Video Presentations and Workshop Artificial Intelligence anticipates over 200 participants around the globe with path breaking subjects, discussions and presentations. This will be a splendid feasibility for the researchers, delegates and the students from Global Universities and Institutes to interact with the world class scientists, speakers, Analyst, practitioners and Industry Professionals.
Longdom all the experts and researchers from the Robotics and Artificial Intelligence sector all over the world to attend “International Conference on Robotics and Artificial Intelligence (Artificial Intelligence 2021) which is going to be held on Miami, USA on November 12-13, 2021. Artificial Intelligence 2021 conference includes Keynote presentations, Oral talks, Poster Presentations, Workshops, and Exhibitors.
The most other engineering majors work with Artificial Intelligence, but the heart of Artificial Intelligence is Automation and Automation Engineering across all the disciples. Artificial Intelligence 2021 conference is also comprised of Best Post Awards, Best Oral Presentation Awards, Young Researchers Forums (YRF) and also Video Presentation by experts. We are glad to welcome you all to join and register for the “International Conference on Robotics and Artificial Intelligence” which is going to be held in Miami, USA on November 12-13, 2021.
Venkatesh Wadawadagi
Improving cities is a pressing global need as the world’s population grows and our species becomes rapidly more urbanized. In 1900 just 14 percent of people on earth lived in cities but by 2008 half the world’s population lived in urban areas. Today, 55% of the world’s population lives in urban areas and this percentage is expected to rise to 68% by 2050.
The use of artificial intelligence in smart cities can be life-changing if implemented in the right spaces. There are multiple zones in cities or in urban development where AI can be used to improve the performance and efficiency of the system. AI has the ability to understand how cities are being used and how they are functioning. It assists city planners in comprehending how the city is responding to various changes and initiatives. AI with the help of Deep Learning and Computer Vision has changed the way vehicle analytics is done. With these advancements, vehicle analytics is helping in implementing intriguing solutions like Toll booth automation, Smart parking, Gate security, ATCS (Adaptive Traffic Control System), RLVD (Red Light Violation Detection) etc. This talk starts by briefing about what's AI based vehicle analytics and what all it includes, and goes on to talk about varieties of applications of vehicle analytics including implementation and deployment challenges. Towards the end talk focuses on why it's need of the hour for this populated, industrialised and tech-driven era.
Selma Elizabeth Blum
Technology has become an essential aspect of law enforcement routine, helping police officers on solving, preventing and even predicting criminal activity globally. Artificial Intelligence is one of many important tools police can rely on. The harmonic integration between men and machine is now an essential part for operations success on security enforcement. How artificial intelligence can address criminal justice needs? Which innovations we have available to improve public safety? This article will demonstrate how artificial intelligence (AI) has became a major resource in numerous ways. It is now the ultimate solution for criminal justice, based on big data, algorithmics and machine learning to detect different patterns on human behavior. Those solutions are mainly based on pattern identification, image scanning, face recognition, sociodemographic analysis, voice parameters, actions, conducts, movements, biometrics and even emotions acknowledgement, which are now being considered an excellent evidence for deception detection, fraud, violence and terrorists acts. It is also used on DNA documentation, ballistics and profiling. Unlike humans, machines do not tire. On the opposite, it is proven on several ways, to be better than humans. It is confirmed machines are very good on identifying anomalous patterns and learning new patterns faster than humans. AI technologies provide the capacity to detect, predict and evaluate, overcoming errors and present virtuous results. The more amount of data, more precise will be the outcome. AI algorithms can potentially be used as a very efficient observer, increasing the accuracy of police officers on their complex daily routine. Predictive analysis (ex. PREDPOL) is one of many examples we will show to demonstrate how important those solutions subsist and innovate the security context. Those systems process large volumes of information simultaneously, providing precise outcomes. This article will deeply investigate and compare several platforms used by different law enforcement units around the globe, pointing new solutions, challenges and potential developments needed. As a conclusion, we have noticed how important was the introduction of AI on law enforcement routine, performing risk evaluations, crime solutions and delinquency prevention.
Ramin Mobasseri
With the exponential rise of AI usage in Banks, many financial organizations are still struggling with building efficient Model Development Life Cycles (MDLC) and the means to expedite business value realization and return of investment (ROI). There are several contributing factors which can give rise in less than optimal MDLC, such as, lack of proper data governance and processes around it as well as lack of performant AI solutions and platforms.
In this session, you will learn how to use most essential business value accelerators (BVA) to expedite Data Science Discovery, Data Ingestion, and Model Development leading to most optimal Model Business integration. This session will provide lots of valuable and real to life strategies and executable plans to help reduce your MDLC and time to market by at least 50%
Sections will include but not limited to:
- Overview of AI Acceleration in highly regulated environments
- Effective use of tools and processes in each phase of MDLC
- Hints and Tips on building effective meta use cases with the lines of businesses, e.g. Fraud, Anti Money Laundering amongst many others
- Agile Blueprints for Machine Learning (ML) and Natural Language Processing (NLP)
- Effective Data Governance Policy and Strategy
V.Kanimozhi
Although numerous profound learning models had been proposed, this research article added to symbolize the investigation of significant deep learning models on the sensible IoT gadgets to perform online protection in IoT by using the realistic Iot-23 dataset. It is a recent network traffic dataset from IoT appliances. IoT gadgets are utilized in various program applications such as domestic, commercial mechanization, and various forms of wearable technologies. IoT security is more critical than network security because of its massive attack surface and multiplied weak spot of IoT gadgets. Universally, the general amount of IoT gadgets conveyed by 2025 is foreseen to achieve 41600 million. So we would like to conduct IoT intrusion and anomaly detection systems of detecting IoT-based attacks by introducing various deep learning models on artificial neural networks such as Recurrent Neural Networks, Convolutional Neural Networks, Multilayer Perceptron, Supervised GAN Adversarial Network, etc in both binary and multiclass classification modes in IoT- cybersecurity. We generate wide performance metric scores such as Accuracy, false alarm rate, detection rate, loss function, and Mean Absolute error.
Hsuan-Hsuan Chao
Climate change and industrial development have brought greater uncertainty to water resources, and the quality of water has a very significant impact on humans and the entire ecosystem. The current water quality testing relies on the data collected by various monitoring systems, some of which are not immediately available or require more expensive equipment to analyze. Most experts agree that the amount of dissolved oxygen (DO) in the water is the main indicator for judging the quality of water. However, the process of obtaining information is more complicated and cumbersome. If the difficulty of obtaining the information can be simplified, it will make water resources better. Management is more efficient.
In recent years, artificial intelligence is often developed to assist in many complex decision-making tasks. We develop a prediction model based on LSTM. We design a machine learning model and provide a large amount of data to make it find the rules and learn from it. Improve the predictive ability of the model. Through the model, the water quality can be monitored and analyzed, and the data obtained can be used to judge and predict the water quality state and deal with water pollution problems in time.
Farah Jemili
The recent White House report on artificial intelligence (AI) highlights the importance of AI and the need for a clear roadmap and strategic investment in this area. As AI emerges from science fiction to become the frontier of world-changing technologies, there is an urgent need to systematically develop and implement AI to see its real impact in the next generation of industrial systems, known as Industry 4.0. This article provides an overview of the current state of AI in industrial applications and offers our contribution to the deployment of AI in cybersecurity for Industry 4.0.
Sergio Mastrogiovanni*
In 2020, COVID-19 exposed the fragility of health sector. In the US in particular, the most expensive healthcare system in the world, it also faces a tremendous challenge in responding to diagnostic needs. One of the biggest challenges in medical imaging like MRI is not the high cost per se, but the capacity. An MRI session lasts between 15 and 60 minutes. There are hospitals with only one device or even no one. Medical imaging is one of the best use cases for AI in healthcare, but lack of physician engagement and data bottlenecks can make the technology less useful than promised. When used to decode the complicated nature of MRIs, CT scans, and other testing modalities, advanced analytical tools have proven their ability to extract meaningful information to improve decision-making, sometimes with greater precision than humans themselves. . With deep learning, it is possible to capture less data and thus scan faster, while preserving or even enhancing the rich information content of MRI images. The key is to train artificial neural networks to recognize the underlying structure of the images in order to fill in the missing views from the accelerated scan. This approach is similar to how humans process sensory information. When we experience the world, our brains often receive an incomplete image, as in the case of darkened or dimly lit objects, that we need to convert into actionable information.
George Kantor
Since the dawn of agriculture, crop monitoring and inspection remains a mainstay of every farmer’s routine. Today, many farmers visually inspect their crops armed with a variety of tools to help ensure ideal plant health and performance. Although human visual inspection remains an essential part of agriculture, it has many challenges and many limitations. Research over the last decade or so has assessed the applicability of computer vision and deep learning to address the crop inspection challenge [Nusk2011, Nusk2014, Blom2009, Herr2015]. These approaches have shown tremendous promise, they are only just now beginning to go beyond the research phase into commercialization. Around 2015, image processing methods using deep neural networks began to replace the earlier classical computer vision approach, providing both better performance and more generalizable results. Again, through the early work of the CMU team, a StalkNet [Bawe2018] architecture was developed, which combines an RCNN feature detector with a GAN based pixel segmenter. To date, StalkNet has been trained to measure dozens of widely varying features in different crops, ranging from leaf necrosis to fruit ripeness to sorghum seed size for grain yield. The first market Bloomfield has chosen to address is grape growing and vineyard inspection, but we see CEA as a natural next step in the progression of our technology and a large opportunity. Flash combines highresolution flash lighted stereo RGB images with a cloud-based deep learning pipeline to inspect and assess the health and performance of each and every plant in a field or grow, one plant at a time. The result, so far, with Bloomfield’s vineyard customers is yield estimation, pest/disease detection, labor saving and digitalization. This comprehensive analysis forms the foundation for Bloomfield’s health and performance assessment of each geolocated plant, one plant at a time through a web-based dashboard accessible via tablet, cellphone or computer. Bloomfield’s approach to inspecting and assessing plants contrasts sharply with the visual inspection which includes sparse subjective judgements of randomly sampled plant data.
Karishma Nanda
This paper aims to contribute to the cold start problem in recommender system with Neural Network based approach. There are several attempts in academia and in the industry to improve the recommender system. For instance, latent matrix factorization is an algorithm that solves the recommendation problem, it produces efficient outcomes from the core problem. Latent factors are not directly observed but are inferred from other factors. It can be computed by assuming a specific number of such factors and then transforming the large user-item matrix into a smaller matrix based on previously assumed factors. These smaller matrices can be multiplied to reproduce a close approximation to the original user-item matrix using a technique called matrix factorization. Assuming that the matrix can be written as the product of two low-rank matrices, matrix factorization techniques seek to retrieve missing or corrupted entries. Matrix factorization approximates the matrix entries by a simple fixed-function — namely, the inner product — acting on the corresponding row and column latent feature vectors. Substituting a neural architecture for the inner product that learns from the data, improves recommendation problem and deals with the cold start problem
Joan Manuel Rodriguez Nunez
Objective: By the lack of initiative by force (Faith) Iron man lives. Iron defi ciency causes anemia, anemia causes dementia, Alzheimer dementia and Alzheimer’s produces cognitive impairment in memory produces bases. Well hear him. The Iron Will Alkaline, the answer is yes. Methodology: On the basis of Love and the use of Iron and its allies, which are the B vitamins, Vitamin C, E and vitamin A. It is necessary to remember that there is to try to fi ght the greatest sustenance Anemia in all its contrarestantes. Conclusion: The theory focuses on the oxygenation of the blood, which must be done, where the Warburg Alkaline Diet is demonstrated, among other factors it is necessary to emphasize the oxygenation that consists of the mental and physical, which is reduced in Sleeping correctly, Warburg Alkaline Diet, Drink Enough Water, Make Walks or Moderate Exercises, Comfort and Drink Iron, Vitamin C, Vitamin E, Complex B and Vitamin A. All this consists in Producing New Oxygen.
Affan Shoukat
The problem of predicting a set of tensorial outputs based on inputs of tensor form has been receiving increasing attention in recent years. This problem arises in various areas of mathematical, statistical and computational sciences, and generalizes the case of the widely used scalar-on-scalar regression methods. In this paper, we develop a tensor-on-tensor re gression framework using a hybrid of convolutionary neural networks and lowrank tensor learning algorithms. Our proposed framework integrates several promising approaches which have been developed previously to tackle this problem and extends their domain of applica tions. In particular, we demonstrate the advantage of this framework in comparison with traditional methods through an example of predicting the third-order tensors which arises within the procedures required for performing the time-homogeneous top-K ranking algo rithm. Computational results are further provided which pertain to analysis of the U.S. stock market during the time period from January 1990 to December 2019
Ria Silva
We are pleased to welcome you to the “International Conference on Stem Cell, Tissue Engineering and Regenerative Medicine” after the successful completion
of the series of Stem Cell Research 2020. The congress is scheduled to take place in the beautiful city of Rome, Italy on September 29-30, 2021. This Stem Cell
Research 2021 conference will provide you with an exemplary research experience and huge ideas.
The perspective of the Stem Cell Conference is to set up transplant research to help people understand how treatment techniques have advanced and how the
field has developed in recent years.
Longdom proffers our immense pleasure and honour in extending you a warm invitation to attend Stem Cell Research 2021 in Rome, Italy on September 29-30,
2021. It is focusing on “Advancement in Stem Cell Biology and Regenerative Medicine”, to enhance and explore knowledge among Stem Cell community and to
establish corporations and exchanging ideas. Providing the right stage to present stimulating Keynote talks, Plenary sessions, Discussion Panels, B2B Meetings,
Poster symposia, Video Presentations and Workshop Stem Cell Research 2021 anticipates over 200 participants around the globe with path breaking subjects,
discussions and presentations. This will be a splendid feasibility for the researchers, delegates and the students from Global Universities and Institutes to interact
with the world class scientists, speakers, Analyst, practitioners and Industry Professionals.
Longdom all the experts and researchers from the Stem Cell, Tissue Engineering and Regenerative Medicine sector all over the world to attend “International
Conference on Stem Cell, Tissue Engineering and Regenerative Medicine (Stem Cell Research 2021) which is going to be held on Rome, Italy on September
29-30, 2021. Stem Cell Research 2021 conference includes Keynote presentations, Oral talks, Poster Presentations, Workshops, and Exhibitors.
Stem Cell Research 2021 conference is also comprised of Best Post Awards, Best Oral Presentation Awards, Young Researchers Forums (YRF) and also
Video Presentation by experts. We are glad to welcome you all to join and register for the “International Conference on Stem Cell, Tissue Engineering and
Regenerative Medicine” which is going to be held in Rome, Italy on September 29-30, 2021.
Ademola Olayinka
Background: Nasal carriage of Community-Acquired Methicillin-resistance Staphylococcus aureus (CA-MRSA) is recognized for its rapid community spread and
tendency to cause various infections especially in communities with a large population where personal hygiene is poor. We sought to investigate the prevalence
and evaluated the possible risk factors of CA-MRSA among the healthy population.
Methods: Nasal swabs collected from 392 males and 308 females using the multi-stage sampling technique were cultured for Staphylococcus aureus. Isolates
were identified by conventional biochemical tests, Microbact™ 12S identification kit and confirmed with 16SrRNA. Antibiotic susceptibility testing was performed
using the Kirby-Bauer disc diffusion technique. Finally, isolates were further investigated for methicillin resistance by using the cefoxitin disk diffusion test
followed by polymerase chain reaction amplification of MecA and Nuc genes. Proportions were tested using Chi-Square and Fisher’s Exact Probability Test in
Epi InfoTM.
Results: The results showed 31.9% and 9.4% prevalence of S. aureus nasal carriage and Methicillin-resistance Staphylococcus aureus respectively. Low
educational background (Ï°2 =36.817, P Ë? .001), age >40-50 years (Ï°2 = 8.849, P = .003), recent antibiotics use (Ï°2 = 7.556, P = .006), recent hospital visitation
(Ï°2 = 8.693, P = .003) and male gender (Ï°2 = 9.842, P = .002) are significantly associated with CA-MRSA. The results of this research study show that CA-MRSA
are highly multi-drug resistant.
Conclusion: The study established a high prevalence and resistance burden of CA-MRSA in the population; this poses a serious public health concern in the
region and necessitates the demands for continuous surveillance on the colonization state of CA-MRSA to restrict the transmission of the bacterium in the
community.
Mohd Nadhir Ab Wahab
According to the report, about 1 in 50 families live with paralysis – around 5.4 million individuals. It is the same number of individuals as the collective residents
of Los Angeles, Philadelphia, and Washington D.C., which is almost 40% greater than the standard figures. Typical forms of paralysis include Monoplegia,
Hemiplegia, Diplegia, Paraplegia, and Quadriplegia. Another paralysis, except for Diplegia, had lower limbs (either partly or wholly) requiring a wheelchair to
support them in terms of mobility. Many wheelchairs, however, enable them to use their hands to maneuver around. It may concern patients with Hemiplegia or
Quadriplegia, as their hand movements are very restricted. As a result, this study suggested a wheelchair motion control using eye-tracking. The wheelchair is
portrayed by a differential mobile robot, where the same moving principle is shared. This project's key feature is that the patient determines the direction of travel
of the wheelchair without physically stressing it. This project consists of a video streaming module, a face detection module, an eye recognition module, and a
robot control module. The camera streams video to detect the face in live mode. The video frames will then be analyzed to identify the eye and decide the eye's
location by interacting with the mobile robot to drive the robot forward, turn left, turn right, and stop. Machine learning is used to detect the face and identify the
eye to achieve better results using the face hallmark detector that implements the One Millisecond Face Alignment and the Regression Tree Ensemble. Several
studies carried out have shown that the concept of monitoring the motion of a wheelchair by eye-tracking is achievable.
Patrick fiati
SMILE (Smartphone Intuitive Likeness and Engagement) application, a portable Android application that allows a human to control a robot using speech input.
SMILE is a novel open source and platform independent tool that will contribute to the robot soccer research by allowing robot handlers to verbally command
robots. The application resides on a smartphone embedded in the face of a humanoid robot, using a speech recognition engine to analyze user speech input
while using facial expressions and speech generation to express comprehension feedback to the user. With the introduction of intuitive human robot interaction
into the arena of robot soccer, we discuss a couple specific scenarios in which SMILE could improve both the pace of the game and autonomous appearance
of the robots. The ability of humans to communicate verbally is essential for any cooperative task, especially fast-paced sports. In the game of soccer, players
must speak with coaches, referees, and other players on either team. Therefore, if humanoids are expected to compete on the same playing field as elite soccer
players in the near future, then we must expect them to be treated like humans, which include the ability to listen and converse. SMILE (Smartphone Intuitive
Likeness and Engagement) is the first platform independent smartphone based tool to equip robots with these capabilities. Currently, humanoid soccer research
is heavily focused on walking dynamics, computer vision, and intelligent systems; however human-robot interaction (HRI) is overlooked. We delved into this
area of robot soccer by implementing SMILE, an Android application that sends data packets to the robot’s onboard computer upon verbal interaction with a user.
Rabiah Ahmad
Recent information technologies are able to facilitate the transformation of traditional administrative processes to services which can be performed online. The
rapid growth of ICT is proved to be aligned with its application for the 4th Industry Revolution. Today, information security has become a vital entity to most
organizations due to current trends in information transfer through a borderless and vulnerable world. The concern and interest in information security is mainly
due to the fact that information security risk analysis (ISRA) is seen as a focal method not only to identify and prioritize information assets but also to identify
and monitor the specific threats that an organization induces; especially the chances of these threats occurring and their impact on the respective businesses.
Thus, a total of 18 years research in Information Security were conducted, and their findings were gathered and analysed meticulously. Most of the research
were particularly focusing in exploring the various aspect of security threats and its countermeasure through empirical researches, tool development, systematic
literature review and dynamic analysis impacted from theoretical knowledge development to its implementation growth in Organization. Our reviews suggested
that risks analysis demand critical and deep research to make sure they are able to introduce effective security counter measure. Our research focused on
critical information infrastructure such as Healthcare, Power System and Manufacturing. One of the study, we applied empirical study to categorize threats
and calculate risks for Healthcare system. In addition to that we developed tool using Machine Learning to explore various type of risks categories using the
same dataset. In other cases our research explored information requirements needed for SME based company in implementing risk analysis and comply with
standard. With the same objectives i.e., to introduce effective security counter measure, we explored different methods for analyzing risks, vulnerabilities and
threats using survival analysis. We further explored those parameters at critical sectors such as Oil& GAS and Manufacturing. For this, terms used are slightly
different yet aim/intention/ motive almost similar. The research finding explored Cyber Terrorism and its impact to critical system. Our come concluded that
Cyberterrorism required advanced technology for protection. The protection system should incorporated latest technology, expert, and systematic process. Our
proposed safeguards for cyber terrorism activities comply with international standard ISO 27100. Complexity in performing risks analysis is due to various type
of data i.e., either qualitative or quantitative or both. Most of risk analysis tools in the market only allow single type of data to be analysed. Therefore, in order to
facilitate this issue we explored and introduced techniques that allow both type of data to be treated as one. As a conclusion from the 18 years research in Risks,
Vulnerabilities and Threats Analysis in Information Security involved with various type of platform, software, hardware, middleware and Cyber Physical System.
Those technologies rapidly growth and backbone for Industry Revolution 5.0.
Sadek Hossain Asif
The advancements of computer science and its related fields are making our tasks easier in almost every scientific and non-scientific field. The use of machine
learning in the field of drug discovery and development is accelerating so fast and helping us to discover anti-viral drugs for devastating viruses like coronavirus.
The author will discuss using a deep reinforcement learning model 'ORGAN' which is a modified version of Generative Adversarial Network for predicting the
potential anti-viral of coronavirus. The author used the deep reinforcement learning model (ORGAN) to generate potential candidates’ drugs, with a λ of 0.2
and epochs of 240 and a sample set of 6400, 10 good sample SMILES were generated and the Solubility or LogP of these samples is 0.7098. Then using the
coronavirus as a target, all the good samples of SMILES were bounded and the drug with the highest binding affinity (Most negative value) is C18H15ClN4O2
also known as Olutasidenib which can be the potential anti-viral drug of coronavirus.
Farah Jemili
The recent White House report on artificial intelligence (AI) highlights the importance of AI and the need for a clear roadmap and strategic investment in this area. As AI emerges from science fiction to become the frontier of world-changing technologies, there is an urgent need to systematically develop and implement AI to see its real impact in the next generation of industrial systems, known as Industry 4.0. This article provides an overview of the current state of AI in industrial applications and offers our contribution to the deployment of AI in cybersecurity for Industry 4.0.
Hsuan-Hsuan Chao
Climate change and industrial development have brought greater uncertainty to water resources, and the quality of water has a very significant impact on humans and the entire ecosystem. The current water quality testing relies on the data collected by various monitoring systems, some of which are not immediately available or require more expensive equipment to analyze. Most experts agree that the amount of dissolved oxygen (DO) in the water is the main indicator for judging the quality of water. However, the process of obtaining information is more complicated and cumbersome. If the difficulty of obtaining the information can be simplified, it will make water resources better. Management is more efficient. In recent years, artificial intelligence is often developed to assist in many complex decision-making tasks. We develop a prediction model based on LSTM. We design a machine learning model and provide a large amount of data to make it find the rules and learn from it. Improve the predictive ability of the model. Through the model, the water quality can be monitored and analyzed, and the data obtained can be used to judge and predict the water quality state and deal with water pollution problems in time.
Isham Alzoubi,
The aim of this work was to determine best linear model Adaptive Neuro-Fuzzy Inference System (ANFIS) and Sensitivity Analysis in order to predict the energy consumption for land leveling. In this research effects of various soil properties such as Embankment Volume, Soil Compressibility Factor, Specific Gravity, Moisture Content, Slope, Sand Percent, and Soil Swelling Index in energy consumption were investigated. The study was consisted of 90 samples were collected from 3 different regions. The grid size was set 20 m in 20 m (20*20) from a farmland in Karaj province of Iran. The values of RMSE and R2 derived by ICA-ANN model were, to Labor Energy (0.0146 and 0.9987), Fuel energy (0.0322 and 0.9975), Total Machinery Cost (0.0248 and 0.9963), Total Machinery Energy (0.0161 and 0.9987) respectively, while these parameters for multivariate regression model were, to Labor Energy (0.1394 and 0.9008), Fuel energy (0.1514 and 0.8913), Total Machinery Cost (TMC) (0.1492 and 0.9128), Total Machinery Energy (0.1378 and 0.9103).Respectively, while these parameters for ANN model were, to Labor Energy (0.0159 and 0.9990), Fuel energy (0.0206 and 0.9983), Total Machinery Cost (0.0287 and 0.9966), Total Machinery Energy (0.0157 and 0.9990) respectively, while these parameters for Sensitivity analysis model were, to Labor Energy (0.1899 and 0.8631), Fuel energy (0.8562 and 0.0206), Total Machinery Cost (0.1946 and 0.8581), Total Machinery Energy (0.1892 and 0.8437) respectively, respectively, while these parameters for ANFIS model were, to Labor Energy (0.0159 and 0.9990), Fuel energy (0.0206 and 0.9983), Total Machinery Cost (0.0287 and 0.9966), Total Machinery Energy (0.0157 and 0.9990) respectively, Results showed that ICA_ANN with seven neurons in hidden layer had better. According to the results of Sensitivity Analysis, only three parameters; Density, Soil Compressibility Factor and, Embankment Volume Index had significant effect on fuel consumption. According to the results of regression, only three parameters; Slope, Cut-Fill Volume
(V) and, Soil Swelling Index (SSI) had significant effect on energy consumption. Using adaptive neuro-fuzzy inference system for prediction of labor energy, fuel energy, total machinery cost, and total machinery energy can be successfully demonstrated.
Joan Manuel Rodriguez Nunez
Objective: By the lack of initiative by force (Faith) Iron man lives. Iron deficiency causes anemia, anemia causes dementia, Alzheimer dementia and Alzheimer’s produces cognitive impairment in memory produces bases. Well hear him. The Iron Will Alkaline, the answer is yes.
Methodology: On the basis of Love and the use of Iron and its allies, which are the B vitamins, Vitamin C, E and vitamin A. It is necessary to remember that there is to try to fight the greatest sustenance Anemia in all its contrarestantes.
Conclusion: The theory focuses on the oxygenation of the blood, which must be done, where the Warburg Alkaline Diet is demonstrated, among other factors it is necessary to emphasize the oxygenation that consists of the mental and physical, which is reduced in Sleeping correctly, Warburg Alkaline Diet, Drink Enough Water, Make Walks or Moderate Exercises, Comfort and Drink Iron, Vitamin C, Vitamin E, Complex B and Vitamin A. All this consists in Producing New Oxygen.
V. Kanimozhi,
Although numerous profound learning models had been proposed, this research article added to symbolize the investigation of significant deep learning models on the sensible IoT gadgets to perform online protection in IoT by using the realistic Iot-23 dataset. It is a recent network traffic dataset from IoT appliances. IoT gadgets are utilized in various program applications such as domestic, commercial mechanization, and various forms of wearable technologies. IoT security is more critical than network security because of its massive attack surface and multiplied weak spot of IoT gadgets. Universally, the general amount of IoT gadgets conveyed by 2025 is foreseen to achieve 41600 million. So we would like to conduct IoT intrusion and anomaly detection systems of detecting IoT-based attacks by introducing various deep learning models on artificial neural networks such as Recurrent Neural Networks, Convolutional Neural Networks, Multilayer Perceptron, Supervised GAN Adversarial Network, etc in both binary and multiclass classification modes in IoT- cybersecurity. We generate wide performance metric scores such as Accuracy, false alarm rate, detection rate, loss function, and Mean Absolute error.
Neda Navidi
The main focus of this talk is "human-AI teaming", specifically the mode of "human-AI collaboration" where humans and AIRL-based agents accomplish tasks together in a multi-agent system. Therefore, the objective cannot be achieved by just a lone human or agent, and the responsibilities in the environment are partitioned and/or shared between humans and agents. Collaborative multi-agent reinforcement learning (MARL) as a specific category of reinforcement learning (RL) provides effective results with agents learning from their observations, received rewards, and internal interactions between agents. However, centralized learning methods with a joint global policy in a highly dynamic environment present unique challenges in dealing with large amounts of information. This study proposes two innovative solutions to address the complexities of a collaboration between human and multiple RL-based agents (referred to hereafter as “Human-MARL teaming”) where the goals pursued cannot be achieved by a human alone or agents alone. The first innovation is the introduction of a new open-source MARL framework, called COGMENT, to unite humans and agents in real-time complex dynamic systems and efficiently leverage their interactions as a source of learning. The second innovation is our proposal of a new hybrid MARL method, named Dueling Double Deep Q learning MADDPG (D3-MADDPG) to allow agents to train decentralized policies parallelly in a joint centralized policy. This method can solve the overestimation problem in Q-learning methods of value-based MARL. We demonstrate these innovations by using a designed real-time environment with unmanned aerial vehicles driven by RL agents, collaborating with a human to fight fires. The team of RL agent drones autonomously looks for fire seats and the human pilot douses the fires. The results of this study show that the proposed collaborative paradigm and the open-source framework leads to significant reductions in both human effort and exploration costs. Also, the results of the proposed hybrid MARL method shows that it effectively improves the learning process to achieve more reliable Q-values for each action, by decoupling the estimation between state value and advantage value.
Ramin Mobasseri
With the exponential rise of AI usage in Banks, many financial organizations are still struggling with building efficient Model Development Life Cycles (MDLC) and the means to expedite business value realization and return of investment (ROI). There are several contributing factors which can give rise in less than optimal MDLC, such as, lack of proper data governance and processes around it as well as lack of performant AI solutions and platforms. In this session, you will learn how to use most essential business value accelerators (BVA) to expedite Data Science Discovery, Data Ingestion, and Model Development leading to most optimal Model Business integration. This session will provide lots of valuable and real to life strategies and executable plans to help reduce your MDLC and time to market by at least 50%
Md.Sadek Hossain Asif
The advancements of computer science and its related fields are making our tasks easier in almost every scientific and non-scientific field. The use of machine learning in the field of drug discovery and development is accelerating so fast and helping us to discover anti-viral drugs for devastating viruses like coronavirus. The author will discuss using a deep reinforcement learning model 'ORGAN' which is a modified version of Generative Adversarial Network for predicting the potential anti-viral of coronavirus. The author used the deep reinforcement learning model (ORGAN) to generate potential candidates’ drugs, with a λ of 0.2 and epochs of 240 and a sample set of 6400, 10 good sample SMILES were generated and the Solubility or LogP of these samples is 0.7098. Then using the coronavirus as a target, all the good samples of SMILES were bounded and the drug with the highest binding affinity (Most negative value) is C18H15ClN4O2 also known as Olutasidenib which can be the potential anti-viral drug of coronavirus.
Selma Elizabeth Blum
Technology has become an essential aspect of law enforcement routine, helping police officers on solving, preventing and even predicting criminal activity globally. Artificial Intelligence is one of many important tools police can rely on. The harmonic integration between men and machine is now an essential part for operations success on security enforcement. How artificial intelligence can address criminal justice needs? Which innovations we have available to improve public safety? This article will demonstrate how artificial intelligence (AI) has became a major resource in numerous ways. It is now the ultimate solution for criminal justice, based on big data, algorithmics and machine learning to detect different patterns on human behavior. Those solutions are mainly based on pattern identification, image scanning, face recognition, sociodemographic analysis, voice parameters, actions, conducts, movements, biometrics and even emotions acknowledgement, which are now being considered an excellent evidence for deception detection, fraud, violence and terrorists acts. It is also used on DNA documentation, ballistics and profiling. Unlike humans, machines do not tire. On the opposite, it is proven on several ways, to be better than humans. It is confirmed machines are very good on identifying anomalous patterns and learning new patterns faster than humans. AI technologies provide the capacity to detect, predict and evaluate, overcoming errors and present virtuous results. The more amount of data, more precise will be the outcome. AI algorithms can potentially be used as a very efficient observer, increasing the accuracy of police officers on their complex daily routine. Predictive analysis (ex. PREDPOL) is one of many examples we will show to demonstrate how important those solutions subsist and innovate the security context. Those systems process large volumes of information simultaneously, providing precise outcomes. This article will deeply investigate and compare several platforms used by different law enforcement units around the globe, pointing new solutions, challenges and potential developments needed. As a conclusion, we have noticed how important was the introduction of AI on law enforcement routine, performing risk evaluations, crime solutions and delinquency prevention.
Venkatesh Wadawadagi
Improving cities is a pressing global need as the world’s population grows and our species becomes rapidly more urbanized. In 1900 just 14 percent of people on earth lived in cities but by 2008 half the world’s population lived in urban areas. Today, 55% of the world’s population lives in urban areas and this percentage is expected to rise to 68% by 2050. The use of artificial intelligence in smart cities can be life-changing if implemented in the right spaces. There are multiple zones in cities or in urban development where AI can be used to improve the performance and efficiency of the system. AI has the ability to understand how cities are being used and how they are functioning. It assists city planners in comprehending how the city is responding to various changes and initiatives. AI with the help of Deep Learning and Computer Vision has changed the way vehicle analytics is done. With these advancements, vehicle analytics is helping in implementing intriguing solutions like Toll booth automation, Smart parking, Gate security, ATCS (Adaptive Traffic Control System), RLVD (Red Light Violation Detection) etc. This talk starts by briefing about what's AI based vehicle analytics and what all it includes, and goes on to talk about varieties of applications of vehicle analytics including implementation and deployment challenges. Towards the end talk focuses on why it's need of the hour for this populated, industrialised and tech-driven era.
Radio wire is an article used to snatch the signs from space. It is utilized for the activity of the remote items by getting the waves and disseminating them accordingly. Antenna configuration can go from virtually difficult to exceptionally basic.
PC network is a gathering of PCs that utilization a bunch of normal correspondence conventions over computerized interconnections to share assets situated on or given by the organization hubs. The interconnections between hubs are shaped from a wide range of telecom network advancements, in view of truly wired, optical, and remote radio-recurrence techniques that might be organized in an assortment of organization geographies. The hubs of a PC organization may incorporate PCs, workers, organizing equipment, or other specific or broadly useful hosts.
The motivation behind this paper is to frame a fundamental theory
about how to distinguish qualities that a pioneer needs to zero in on
while focusing on network safety initiative. The paper considers the
key ideas and terms of network safety and presents the actual world
and the digital world system. The paper alludes to a framework model
of a general public and utilizations that model to investigate the
aftereffects of two restricted media reviews about digital related paper
articles. The media overviews demonstrate a solid need to sort out
the digital world. Network safety alludes to the assemblage of
advances, cycles, and practices intended to ensure networks
Data mining is the way toward examining information according to
alternate points of view and summing up it into helpful data,
which can be utilized to expand income, reduces expenses, or
both. The real information mining task is the programmed or
self-loader investigation of enormous amounts of information
to remove beforehand obscure, intriguing examples, for example,
gatherings of information records (bunch examination), surprising
records (oddity recognition), and conditions (affiliation rule mining).
Data technology and software engineering diaries, sensor
networks and data communications diaries,
Sight and sound is a type of correspondence that joins distinctive
substance structures like content, sound, pictures, liveliness, or video
into a solitary show, rather than customary broad communications,
like written word or sound chronicles. Famous instances of interactive
media incorporate video web recordings, sound slideshows and
animated videos. Multimedia can be recorded for playback on PCs,
workstations, cell phones, and other electronic gadgets, either on
request or progressively (streaming). In the early long stretches of
sight and sound, the expression "rich media" was inseparable from
intuitive mixed media. Over the long run. Improved degrees of
intuitiveness are made conceivable by joining different types of
media content. Online media is progressively becoming item situated
and information driven, empowering applications with community
end-client advancement and personalization on different types of
content after some time. Instances of these reach from numerous
types of content on Web destinations like photograph displays with
the two (pictures) and title (text) client refreshed
Nathaniel F. Watson
N. Eva Wu
Jesús M. Paniagua
Dipty Tripathi
Zheng Yan*
Fei Yuan*
Antoine Peris *
Rafael Souza*
DOI: 10.37421/2167-0919.2022.11.308
DOI: 10.37421/2167-0919.2022.11.309
DOI: 10.37421/2167-0919.2022.11.312
DOI: 10.37421/2167-0919.2022.11.311
DOI: 10.37421/2167-0919.2022.11.310
DOI: 10.37421/2167-0919.2023.12.367
DOI: 10.37421/2167-0919.2023.12.365
DOI: 10.37421/2167-0919.2023.12.366
DOI: 10.37421/2167-0919.2023.12.363
DOI: 10.37421/2167-0919.2023.12.364
Deep fakes have become a major concern due to their potential impact on various fields such as politics, entertainment, and security. With the help of deep learning techniques, even non-experts can now create convincing and realistic fake videos or images that can be used for both benign and malicious purposes. One of the key issues with deep fakes is that they can be used to manipulate people's perceptions of reality by creating fake videos or images of individuals saying or doing things that they never did. This has serious implications for politics and elections, where deep fakes could be used to sway public opinion and affect the outcome of elections. Another concern is that deep fakes can be used to create fake identities or impersonate individuals, which can lead to identity theft, fraud, and other malicious activities. In addition, deep fakes can be used to manipulate or deceive facial recognition systems, which are used in various applications such as security and surveillance.
DOI: 10.37421/2167-0919.2024.13.413
Antennas are essential components in wireless communication systems, playing a crucial role in determining system performance and range. To achieve optimal performance, various antenna optimization techniques have been developed, aiming to enhance key parameters such as gain, efficiency, bandwidth and radiation pattern. This article provides an overview of popular antenna optimization techniques, including design optimization, array techniques and advanced materials utilization. Through these techniques, engineers can achieve improved performance and extended range in wireless communication systems, addressing the ever-growing demands for higher data rates, increased reliability and wider coverage.
DOI: 10.37421/2167-0919.2024.13.414
With the continuous evolution of technology, the demand for faster and more reliable connectivity is ever-growing. Antenna design plays a crucial role in meeting these demands, especially as we transition into the era of next-generation technologies such as 5G, Internet of Things (IoT) and beyond. This article explores the importance of future-proofing connectivity through advanced antenna design, highlighting key considerations, challenges and emerging trends in this field. By addressing these factors, engineers can ensure that antenna systems are capable of supporting the evolving landscape of communication technologies, ultimately enabling seamless connectivity for the future.
DOI: 10.37421/2167-0919.2024.13.415
Wireless networking has revolutionized the way we communicate and connect in the modern world. With its convenience and flexibility, wireless networking has become an integral part of our daily lives, enabling seamless connectivity across various devices and locations. However, it also presents challenges such as security concerns and reliability issues. This article explores the advantages, challenges, and future trends of wireless networking, shedding light on its impact on society and technology.
DOI: 10.37421/2167-0919.2024.13.416
Artificial Intelligence (AI) has revolutionized various industries and its impact on computer networks is profound. As the backbone of modern communication systems, computer networks face challenges such as congestion, security threats and resource allocation inefficiencies. AI techniques, including machine learning and deep learning, offer solutions to these challenges by optimizing network performance, enhancing security measures and automating management tasks. This article explores the role of AI in optimizing computer networks, highlighting key applications, benefits and future prospects.
DOI: 10.37421/2167-0919.2024.13.417
DOI: 10.37421/2167-0919.2024.13.418
DOI: 10.37421/2167-0919.2024.13.419
DOI: 10.37421/2167-0919.2024.13.420
DOI: 10.37421/2167-0919.2024.13.421
DOI: 10.37421/2167-0919.2024.13.422
Telecommunications System & Management received 109 citations as per Google Scholar report
Spanish 
Chinese 
Russian 
German 
French 
Japanese 
Portuguese 
Hindi 