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Development of Smart Chabot in the Field of Trading using Smart Artificial intelligence Informal technology

Development of Smart Chabot in the Field of Trading using Sm...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Nachaat Mohamed Shalini Ninoria Chitra Krishnan Suresh Babu Rajasekaran Badria Sulaiman Alfurhood Devesh Pratap Singh Rabdan Academy UAE Homeland Security Universiti Sains Malaysia Abu Dhabi College of CSIT Teer Thanker Mahaveer University Moradabad Uttar Pradesh India Symbiosis Center for Management Studies Noida Symbiosis International Deemed University Pune India Group Product Manager NVIDIA Corporation United State Department of Computer Sciences College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Saudi Arabia Department of CSE Graphic Era Deemed to be University Dehradun Uttarakhand India

Artificial intelligence (AI) and Natural Language Processing (NLP) calculations are being used to create a sophisticated system known as a chatbot. It answers questions from customers and successfully communicates with them. Discourse/discussion administrators are most frequently used by organizations, official affiliations, and fictitious affiliations. Organizations that deal with money, like banks, charge card companies, associations with online retailers, and new businesses, frequently disseminate them. These conversational specialists work for a variety of companies, from huge associations to tiny, new businesses. There are chatbot advancement frameworks on the market that are both interface-and code-based. They do their best to avoid being nimble and adaptable when starting sincere conversations though. There are several well-known intelligent personal assistants, including "Amazon's Alexa, Google's Google Assistant, and Microsoft's Cortana." These experts only have a few components. The current chatbots don't deliver satisfactory results, regardless of whether they were developed using recovery-based strategies, rule-based tactics, or basic AI computations. This article offers a basic audit of chatbots and carefully examines and discusses the available strategies.

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Network Traffic Analysis and Anomaly detection

Network Traffic Analysis and Anomaly Detection

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Smart Generation Computing, Communication and Networking (SMART GENCON), international conference on

作者： Sanchi Agarwal Ayon Somaddar Paritosh Harit Divya Thakur Akshita Sharma Krishna Kaushal Singh CSE-Information Security Chandigarh University Mohali CSE-AIML Chandigarh University Mohali Apex Institute of Technology-CSE Chandigarh University Mohali

In the ever-changing world of network security, they've developed a cutting-edge intelligent network traffic analysis (ITA) system that places a premium on understanding the intricacies of each individual network packet. This cutting-edge approach allows the system to conduct deep-dive analyses of network data, making it highly sensitive to even the most subtle anomalies and potential network security breaches. Using cutting-edge ML techniques, such as convolutional neural networks (CNNs), Random Forest, Decision Tree, and LSTMs, their system shows remarkable flexibility in responding to the ever-evolving cyber threat landscape, uncovering complex patterns and relationships within the network data. This system is distinguished by its high level of accuracy achieved through packet level analysis and stringent pre-processing, as well as its utilization of high-quality packet-level data from tools like the Cisco packet Tracer platform or Wireshark's packet data, which helps improve network security by providing faster response times and fewer false positives. The easy integration of visualization tools also improves transparency, allowing network administrators and security professionals to make informed decisions and improve incident response efforts. The system also provides valuable insights into attacks, port scans, malicious activity, and unauthorized access attempts. In conclusion, their project stands as a beacon of innovation and protection in an era where network security reigns supreme, safeguarding networks against the ever-evolving cyber threat landscape.

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Channel Estimation Based on Improved Compressed Sensing Algorithm with Reconfigurable intelligence Surface Assistance

Channel Estimation Based on Improved Compressed Sensing Algo...

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Computer Science, Electronic Information Engineering and Intelligent Control technology (CEI), IEEE international conference on

作者： Mengmeng Chen Hu Liu Xinrui Yang School of Railway Transportation Shanghai Institute of Technology Shanghai China

ISBN: (数字)9798350372052

ISBN: (纸本)9798350372069

Aiming at the problem of cascade channel estimation for Reconfigurable Intelligent Surface (RIS) assisted wireless communication systems, an improved Compressive Sensing (CS) algorithm based on double-structure sparsity is proposed in this paper. Firstly, the double-structure sparsity characteristic of cascade channel in RIS assisted wireless communication system is studied. Then, aiming at the problem that base station (BS) and users cannot obtain channel sparsity before channel estimation, an improved compressed sensing algorithm is proposed. Simulation results show that the algorithm proposed in this paper can solve the problem of unknown channel sparsity. It is more practical than other compressed sensing algorithms without relying on complete prior information.

关键词： Wireless communication Computer science Surface waves Simulation Channel estimation Reconfigurable intelligent surfaces Sensors

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Facial detection and Recognition-based Smart system on Feature Extraction using Raspberry Pi

Facial Detection and Recognition-based Smart System on Featu...

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international conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： M Pavithra A Murugesan K Saranya T Srihari K Mohanraj M Parimala Devi Department of Electronics and Communication Engineering Kongu Engineering College Erode India Department of Electrical and Electronics Engineering K S R Institute for Engineering and Technology Tiruchengode India Department of Electrical and Electronics Engineering Dr. Mahalingam College of Engineering and Technology India Department of Electrical and Electronics Engineering Saveetha School of Engineering SIMATs University Saveetha Nagar Thandalam Chennai India Department of Electrical and Electronics Engineering Kongunadu College of Engineering and Technology Thottiyam Trichy India Department of Electronics and Communication Engineering Velalar College of Engineering and Technology Erode India

Combining Linear Discriminant Analysis (LDA) and Primary Component Analysis (PCA) feature extraction techniques to improve the efficacy of the face-centered real-time review approach. The measurement functions used to extract PCA and LDA values must be combined to get scores expressing the degree of similarity. When the total of the values generated from both functions is used, the scores are identical. The combination extractor has the capacity to enhance specific qualities in scanned facial photographs. The Euclidean distance between a subset of the test shots and the templates must be computed in order to determine the template image that most closely resembles the test shots. A comparative study of the user’s facial traits in relation to a database-stored reference image can be used to authenticate an individual’s identity. Based on the assessment of the eleven-user image, it appears that the combination extractor outperforms the single extraction feature. On average, the performance of the proposed methodology outperforms that of using a single extractor. If the performance of a system is proven to be insufficient, one alternative course of action is to implement a facial identification instrument. To achieve better results, it is critical to increase the adaptability and applicability of the time allotted for problem-solving activities.

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Building an Emotion detection system in Python Using Multi-Layer Perceptrons for Speech Analysis

Building an Emotion Detection System in Python Using Multi-L...

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Regula Srilakshmi Vidya Kamma Shilpa Choudhary Sandeep Kumar Munish Kumar Computer Science and Engineering Neil Gogte Institute of Technology Hyderabad India Department of Computer Science and Engineering (AI&ML) Neil Gogte Institute of Technology Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India

Human speech emotion recognition analyses a speaker's speech to determine their emotional state. Included are several applications in psychology, medicine, and human-computer interaction. Automated speech emotion identification techniques have been widely used in recent years thanks to machine learning algorithms. A number of speech processing libraries are available in Python, a popular programming language for system research. This focuses on creating a Python-based full speech popularity system that uses MLP classifiers. The goal of applications in psychology, healthcare, and human-computer interaction is to routinely discern the emotional state of a speaker based on their speech. With the aid of a multi-layer perceptron (MLP) classifier, we intend to improve a speech emotion reputation device written in Python. The proposed system will derive features from speech recordings, including mal-frequency cepstral coefficients (MFCCs) and prosodic functions. We will train and evaluate our model using an open-supply speech emotion popularity dataset, including RAVDESS. The extracted features may be supplied to an MLP classifier that has been trained using the classified emotional speech statistics dataset. Once mastered, the device can accurately categorise the speaker's emotional condition into a variety of categories, including happiness, despair, anger, and fear. Finally, we can evaluate the performance of our model using existing techniques on a distinct check out dataset.

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An Evaluation and Implementation of the Effectiveness of Cloud Security as a Service in IoT Security

An Evaluation and Implementation of the Effectiveness of Clo...

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Smart Generation Computing, Communication and Networking (SMART GENCON), international conference on

作者： Benila A Swapnil K. Shinde Anurag Shakya Jambi Ratna Raja Kumar Dipannita Mondal S. Hemelatha Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department of Commerce Mangalayatan University Aligarh India Department of Computer Engineering Genba Sopanrao Moze College of Engineering Pune Maharashtra India Artificial Intelligence And Data Science Department Dr. D.Y.Patil College Of Engineering And Innovation Talegaon Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore

The net of factors (IoT) has seen a speedy growth in current years, with more devices being linked to the internet daily. Those devices range from family home equipment to business systems, and their lavish use has made them appealing targets for cyber assaults. It poses a tremendous task for safety experts, as more than conventional safety features are frequently needed to guard IoT gadgets. To cope with this issue, many agencies are turning to Cloud protection as a carrier (Case) solution to beautify their IoT security. CSaaS is a cloud-primarily based protection solution that provides more than a few protection services, with chance detection, facts encryption, and the right of entry to manipulate IoT gadgets from cyber threats. It offers a scalable and value-powerful way to relax big numbers of IoT gadgets and can doubtlessly overcome the constraints of traditional security features. But, the effectiveness of Cases in IoT protection continues to be a topic of discussion. This paper aims to assess and implement the effectiveness of Cases in IoT security. It's going to begin by way of imparting an outline of the contemporary nation of IoT security and the challenges confronted in securing those devices. It will then discuss the concept of Cases and its capability benefits for IoT security. The paper may also evaluate current literature on Cases and their applicability to IoT protection

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An Evaluation of the Impact of AI on Data Access Optimization Performance

An Evaluation of the Impact of AI on Data Access Optimizatio...

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Smart Generation Computing, Communication and Networking (SMART GENCON), international conference on

作者： Vijaykumar R. Ghule Ankur Kumar Agrawal Jambi Ratna Raja Kumar Dipannita Mondal S. Hemelatha Preetha. M Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department Of Management Mangalayatan University Aligarh India Department of Computer Engineering Genba Sopanrao Moze College of Engineering Balewadi Pune Maharashtra India Artificial Intelligence And Data Science Department Dr. D.Y. Patil College Of Engineering And Innovation Talegaon Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai

The emergence of synthetic intelligence (AI) has substantially revolutionized the way records is accessed and optimized. With AI-powered equipment and algorithms, groups are capable of efficiently method and manipulate large amounts of data, main to upgrades in overall performance and decision-making. This paper pursuits to evaluate the impact of AI on statistics get entry to optimization performance, discussing both the potential blessings and challenges that include its integration. One of the major blessings of using AI for information get entry to optimization is its capability to automate and streamline obligations. AI-powered structures can analyze and categorize massive datasets at a quicker and extra correct rate than human beings, liberating up time and assets for different obligations. This results in stronger efficiency and productivity in statistics control methods. Further, AI also can enhance the accuracy and best of facts access. thru system learning and predictive analytics, AI structures can identify styles and correlations in information, providing treasured insights for decision-making. This now not simplest improves the speed at which records is accessed, however also the reliability and relevance of the data. However, there are also demanding situations associated with the combination of AI in facts get admission to optimization. one of the fundamental worries is the potential bias in algorithms, which can result in erroneous or discriminatory selection-making. it's far vital for groups to carefully display and review the algorithms utilized by AI structures.

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Development of a sleep monitoring system using cloud computing and big data techniques

Development of a sleep monitoring system using cloud computi...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Sonia Duggal Sanjay Sood Mahipal Singh Komuravelly Sudheer Kumar Mohit Tiwari S. Veeresh Kumar Department of Computer Applications MRIIRS Faridabad Haryana India Lovely Professional University Phagwara Punjab India Uttaranchal Institute of Technology Uttaranchal University Dehradun India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi India Department of IT St. Martin's Engineering College Secunderabad Telangana India

Among the most significant sleep concerns is obstructive apnea syndrome in sleep (OASS) which harms the quality of life. A few effects of OASS include cognitive decline, impaired psychiatric function, behavior problems, and personality ailments. Real-time monitoring of this condition is thus essential for clinical services. There seem to be numerous OASS-detecting techniques. Hence, the present experiment provides a new system both for detecting and assisting therapies of OASS in elderly individuals. This design was developed for tracking a variety of variables including the surroundings where a person goes to sleep, their sleeping quality, physical activity levels, and physiological variables by utilizing open data from smarter city systems. 2 sorts of processing are performed by the suggested method. In the instance of a crisis, real-time notices can be sent to those in charge of caring for the old aged owing to the rule-relied pre-processing technique. This method fundamentally relies on a fog computing strategy applied in a smarter device functioning at networking edges which also provides enhanced interoperable ability services, including technological, semantic, and syntactic capabilities. Big data devices on cloud computing are utilized in this process. The effectiveness of the system has also been assessed given latencies. The outcomes obtained indisputably show that pre-processing of data at networking edges increases system effectiveness.

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A Deep Learning Neural Network-based system for Food Recognition and Calorie Estimation

A Deep Learning Neural Network-based System for Food Recogni...

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international conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： Pallavi Yarde Dibyhash Bordoloi Rahul Mohan Chavan Vipul Vekariya Harshal Patil L Natrayan Department of Technology & Management Balaji Institute of Technology & Management Sri Balaji University Pune Maharashtra India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Physical Education and Sports Kholeshwar College Ambajogai Maharashtra India Department of Computer Science and Engineering Parul Institute of Engineering and Technology Parul University Post Limda Waghodia Gujarat India Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

The global increase in health awareness reflects a rising desire among individuals to pursue healthy and normal lives. Obesity has emerged as an important health issue in the modern world due to its rapid pace. Obesity is defined by medical authorities as having a Body Mass Index (BMI) of more than 30 kg/m2, which can lead to a variety of health difficulties like liver failure, cholesterol, respiratory disorders, diabetes, cardiac complications, and, in serious cases, cancer. Maintaining a diet rich in nutrients and low in calories is critical in the fight against obesity. Individuals, on the other hand, frequently struggle with food decisions. In response to this difficulty, this research presents a system that offers calorific calculation followed by food recognition to aid in preventing obesity. The goal of this research is to identify types of food and estimate their calorific values. For food recognition, a novel Attention-based Dilated Convolutional Neural Network Logistic Regression (A-CNNLR) model is introduced and compared to a traditional CNN model. The proposed ACNNLR model demonstrates a recognition rate of 99%, surpassing the CNN model’s 97%. The study determines the calories in the food according to their volume using data from the ECUST Food Dataset. This study provides a novel approach to the fight against obesity by using sophisticated algorithms for precise food detection and calorie estimation.

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Enhancing FTP Security Through Ensemble Learning-based Brute Force Attack detection

Enhancing FTP Security Through Ensemble Learning-based Brute...

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international conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： Aadil Khan Ishu Sharma Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

A common network environment, File Transfer Protocol (FTP), efficiently transfers data between clients and servers on a computer network. FTP, a client-server protocol, simplifies file administration by uploading and downloading files. FTP allows clients to request and transmit files to servers, facilitating smooth data interchange. FTP is simple but sends data in plain text, hence secure variations like FTPS and SFTP were developed to solve encryption and security. Network risks must be detected early to avoid harm and illegal access. Finding security risks early on lets you take quick and preventative steps, which lowers the chances of data breaches, system compromises, and network interruptions. Early detection helps prevent malicious activity from escalating, protecting network integrity and sensitive data. This proactive strategy is essential for network security and resilience against emerging cyber threats. The research work showed that machine learning methods work, especially when it comes to finding brute force attacks on FTP sites. With the help of the CIC_IDS_2018 dataset, the model showed that it was very accurate, showing how well it can spot early risks. It was much easier for the protective system to hold up after its settings were carefully changed. The mathematical models showed the complex behavior, especially the changes in the maximum depth. These results show how useful machine learning can be for making networked systems stronger against certain cyber risks. They also give us important information for creating proactive cybersecurity strategies. Machine learning recognized FTP-bruteforce attacks effectively in the experiment. The algorithm detected and prevented early threats well using the CIC_IDS_2018 dataset.

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