Reviewerly/exHarmony huggingface dataset:
Improve Firefly Heuristic Optimization Scheme for Web based Information Retrieval

The main goal of web Information Retrieval (IR) is to find highly relevant data for Internet users. Nowadays, huge billions of websites are populated day by day. The information growth is tremendous which is unbelievable. The current Information Retrieval (IR) system brings out very inadequate results because it is a keyword-based technique. The current IR system tools designed are very less capable to match exact document to the net users from the large web repository. Information ambiguity leads to poor information interconnection. It is impossible to handle many users and content to ensure trust at all levels. Hence to overcome these issues, in this novel proposes an Improve Firefly Heuristic Optimization Scheme (IFHOS) algorithm is used to identify the relevant document. The proposed first stage is pre-processing to remove stop words and tokenization etc. Then identify the web user interest using Density based Search Importance Rate (DSIR) technique. Later the proposed IFHOS technique identifies the appropriate information document from a user search. Thus, the proposed method achieves efficient performance than other comparative methods.
Cryptography based Network Security Analysis using Secure Hashed Identity Message Authentication

Network security, made at low cost, and the easily made system has won the prestige compared to wireless networks. Networks are widely used by laptop users in corporate and educational contexts. Secure communication is a challenge in networks. Limited memory resources and power constraints, and complex many key security mechanisms may not be compatible with resources. Network security is key management because it is a complex task of its controlled nature. However, some fundamental drawbacks among wireless storage media are that networks are highly vulnerable to encryption attacks. Therefore, it is very important to protect the data while transferring over the network. Proxy delays in Proxy Re-Encryption (PRE) programming and retrieval Proximity is a significant computational load unloading problem caused by the Lightweight Symmetric Asymmetric Encryption Algorithm (LSAE), and the use of asymmetric encryption of the user's decryption due to the operation and recalculation of the data owner's encryption. In order to resolve these issues, we propose a Secure Hashed Identity Message Authentication (SHIMA) which is used to verify information integrity and authentication. The estimated cost of re-encryption processing caused by network security is very efficient. The SHIMA algorithm can reduce end-to-end encryption and decryption provided by end-user's resource-controlled equipment. Safety and performance research is conducted, and the results show that our method is time-consuming and reduce delay performance safe and efficient. At the same time, the rating has found times the higher performance on these network clients and network servers. Based on this rating, it is recommended that this work be achieved and implemented in networking.
An Improved Industrial Analysis of Electrical Equipment using Deep Learning based Approach

In general some if the modern electrical equipments are used to tighten and loosen screws. Its ends should be slightly flattened to fit into the grooves at the top of the screw. It is available in 4″, 6″, 8″, 9″ and 12″ lengths. We name it according to its length. Its handle is made of wood or plastic. Plastic handles look attractive but do not last as long as wooden handles. Its handle is made of plastic. Used for tightening and loosening small screws in connectors, bearings, fuses, etc. Maximum length is 4″ or 5″. It is the most important tool for electrical workers. It will be at the level of connecting The handle is made of plastic. It is arranged in the form of an inductance cylinder with a resistor, a neon lamp and a coil connected in series. A plug (Clip) on the probe head with a resistor, neon lamp, coil arc and wire are connected in series with each other. Place this probe on a point where there is current and if you put your finger on the plug on the probe head, the neon lamp in the middle will light up. We know that if the light is not lit then there is no current in the point.
Application of FLP Model in Economic Management

With the rapid development of social, political and economic, the number of natural and man-made disasters in China has increased year by year, the scope of influence has been expanding, and the losses caused by disasters have gradually increased. It is particularly important to carry out disaster prevention and control and reduce disaster losses, which is an important guarantee for social harmony and stability and sustainable economic development. FLP model was developed by economist Robert solo. The basic idea of this model is that the production function of an enterprise can be expressed as a linear combination of capital and labor input. In other words, each unit of output can be produced by one unit of capital or two units of labor. This means that in order to produce any specific quantity of products, you need capital and labor inputs (i.e., input-output tables). Resource management is a key issue of cloud computing. It enables the cluster of cloud computing data centers to be treated as a single resource to ensure that resources can be reasonably allocated and used, so as to ultimately achieve the goal of resource sharing and provide efficient and convenient services for cloud users. With the widespread use of cloud computing technology, the security problem of resource management has become increasingly prominent and has become an important problem to be solved.
The Smart Computation of Pollution Management in Industrial Environments using Artificial Intelligence based Neural Networking Model

Environmental pollution started the day we changed to a lifestyle in harmony with nature. Market development is gradually self-requirement satisfaction, barter system, production-oriented development, sales-oriented development, market-oriented development, and now consumer-oriented production has become an evolution. If scientific development was the main factor for this, this society would not have developed to this extent today. At the same time, when the basic needs of land, water, air and air lose their self- sufficiency, living beings have to experience various hardships. If this situation continues, there will be a situation where the next generation will know their ancestors, rare species of animals and plants only in history. Man has destroyed forests and hunted wild animals for pride and selfish needs. Society and people are interdependent. The development of science combined with nature is the only powerful weapon that will carry this community for many centuries.
Gradient Kernelized Cat Boost Pre-Emptive Traffic Aware Data Distribution in Cloud Computing

Cloud provides a distributed computing flexible on-demand data services for the individual person and organizations to store and access their data anywhere and anytime through the internet without user. Every day a large volume of data is generated from different sources. The large volume of data occupies excessive bandwidth and it becomes directed to network traffic. The traditional processing tools are unable to solve effectively for network traffic during the large volume of data transmission. Therefore, a novel technique called Gradient kernelized Cat Boost Priority Pre-emptive traffic aware data distribution (GECOPP) method is introduced for minimizing the traffic occurrence level in the cloud environment during the large volume of data distribution. First, the server receives the number of user-requested data from the user. Then the GECOPP method classifies the user-requested data into an emergency or normal based on different resource factors such as energy, bandwidth, arrival data size, and session time by using Nesterov Gradient kernelized Cat Boost classifier. The task assigner stores the classified data in the priority queue to minimize the traffic occurrence level with lesser space complexity. Finally, a pre-emptive approach is employed for distributing the data to multiple data centers at different locations hence it minimizing the traffic occurrence level in the cloud environment. Experimental evaluation of the GECOPP Method is carried out on the different factors such as classification accuracy, space complexity, makespan, and throughput with respect to the number of data and number of cloud users. The observed results indicate that the GECOPP technique offers an efficient solution in terms of achieving higher accuracy, and throughput, and minimizing the makespan as well as space complexity than the conventional techniques.
An Improved Fuzzy Logic Method for Calculating Humidity in Workplaces

Generally very dry air dehydrates the body, drying out the mucous membranes. Dryness of the nasal mucosa is the cause of frequent colds. The skin is dry, inelastic, metabolism is disturbed. People often experience nasal congestion, difficulty breathing, sleep disturbances, anxiety and irritability. Dry air in the apartment contributes to the spread of dust, in which various microorganisms, as well as dust mites, multiply. The leaves of indoor plants dry up and fall off. Wooden furniture, parquet is dry and covered with cracks. The same applies to musical instruments made of wood: they are constantly out of tune and gradually deteriorate. Excess and lack of moisture in the air is dangerous for the health and condition of the apartment. For a person to be comfortable in a room, the air humidity should be between 30 and 60%. Comfortable air humidity in the apartment during the cold season is about 30-45%. In the warm season, this position is not difficult to maintain. However, in winter, when batteries and other heating devices are running, this can be very difficult. At this time of the year, the percentage of water in the air sometimes drops to 20% and sometimes to 15%, which is completely unacceptable.
A Tumor Classification Algorithm Utilizing Extreme Gradient Boosting

Breast cancer is among the most infectious ailments impacting early part women in countries like India. The severe booster algorithm “Maximize the effectiveness” classifier was used in the proposed solution to automate the medical sector in order to produce an efficient breast cancer detector. Traditional methods for machine learning have all produced models with serious bias or little variance. As a result, the suggested technique used a variety of evaluation markers to evaluate the model. Optimization has a significant impact on machine learning. The devised approach used hierarchical models throughout the svm classifier to address this issue. Then, the suggested framework is contrasted with current models. The early identification of breast cancer is the main goal of the proposed theory.
An Improved Eco-friendly Practices in Modern Agricultural Department using Agro Deep Learning Model

In general, various small and large hand tools are used for electrical work and if they are used in the best way, our work will be completed efficiently and quickly, and the tools will be used for a long time without repair. It is an important tool for electrical workers. It is used for cutting wires, twisting and winding them. Its handles are covered with rubber so it can be used to work even on live wires. We also use it to remove and replace screws. Used to loosen and fit screws at short intervals. It is mostly used to repair electronic devices like radio box amplifiers. It is used to remove the insulation over electrical conductors. It is also used to clean the wires from which the fuse has been removed. It should have a wooden or plastic handle. It should be 4” or 5” long enough to fold it up and put it in a shirt pocket..
Dimensional Reduction Method based on Big Data Techniques for Large Scale Data

In the age of big data, analysing complex and enormous amounts of data costs time and money and might result in mistakes and misunderstandings. Therefore, bad inference and decision-making, and occasionally irreversible and catastrophic occurrences, could result from imprecise and mistaken reasoning. On the other hand, effective administration and use of priceless data can considerably expand knowledge and lower costs through preventive measures. Time-to-event and survival data analysis are the cornerstones of risk assessment in this area and play a crucial part in estimating the likelihood of numerous occurrences, including the failure of a device or component. As a result, before beginning any analytic process, it is desirable to apply appropriate approaches to effectively reduce large-scale, huge, and complex data, especially in terms of variables. To minimize the abovementioned decision-making challenges and to make survival data and failure analysis easier, we offer an applied data reduction strategy in this research that enables us to obtain appropriate multiclass classification in large dimensionality data and huge datasets. In order to estimate the risk in addition make decisions in the context of a complex analysis of big volume in survival analysis, this research presents an applied analysis data and attribute selection methodology.
Scoring Impressions and Associations for Improved Concept Map Excavating from Dominion Text Demonstration

A concept map is a graphical tool used to organize and represent knowledge or information visually. It consists of a collection of concepts or ideas that are linked together by relationships or connections to form a structured and hierarchical representation of a subject or topic. A concept map can be used to show the relationships between ideas, identify patterns, and clarify complex information. It is commonly used in education, research, and problem-solving to help individuals better understand and visualize information. The graph's entropy serves as a useful indicator of how accurately it represents the document. This study explains how to construct a concept map from the document's sentences by using a tree-based convolution neural network to extract key concepts. The use of latent relations based on a shared word embedding representation of concepts enables the removal of duplication and the efficient organization of concepts. This leads to a more precise, concise, and unambiguous concept map. The paper also describes the modifications made to the concept map and the additional data incorporated into it. By utilizing the latent semantic structure and hierarchical linkage of ideas and by adding significant information to the concepts themselves, the information content of the concept map is improved.
Intelligent English Recognition System Based on Computer Image Algorithm

With the acceleration of the global informatization process, improving the efficiency of information processing has become a top priority. The development of science and technology has made the information processing methods advance by leaps and bounds, and the trend of “electronic information” attracts the participation of all walks of life. This paper aims to study the intelligent English recognition system based on computer image algorithm. This paper firstly gives a brief overview of the current situation and development of intelligent English recognition technology. In view of the current demand for accurate and fast English recognition on the market, the system can automatically distinguish and recognize input images and extract image information. This paper first describes the proposed system from three parts: image preprocessing, document image retrieval, preliminary analysis, and information extraction. In this case, angle detection is faster and can recognize English words more accurately. Experiments show that the combined feature extraction algorithm in this paper has an effective extraction accuracy rate of 80% or a lie, and the optimal learning initial rate is 0.01.
Damage Detection of Civil Structures Based on Quantum Behavior Particle Swarm Optimization Algorithm

In the field of civil engineering, the design and manufacture of structural materials is a very important part of engineering construction. With the continuous development of China's construction industry, the requirements for the quality of civil structural components are getting higher and higher. Therefore, in order to detect the damage of civil structures in time, this paper proposes a quantum behavior particle swarm optimization algorithm to improve the detection capability of civil structures. In this paper, a neural network adaptive learning technology and wavelet transform threshold clustering algorithm based on Monte Carlo simulation, random sequence prediction and support vector machine regression are obtained by combining statistical description method and experimental method. The experimental results show that the minimum error of wavelet detection can reach 2.4%, indicating that the algorithm has certain advantages in civil engineering results detection.
An Improved Method for Deep Learning-based Analysis of Environmental Issues and Remote Sensing

the term ecosystem describes the natural environment that includes the total living and non-living elements around us. Environmental pollution is the environmental damage caused to the functioning of the world and the lifestyle of living beings due to inconvenience, instability, irregularity, harmful activities etc. Contamination can be caused by chemical substances or by noise, heat, and light energy. In this paper, an improved method has proposed for deep learning-based analysis of environmental issues and remote sensing. This pollution creates harm to the biological nature of the environment. Global Positioning Module (GPRS) receivers transmit position, echo data and precision clocks carried by satellites to all stations. Since space has three dimensions, the smallest error in measuring the signals from the satellites with the 3 satellites namely position, time and velocity becomes huge even with clock correction. This is because the speed of the satellite during miscalculation can affect the correct positioning. So receivers can operate using 4 or more satellites without problems with location and time.
A Fast Alignment Algorithm for Posture Measurement of Autistic Children in Swimming using Computer Vision

Motor control ability is directly related to the stereotype and repetitive behavior of autistic children. Posture control refers to the control of the body's center of gravity, which is the most basic control ability. In order to solve the shortcomings of the existing research on the fast alignment algorithm of autistic children's swimming motion attitude measurement, this paper discusses the basic methods of attitude description, strap down inertial navigation system and binocular camera calibration algorithm, and proposes a multi-information aided strapdown inertial navigation system swimming motion attitude measurement alignment based on neural network. The specific experiment shows that the minimum j oint position error estimation of the proposed method reaches 0.0071 in the 200 second swimming posture alignment process. Therefore, the advantages of this method in rapid alignment of autistic children's swimming posture measurement are verified.
Design of Financial Data Security Storage System for Public Institutions Based on Particle Swarm Optimization Algorithm

The safe storage of financial data of public institutions is a systematic project, which involves the internal management and control of institutions and relates to the development of national economy. Due to the continuous improvement of social informatization, rapid progress of science and technology, and rapid updating of new technologies, the traditional management model of public institutions in China has been unable to adapt to the requirements of the times for modern information processing. In this case, a new and efficient intelligent system with strong practicability and operability, which can meet the financial data security requirements of public institutions, is needed to solve this problem, so as to ensure the orderly and effective completion of all internal work of the unit and achieve the maximum benefit of resource utilization. Therefore, this paper first describes the importance of safe storage of financial data, then describes the design principles of storage system, and then studies the application process of particle swarm optimization algorithm. Based on this, a safe storage system is designed and its performance is tested. The test results show that the financial data security storage system of public institutions based on particle swarm optimization algorithm runs fast, the security data processing time is about 8 seconds, the system has a high fault tolerance and is compatible with hardware. This allows users to use the system safely and painlessly.
Privacy Preserving Q-Pragmatic-Sensitive Feature Analysis based Blockchain Security Using Provable Partition Fold Padding Encryption

Information security is important factor in decentralized cloud environment because of sharing personalized information among the Internet need privacy preserving to protect the data. In recent days, the Blockchain security is a developing technique to protect the healthcare sensitive information in outsourced cloud environment. But increasing malicious activity and cryptography ley leakages leads security failures. Most security considering the privacy information as common to protect information publicly. This creates a chance of sensitive information attacks to attain the data. To resolve this security problem, we propose a privacy preserving Q-Pragmatic-Sensitive Feature Analysis (PP-QPSFA) based Blockchain security using provable partition fold padding encryption (PP-FPE) to improve the healthcare transaction security. Initially the Term Frequency Behavior Analysis (TFBA) estimate the privacy concerns of user accessibility. The Q-pragmatic-sensitive feature analysis (PP-QPSFA) is attained to find the sensitive privacy feature terms. Then to make secure data using Provable Partition Fold Padding Encryption (PP-FPE) and verified through Role Based Access Verification (RBA V) to make authentication. The proposed system proves higher security concern in blockchain healthcare to improve the privacy preserving in cloud environment.
An Improved Analysis of Magnetic Force Role in the Modern Electronics Circuits using Improved Fuzzy Logic Control

Magnetic force plays a very important role in the field of electronics. Generators, motors, electrical appliances and transformers operate on the basis of magnetic energy. Also important devices like television sets, radios, airplanes, etc. have devices that work based on magnetic energy. Magnetism and their applications and how much it is used in electrical equipment. Magnetic source materials are classified according to their permeability. Any magnetic material whose susceptibility to magnetic lines is less than one is called diamagnetic material. These are magnetically repelled. Any magnetic material whose susceptibility to magnetic lines is greater than one is called paramagnetic material. Paramagnetic materials are only slightly attracted by a magnet. Any magnetic material whose ability to accept magnetic lines within itself is more than thousands is called ferromagnetic material. Such raw materials are highly attracted by the magnet.