blogs.sql

-- phpMyAdmin SQL Dump
-- version 5.2.1
-- https://www.phpmyadmin.net/
--
-- Host: 127.0.0.1
-- Generation Time: Oct 23, 2023 at 08:14 PM
-- Server version: 10.4.28-MariaDB
-- PHP Version: 8.2.4

SET SQL_MODE = "NO_AUTO_VALUE_ON_ZERO";
START TRANSACTION;
SET time_zone = "+00:00";


/*!40101 SET @OLD_CHARACTER_SET_CLIENT=@@CHARACTER_SET_CLIENT */;
/*!40101 SET @OLD_CHARACTER_SET_RESULTS=@@CHARACTER_SET_RESULTS */;
/*!40101 SET @OLD_COLLATION_CONNECTION=@@COLLATION_CONNECTION */;
/*!40101 SET NAMES utf8mb4 */;

--
-- Database: `blogsdb`
--

-- --------------------------------------------------------

--
-- Table structure for table `blogs`
--

CREATE TABLE `blogs` (
  `Id` int(11) NOT NULL,
  `blogTitle` text DEFAULT NULL,
  `BlogContent` text DEFAULT NULL,
  `ImgURL` text DEFAULT NULL,
  `smallPara` varchar(30) DEFAULT NULL,
  `Authors` varchar(50) DEFAULT NULL
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_general_ci;

--
-- Dumping data for table `blogs`
--

INSERT INTO `blogs` (`Id`, `blogTitle`, `BlogContent`, `ImgURL`, `smallPara`, `Authors`) VALUES
(1, 'Intro to Microprocessor', 'A microprocessor is a computer processor where the data processing logic and control is included on a single integrated circuit (IC), or a small number of ICs. The microprocessor contains the arithmetic, logic, and control circuitry required to perform the functions of a computer\'s central processing unit (CPU). The IC is capable of interpreting and executing program instructions and performing arithmetic operations.[1] The microprocessor is a multipurpose, clock-driven, register-based, digital integrated circuit that accepts binary data as input, processes it according to instructions stored in its memory, and provides results (also in binary form) as output. Microprocessors contain both combinational logic and sequential digital logic, and operate on numbers and symbols represented in the binary number system.\n\nThe integration of a whole CPU onto a single or a few integrated circuits using Very-Large-Scale Integration (VLSI) greatly reduced the cost of processing power. Integrated circuit processors are produced in large numbers by highly automated metal–oxide–semiconductor (MOS) fabrication processes, resulting in a relatively low unit price. Single-chip processors increase reliability because there are fewer electrical connections that could fail. As microprocessor designs improve, the cost of manufacturing a chip (with smaller components built on a semiconductor chip the same size) generally stays the same according to Rock\'s law.\n\nBefore microprocessors, small computers had been built using racks of circuit boards with many medium- and small-scale integrated circuits, typically of TTL type. Microprocessors combined this into one or a few large-scale ICs. While there is disagreement over who deserves credit for the invention of the microprocessor, the first commercially available microprocessor was the Intel 4004, designed by Federico Faggin and introduced in 1971.[2]\n\nContinued increases in microprocessor capacity have since rendered other forms of computers almost completely obsolete (see history of computing hardware), with one or more microprocessors used in everything from the smallest embedded systems and handheld devices to the largest mainframes and supercomputers.\n\nA microprocessor is related but distinct from a system on a chip, microcontroller, and digital signal processor.\nThe complexity of an integrated circuit is bounded by physical limitations on the number of transistors that can be put onto one chip, the number of package terminations that can connect the processor to other parts of the system, the number of interconnections it is possible to make on the chip, and the heat that the chip can dissipate. Advancing technology makes more complex and powerful chips feasible to manufacture.\n\nA minimal hypothetical microprocessor might include only an arithmetic logic unit (ALU), and a control logic section. The ALU performs addition, subtraction, and operations such as AND or OR. Each operation of the ALU sets one or more flags in a status register, which indicate the results of the last operation (zero value, negative number, overflow, or others). The control logic retrieves instruction codes from memory and initiates the sequence of operations required for the ALU to carry out the instruction. A single operation code might affect many individual data paths, registers, and other elements of the processor.\n\nAs integrated circuit technology advanced, it was feasible to manufacture more and more complex processors on a single chip. The size of data objects became larger; allowing more transistors on a chip allowed word sizes to increase from 4- and 8-bit words up to today\'s 64-bit words. Additional features were added to the processor architecture; more on-chip registers sped up programs, and complex instructions could be used to make more compact programs. Floating-point arithmetic, for example, was often not available on 8-bit microprocessors, but had to be carried out in software. Integration of the floating-point unit, first as a separate integrated circuit and then as part of the same microprocessor chip, sped up floating-point calculations.\n\nOccasionally, physical limitations of integrated circuits made such practices as a bit slice approach necessary. Instead of processing all of a long word on one integrated circuit, multiple circuits in parallel processed subsets of each word. While this required extra logic to handle, for example, carry and overflow within each slice, the result was a system that could handle, for example, 32-bit words using integrated circuits with a capacity for only four bits each.\n\nThe ability to put large numbers of transistors on one chip makes it feasible to integrate memory on the same die as the processor. This CPU cache has the advantage of faster access than off-chip memory and increases the processing speed of the system for many applications. Processor clock frequency has increased more rapidly than external memory speed, so cache memory is necessary if the processor is not to be delayed by slower external memory.\n', 'https://pica.zhimg.com/v2-a31d44c598f5e547522a82492e723d90_720w.jpg?source=d16d100b', 'Microprocessor, Brain of every', 'Dr. Shameem'),
(2, 'All About AI and ML', 'Artificial intelligence (AI) is the intelligence of machines or software, as opposed to the intelligence of humans or animals. It is also the field of study in computer science that develops and studies intelligent machines. \"AI\" may also refer to the machines themselves.\n\nAI technology is widely used throughout industry, government and science. Some high-profile applications are: advanced web search engines (e.g., Google Search), recommendation systems (used by YouTube, Amazon, and Netflix), understanding human speech (such as Siri and Alexa), self-driving cars (e.g., Waymo), generative or creative tools (ChatGPT and AI art), and competing at the highest level in strategic games (such as chess and Go).[1]\n\nArtificial intelligence was founded as an academic discipline in 1956.[2] The field went through multiple cycles of optimism[3][4] followed by disappointment and loss of funding,[5][6] but after 2012, when deep learning surpassed all previous AI techniques,[7] there was a vast increase in funding and interest.\n\nThe various sub-fields of AI research are centered around particular goals and the use of particular tools. The traditional goals of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception, and support for robotics.[a] General intelligence (the ability to solve an arbitrary problem) is among the field\'s long-term goals.[8] To solve these problems, AI researchers have adapted and integrated a wide range of problem-solving techniques, including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, operations research, and economics.[b] AI also draws upon psychology, linguistics, philosophy, neuroscience and many other fields.[9]\nEarly researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions.[10] By the late 1980s and 1990s, methods were developed for dealing with uncertain or incomplete information, employing concepts from probability and economics.[11]\n\nMany of these algorithms are insufficient for solving large reasoning problems because they experience a \"combinatorial explosion\": they became exponentially slower as the problems grew larger.[12] Even humans rarely use the step-by-step deduction that early AI research could model. They solve most of their problems using fast, intuitive judgments.[13] Accurate and efficient reasoning is an unsolved problem.\nAn \"agent\" is anything that perceives and takes actions in the world. A rational agent has goals or preferences and takes actions to make them happen.[d][30] In automated planning, the agent has a specific goal.[31] In automated decision making, the agent has preferences – there are some situations it would prefer to be in, and some situations it is trying to avoid. The decision making agent assigns a number to each situation (called the \"utility\") that measures how much the agent prefers it. For each possible action, it can calculate the \"expected utility\": the utility of all possible outcomes of the action, weighted by the probability that the outcome will occur. It can then choose the action with the maximum expected utility.[32]\n\nIn classical planning, the agent knows exactly what the effect of any action will be.[33] In most real-world problems, however, the agent may not be certain about the situation they are in (it is \"unknown\" or \"unobservable\") and it may not know for certain what will happen after each possible action (it is not \"deterministic\"). It must choose an action by making a probabilistic guess and then reassess the situation to see if the action worked.[34] In some problems, the agent\'s preferences may be uncertain, especially if there are other agents or humans involved. These can be learned (e.g., with inverse reinforcement learning) or the agent can seek information to improve its preferences.[35] Information value theory can be used to weigh the value of exploratory or experimental actions.[36] The space of possible future actions and situations is typically intractably large, so the agents must take actions and evaluate situations while being uncertain what the outcome will be.\n\nA Markov decision process has a transition model that describes the probability that a particular action will change the state in a particular way, and a reward function that supplies the utility of each state and the cost of each action. A policy associates a decision with each possible state. The policy could be calculated (e.g. by iteration), be heuristic, or it can be learned.[37]\n\nGame theory describes rational behavior of multiple interacting agents, and is used in AI programs that make decisions that involve other agents.[38]\n\n\n\n', 'https://infopublik.solokkota.go.id/wp-content/uploads/2023/10/63e226d5c74bb.jpg', 'Deep dive into AI and ML', 'Prof. Afeefa'),
(3, 'Introduction to python ', 'Python is a high-level, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation.[32]\n\nPython is dynamically typed and garbage-collected. It supports multiple programming paradigms, including structured (particularly procedural), object-oriented and functional programming. It is often described as a \"batteries included\" language due to its comprehensive standard library.[33][34]\n\nGuido van Rossum began working on Python in the late 1980s as a successor to the ABC programming language and first released it in 1991 as Python 0.9.0.[35] Python 2.0 was released in 2000. Python 3.0, released in 2008, was a major revision not completely backward-compatible with earlier versions. Python 2.7.18, released in 2020, was the last release of Python 2.[36]\n\nPython consistently ranks as one of the most popular programming languages.[37][38][39][40]\nPython was conceived in the late 1980s[41] by Guido van Rossum at Centrum Wiskunde & Informatica (CWI) in the Netherlands as a successor to the ABC programming language, which was inspired by SETL,[42] capable of exception handling and interfacing with the Amoeba operating system.[11] Its implementation began in December 1989.[43] Van Rossum shouldered sole responsibility for the project, as the lead developer, until 12 July 2018, when he announced his \"permanent vacation\" from his responsibilities as Python\'s \"benevolent dictator for life\", a title the Python community bestowed upon him to reflect his long-term commitment as the project\'s chief decision-maker.[44] In January 2019, active Python core developers elected a five-member Steering Council to lead the project.[45][46]\n\nPython 2.0 was released on 16 October 2000, with many major new features such as list comprehensions, cycle-detecting garbage collection, reference counting, and Unicode support.[47] Python 3.0, released on 3 December 2008, with many of its major features backported to Python 2.6.x[48] and 2.7.x. Releases of Python 3 include the 2to3 utility, which automates the translation of Python 2 code to Python 3.[49]\n\nPython 2.7\'s end-of-life was initially set for 2015, then postponed to 2020 out of concern that a large body of existing code could not easily be forward-ported to Python 3.[50][51] No further security patches or other improvements will be released for it.[52][53] Currently only 3.8 and later are supported (2023 security issues were fixed in e.g. 3.7.17, the final 3.7.x release[54]).\n\nIn 2021 (and again twice in 2022), security updates were expedited, since all Python versions were insecure (including 2.7[55]) because of security issues leading to possible remote code execution[56] and web-cache poisoning.[57] In 2022, Python 3.10.4 and 3.9.12 were expedited[58] and 3.8.13, because of many security issues.[59] When Python 3.9.13 was released in May 2022, it was announced that the 3.9 series (joining the older series 3.8 and 3.7) would only receive security fixes in the future.[60] On 7 September 2022, four new releases were made due to a potential denial-of-service attack: 3.10.7, 3.9.14, 3.8.14, and 3.7.14.[61][62]\n\nAs of October 2023, Python 3.12 is the stable release, and 3.12 and 3.11 are the only versions with active (as opposed to just security) support. Notable changes in 3.11 from 3.10 include increased program execution speed and improved error reporting.[63]\n\nPython 3.12 adds syntax (and in fact every Python since at least 3.5 adds some syntax) to the language, the new (soft) keyword type (recent releases have added a lot of typing support e.g. new type union operator in 3.10), and 3.11 for exception handling, and 3.10 the match and case (soft) keywords, for structural pattern matching statements. Python 3.12 also drops outdated modules and functionality, and future versions will too, see below in Development section.\n\nPython 3.11 claims to be between 10 and 60% faster than Python 3.10, and Python 3.12 adds another 5% on top of that. It also has improved error messages, and many other changes.\n\nSince 27 June 2023, Python 3.8 is the oldest supported version of Python (albeit in the \'security support\' phase), due to Python 3.7 reaching end-of-life.[64]\n', 'https://cdn.activestate.com/wp-content/uploads/2021/12/python-coding-mistakes.jpg', 'Lets learn Python', 'Prof. Afeefa'),
(4, 'Intro to Microcontroller', 'The 8086[3] (also called iAPX 86)[4] is a 16-bit microprocessor chip designed by Intel between early 1976 and June 8, 1978, when it was released. The Intel 8088, released July 1, 1979,[5] is a slightly modified chip with an external 8-bit data bus (allowing the use of cheaper and fewer supporting ICs),[note 1] and is notable as the processor used in the original IBM PC design.\n\nThe 8086 gave rise to the x86 architecture, which eventually became Intel\'s most successful line of processors. On June 5, 2018, Intel released a limited-edition CPU celebrating the 40th anniversary of the Intel 8086, called the Intel Core i7-8086K.[5]\nIn 1972, Intel launched the 8008, Intel\'s first 8-bit microprocessor.[note 2] It implemented an instruction set designed by Datapoint Corporation with programmable CRT terminals in mind, which also proved to be fairly general-purpose. The device needed several additional ICs to produce a functional computer, in part due to it being packaged in a small 18-pin \"memory package\", which ruled out the use of a separate address bus (Intel was primarily a DRAM manufacturer at the time).\n\nTwo years later, Intel launched the 8080,[note 3] employing the new 40-pin DIL packages originally developed for calculator ICs to enable a separate address bus. It has an extended instruction set that is source-compatible (not binary compatible) with the 8008[6] and also includes some 16-bit instructions to make programming easier. The 8080 device was eventually replaced by the depletion-load-based 8085 (1977), which sufficed with a single +5 V power supply instead of the three different operating voltages of earlier chips.[note 4] Other well known 8-bit microprocessors that emerged during these years are Motorola 6800 (1974), General Instrument PIC16X (1975), MOS Technology 6502 (1975), Zilog Z80 (1976), and Motorola 6809 (1978).\nThe 8086 project started in May 1976 and was originally intended as a temporary substitute for the ambitious and delayed iAPX 432 project. It was an attempt to draw attention from the less-delayed 16-bit and 32-bit processors of other manufacturers — Motorola, Zilog, and National Semiconductor.\n\nWhereas the 8086 was a 16-bit microprocessor, it used the same microarchitecture as Intel\'s 8-bit microprocessors (8008, 8080, and 8085). This allowed assembly language programs written in 8-bit to seamlessly migrate.[7] New instructions and features — such as signed integers, base+offset addressing, and self-repeating operations — were added. Instructions were added to assist source code compilation of nested functions in the ALGOL-family of languages, including Pascal and PL/M. According to principal architect Stephen P. Morse, this was a result of a more software-centric approach. Other enhancements included microcode instructions for the multiply and divide assembly language instructions. Designers also anticipated coprocessors, such as 8087 and 8089, so the bus structure was designed to be flexible.\n\nThe first revision of the instruction set and high level architecture was ready after about three months,[note 5] and as almost no CAD tools were used, four engineers and 12 layout people were simultaneously working on the chip.[note 6] The 8086 took a little more than two years from idea to working product, which was considered rather fast for a complex design in 1976–1978.\n\nThe 8086 was sequenced[note 7] using a mixture of random logic[8] and microcode and was implemented using depletion-load nMOS circuitry with approximately 20,000 active transistors (29,000 counting all ROM and PLA sites). It was soon moved to a new refined nMOS manufacturing process called HMOS (for High performance MOS) that Intel originally developed for manufacturing of fast static RAM products.[note 8] This was followed by HMOS-II, HMOS-III versions, and, eventually, a fully static CMOS version for battery powered devices, manufactured using Intel\'s CHMOS processes.[note 9] The original chip measured 33 mm² and minimum feature size was 3.2 μm.\n\nThe architecture was defined by Stephen P. Morse with some help from Bruce Ravenel (the architect of the 8087) in refining the final revisions. Logic designer Jim McKevitt and John Bayliss were the lead engineers of the hardware-level development team[note 10] and Bill Pohlman the manager for the project. The legacy of the 8086 is enduring in the basic instruction set of today\'s personal computers and servers; the 8086 also lent its last two digits to later extended versions of the design, such as the Intel 286 and the Intel 386, all of which eventually became known as the x86 family. (Another reference is that the PCI Vendor ID for Intel devices is 8086h.)\n', 'https://upload.wikimedia.org/wikipedia/commons/thumb/7/7b/Siemens_SAB8085A_C_1.jpg/326px-Siemens_SAB8085A_C_1.jpg', 'How microcontrollers work', 'Dr. Shameem'),
(5, 'Computer Networks', 'A computer network is a set of computers sharing resources located on or provided by network nodes. Computers use common communication protocols over digital interconnections to communicate with each other. These interconnections are made up of telecommunication network technologies based on physically wired, optical, and wireless radio-frequency methods that may be arranged in a variety of network topologies.\n\nThe nodes of a computer network can include personal computers, servers, networking hardware, or other specialized or general-purpose hosts. They are identified by network addresses and may have hostnames. Hostnames serve as memorable labels for the nodes and are rarely changed after initial assignment. Network addresses serve for locating and identifying the nodes by communication protocols such as the Internet Protocol.\n\nComputer networks may be classified by many criteria, including the transmission medium used to carry signals, bandwidth, communications protocols to organize network traffic, the network size, the topology, traffic control mechanisms, and organizational intent.[citation needed]\n\nComputer networks support many applications and services, such as access to the World Wide Web, digital video and audio, shared use of application and storage servers, printers and fax machines, and use of email and instant messaging applications.\nComputer networking may be considered a branch of computer science, computer engineering, and telecommunications, since it relies on the theoretical and practical application of the related disciplines. Computer networking was influenced by a wide array of technology developments and historical milestones.\n\n    In the late 1950s, a network of computers was built for the U.S. military Semi-Automatic Ground Environment (SAGE) radar system[1][2][3] using the Bell 101 modem. It was the first commercial modem for computers, released by AT&T Corporation in 1958. The modem allowed digital data to be transmitted over regular unconditioned telephone lines at a speed of 110 bits per second (bit/s).\n    In 1959, Christopher Strachey filed a patent application for time-sharing and John McCarthy initiated the first project to implement time-sharing of user programs at MIT.[4][5][6][7] Stratchey passed the concept on to J. C. R. Licklider at the inaugural UNESCO Information Processing Conference in Paris that year.[8] McCarthy was instrumental in the creation of three of the earliest time-sharing systems (the Compatible Time-Sharing System in 1961, the BBN Time-Sharing System in 1962, and the Dartmouth Time Sharing System in 1963).\n    In 1959, Anatoly Kitov proposed to the Central Committee of the Communist Party of the Soviet Union a detailed plan for the re-organisation of the control of the Soviet armed forces and of the Soviet economy on the basis of a network of computing centres.[9] Kitov\'s proposal was rejected, as later was the 1962 OGAS economy management network project.[10]\n    In 1960, the commercial airline reservation system semi-automatic business research environment (SABRE) went online with two connected mainframes.\n    In 1963, J. C. R. Licklider sent a memorandum to office colleagues discussing the concept of the \"Intergalactic Computer Network\", a computer network intended to allow general communications among computer users.\n    Throughout the 1960s, Paul Baran and Donald Davies independently developed the concept of packet switching to transfer information between computers over a network.[11][12][13] Davies pioneered the implementation of the concept. The NPL network, a local area network at the National Physical Laboratory (United Kingdom) used a line speed of 768 kbit/s and later high-speed T1 links (1.544 Mbit/s line rate).[14][15][16]\n    In 1965, Western Electric introduced the first widely used telephone switch that implemented computer control in the switching fabric.\n    In 1969, the first four nodes of the ARPANET were connected using 50 kbit/s circuits between the University of California at Los Angeles, the Stanford Research Institute, the University of California at Santa Barbara, and the University of Utah.[17] In the early 1970s, Leonard Kleinrock carried out mathematical work to model the performance of packet-switched networks, which underpinned the development of the ARPANET.[18][19] His theoretical work on hierarchical routing in the late 1970s with student Farouk Kamoun remains critical to the operation of the Internet today.\n    In 1972, commercial services were first deployed on public data networks in Europe,[20][21][22] which began using X.25 in the late 1970s and spread across the globe.[14] The underlying infrastructure was used for expanding TCP/IP networks in the 1980s.[23]\n    In 1973, the French CYCLADES network, directed by Louis Pouzin was the first to make the hosts responsible for the reliable delivery of data, rather than this being a centralized service of the network itself.[24]\n    In 1973, Peter Kirstein put internetworking into practice at University College London (UCL), connecting the ARPANET to British academic networks, the first international heterogeneous computer network.[25][26]\n    In 1973, Robert Metcalfe wrote a formal memo at Xerox PARC describing Ethernet, a networking system that was based on the Aloha network, developed in the 1960s by Norman Abramson and colleagues at the University of Hawaii. In July 1976, Robert Metcalfe and David Boggs published their paper \"Ethernet: Distributed Packet Switching for Local Computer Networks\"[27] and collaborated on several patents received in 1977 and 1978.\n    In 1974, Vint Cerf, Yogen Dalal, and Carl Sunshine published the Transmission Control Protocol (TCP) specification, RFC 675, coining the term Internet as a shorthand for internetworking.[28]\n    In 1976, John Murphy of Datapoint Corporation created ARCNET, a token-passing network first used to share storage devices.\n    In 1977, the first long-distance fiber network was deployed by GTE in Long Beach, California.\n    In 1977, Xerox Network Systems (XNS) was developed by Robert Metcalfe and Yogen Dalal at Xerox.[29]\n    In 1979, Robert Metcalfe pursued making Ethernet an open standard.[30]\n    In 1980, Ethernet was upgraded from the original 2.94 Mbit/s protocol to the 10 Mbit/s protocol, which was developed by Ron Crane, Bob Garner, Roy Ogus,[31] and Yogen Dalal.[32]\n    In 1995, the transmission speed capacity for Ethernet increased from 10 Mbit/s to 100 Mbit/s. By 1998, Ethernet supported transmission speeds of 1 Gbit/s. Subsequently, higher speeds of up to 400 Gbit/s were added (as of 2018). The scaling of Ethernet has been a contributing factor to its continued use.[30]\n\n\n', 'https://yzrkillerhome.files.wordpress.com/2019/06/network_95-2.png', 'Be a Network Expert', 'Dr. Salman'),
(6, 'Introduction to Data Structure', 'In computer science, a data structure is a data organization, management, and storage format that is usually chosen for efficient access to data.[1][2][3] More precisely, a data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data,[4] i.e., it is an algebraic structure about data.\nData structures serve as the basis for abstract data types (ADT). The ADT defines the logical form of the data type. The data structure implements the physical form of the data type.[5]\n\nDifferent types of data structures are suited to different kinds of applications, and some are highly specialized to specific tasks. For example, relational databases commonly use B-tree indexes for data retrieval,[6] while compiler implementations usually use hash tables to look up identifiers.[7]\n\nData structures provide a means to manage large amounts of data efficiently for uses such as large databases and internet indexing services. Usually, efficient data structures are key to designing efficient algorithms. Some formal design methods and programming languages emphasize data structures, rather than algorithms, as the key organizing factor in software design. Data structures can be used to organize the storage and retrieval of information stored in both main memory and secondary memory.[8]\nData structures are generally based on the ability of a computer to fetch and store data at any place in its memory, specified by a pointer—a bit string, representing a memory address, that can be itself stored in memory and manipulated by the program. Thus, the array and record data structures are based on computing the addresses of data items with arithmetic operations, while the linked data structures are based on storing addresses of data items within the structure itself.\n\nThe implementation of a data structure usually requires writing a set of procedures that create and manipulate instances of that structure. The efficiency of a data structure cannot be analyzed separately from those operations. This observation motivates the theoretical concept of an abstract data type, a data structure that is defined indirectly by the operations that may be performed on it, and the mathematical properties of those operations (including their space and time cost).[9]\n', 'https://khalilstemmler.com/img/blog/data-structures/hash-tables/hash-table.png', 'Improve your Problem solving s', 'Prof. Anwar'),
(7, 'Electronics and Mechtronics', 'Mechatronics engineering, also called mechatronics, is an interdisciplinary branch of engineering that focuses on the integration of mechanical engineering, electrical engineering, electronic engineering and software engineering,[1] and also includes a combination of robotics, computer science, telecommunications, systems, control, and product engineering.[2][3]\n\nAs technology advances over time, various subfields of engineering have succeeded in both adapting and multiplying. The intention of mechatronics is to produce a design solution that unifies each of these various subfields. Originally, the field of mechatronics was intended to be nothing more than a combination of mechanics, electrical and electronics, hence the name being a portmanteau of the words \"mechanics\" and \"electronics\"; however, as the complexity of technical systems continued to evolve, the definition had been broadened to include more technical areas.\n\nThe word mechatronics originated in Japanese-English and was created by Tetsuro Mori, an engineer of Yaskawa Electric Corporation. The word mechatronics was registered as trademark by the company in Japan with the registration number of \"46-32714\" in 1971. The company later released the right to use the word to the public, and the word began being used globally. Currently the word is translated into many languages and is considered an essential term for advanced automated industry.[4]\n\nMany people treat mechatronics as a modern buzzword synonymous with automation, robotics and electromechanical engineering.[5]\n\nFrench standard NF E 01-010 gives the following definition: \"approach aiming at the synergistic integration of mechanics, electronics, control theory, and computer science within product design and manufacturing, in order to improve and/or optimize its functionality\".[6]\nThe word mechatronics was registered as trademark by the company in Japan with the registration number of \"46-32714\" in 1971. The company later released the right to use the word to the public, and the word began being used globally.\n\nWith the advent of information technology in the 1980s, microprocessors were introduced into mechanical systems, improving performance significantly. By the 1990s, advances in computational intelligence were applied to mechatronics in ways that revolutionized the field.\nA mechatronics engineer unites the principles of mechanics, electrical, electronics, and computing to generate a simpler, more economical and reliable system.[7]\n\nEngineering cybernetics deals with the question of control engineering of mechatronic systems. It is used to control or regulate such a system (see control theory). Through collaboration, the mechatronic modules perform the production goals and inherit flexible and agile manufacturing properties in the production scheme. Modern production equipment consists of mechatronic modules that are integrated according to a control architecture. The most known architectures involve hierarchy, polyarchy, heterarchy, and hybrid. The methods for achieving a technical effect are described by control algorithms, which might or might not utilize formal methods in their design. Hybrid systems important to mechatronics include production systems, synergy drives, exploration rovers, automotive subsystems such as anti-lock braking systems and spin-assist, and everyday equipment such as autofocus cameras, video, hard disks, CD players and phones.\n', 'https://qph.cf2.quoracdn.net/main-qimg-1476d3aee8cb398dd5759932d25a552b', 'Know the differnce of Electron', 'Dr. Shameem'),
(8, 'Learn Neural Network', 'Deep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective \"deep\" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.[2]\n\nDeep-learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks, convolutional neural networks and transformers have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, climate science, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance.[3][4][5]\n\nArtificial neural networks (ANNs) were inspired by information processing and distributed communication nodes in biological systems. ANNs have various differences from biological brains. Specifically, artificial neural networks tend to be static and symbolic, while the biological brain of most living organisms is dynamic (plastic) and analog.[6][7]\nDeep learning is a class of machine learning algorithms that[8]: 199–200  uses multiple layers to progressively extract higher-level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces.\n\nFrom another angle to view deep learning, deep learning refers to \"computer-simulate\" or \"automate\" human learning processes from a source (e.g., an image of dogs) to a learned object (dogs). Therefore, a notion coined as \"deeper\" learning or \"deepest\" learning[9] makes sense. The deepest learning refers to the fully automatic learning from a source to a final learned object. A deeper learning thus refers to a mixed learning process: a human learning process from a source to a learned semi-object, followed by a computer learning process from the human learned semi-object to a final learned object.\nMost modern deep learning models are based on multi-layered artificial neural networks such as convolutional neural networks and transformers, although they can also include propositional formulas or latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep Boltzmann machines.[10]\n\nIn deep learning, each level learns to transform its input data into a slightly more abstract and composite representation. In an image recognition application, the raw input may be a matrix of pixels; the first representational layer may abstract the pixels and encode edges; the second layer may compose and encode arrangements of edges; the third layer may encode a nose and eyes; and the fourth layer may recognize that the image contains a face. Importantly, a deep learning process can learn which features to optimally place in which level on its own. This does not eliminate the need for hand-tuning; for example, varying numbers of layers and layer sizes can provide different degrees of abstraction.[11][12]\n\nThe word \"deep\" in \"deep learning\" refers to the number of layers through which the data is transformed. More precisely, deep learning systems have a substantial credit assignment path (CAP) depth. The CAP is the chain of transformations from input to output. CAPs describe potentially causal connections between input and output. For a feedforward neural network, the depth of the CAPs is that of the network and is the number of hidden layers plus one (as the output layer is also parameterized). For recurrent neural networks, in which a signal may propagate through a layer more than once, the CAP depth is potentially unlimited.[13] No universally agreed-upon threshold of depth divides shallow learning from deep learning, but most researchers agree that deep learning involves CAP depth higher than 2. CAP of depth 2 has been shown to be a universal approximator in the sense that it can emulate any function.[14] Beyond that, more layers do not add to the function approximator ability of the network. Deep models (CAP > 2) are able to extract better features than shallow models and hence, extra layers help in learning the features effectively.\n\nDeep learning architectures can be constructed with a greedy layer-by-layer method.[15] Deep learning helps to disentangle these abstractions and pick out which features improve performance.[11]\n\nFor supervised learning tasks, deep learning methods eliminate feature engineering, by translating the data into compact intermediate representations akin to principal components, and derive layered structures that remove redundancy in representation.\n\nDeep learning algorithms can be applied to unsupervised learning tasks. This is an important benefit because unlabeled data are more abundant than the labeled data. Examples of deep structures that can be trained in an unsupervised manner are deep belief networks.[11][16]\n', 'https://cdn.activestate.com/wp-content/uploads/2021/12/python-coding-mistakes.jpg', 'Make prosthetic brains', 'Prof. Afeefa'),
(9, 'Cyber security', 'Computer security, cyber security, digital security or information technology security (IT security) is the protection of computer systems and networks from attacks by malicious actors that may result in unauthorized information disclosure, theft of, or damage to hardware, software, or data, as well as from the disruption or misdirection of the services they provide.[1][2]\n\nThe field is significant due to the expanded reliance on computer systems, the Internet,[3] and wireless network standards such as Bluetooth and Wi-Fi. Also, due to the growth of smart devices, including smartphones, televisions, and the various devices that constitute the Internet of things (IoT). Cybersecurity is one of the most significant challenges of the contemporary world, due to both the complexity of information systems and the societies they support. Security is of especially high importance for systems that govern large-scale systems with far-reaching physical effects, such as power distribution, elections, and finance.[4][5]\nSince the Internet\'s arrival and with the digital transformation initiated in recent years, the notion of cybersecurity has become a familiar subject in both our professional and personal lives. Cybersecurity and cyber threats have been consistently present for the last 60 years of technological change. In the 1970s and 1980s, computer security was mainly limited to academia until the conception of the Internet, where, with increased connectivity, computer viruses and network intrusions began to take off. After the spread of viruses in the 1990s, the 2000s marked the institutionalization of organized attacks such as distributed denial of service.[6] This led to the formalization of cybersecurity as a professional discipline.[7]\n\nThe April 1967 session organized by Willis Ware at the Spring Joint Computer Conference, and the later publication of the Ware Report, were foundational moments in the history of the field of computer security.[8] Ware\'s work straddled the intersection of material, cultural, political, and social concerns.[8]\n\nA 1977 NIST publication[9] introduced the CIA triad of confidentiality, integrity, and availability as a clear and simple way to describe key security goals.[10] While still relevant, many more elaborate frameworks have since been proposed.[11][12]\n\nHowever, in the 1970s and 1980s, there were no grave computer threats because computers and the internet were still developing, and security threats were easily identifiable. More often, threats came from malicious insiders who gained unauthorized access to sensitive documents and files. Although malware and network breaches existed during the early years, they did not use them for financial gain. By the second half of the 1970s, established computer firms like IBM started offering commercial access control systems and computer security software products.[13]\n\nOne of the earliest examples of an attack on a computer network was the computer worm Creeper written by Bob Thomas at BBN, which propagated through the ARPANET in 1971. The program was purely experimental in nature and carried no malicious payload. A later program, Reaper, was created by Ray Tomlinson in 1972 and used to destroy Creeper.\n\nBetween September 1986 and June 1987, a group of German hackers performed the first documented case of cyber espionage. The group hacked into American defense contractors, universities, and military base networks and sold gathered information to the Soviet KGB. The group was led by Markus Hess, who was arrested on 29 June 1987. He was convicted of espionage (along with two co-conspirators) on 15 Feb 1990.\n\nIn 1988, one of the first computer worms, called the Morris worm, was distributed via the Internet. It gained significant mainstream media attention.\n\nIn 1993, Netscape started developing the protocol SSL, shortly after the National Center for Supercomputing Applications (NCSA) launched Mosaic 1.0, the first web browser, in 1993. Netscape had SSL version 1.0 ready in 1994, but it was never released to the public due to many serious security vulnerabilities. These weaknesses included replay attacks and a vulnerability that allowed hackers to alter unencrypted communications sent by users. However, in February 1995, Netscape launched Version 2.0.\n\nThe National Security Agency (NSA) is responsible for the protection of U.S. information systems and also for collecting foreign intelligence.[14]\n\nThe agency analyzes commonly used software and system configurations to find security flaws, which it can use for offensive purposes against competitors of the United States.[15]\n\nNSA contractors created and sold click-and-shoot attack tools to US agencies and close allies, but eventually, the tools made their way to foreign adversaries.[citation needed] In 2016, NSAs own hacking tools were hacked, and they have been used by Russia and North Korea.[citation needed] NSA\'s employees and contractors have been recruited at high salaries by adversaries, anxious to compete in cyberwarfare.[citation needed] In 2007, the United States and Israel began exploiting security flaws in the Microsoft Windows operating system to attack and damage equipment used in Iran to refine nuclear materials. Iran responded by heavily investing in their own cyberwarfare capability, which it began using against the United States.[15]\n', 'https://www.waftr.com/wp-content/uploads/2022/08/best-cyber-security-websites-696x392.jpg', 'Stay safe from cyber threats', 'Dr. Salman'),
(10, 'Fear of Mathematics', 'Mathematical anxiety, also known as math phobia, is a feeling of tension and anxiety that interferes with the manipulation of numbers and the solving of mathematical problems in daily life and academic situations.[1] This is, arguably, distinct from statistics anxiety where the negative state is the result of encountering statistics at any level but related to but distinct from mathematical anxiety.[2]\nMark H. defines math anxiety as \"a feeling of tension, apprehension, or fear that interferes with math performance\" (2002, p. 1).[3] It is a phenomenon that is often considered when examining students\' problems in mathematics. According to the American Psychological Association, mathematical anxiety is often linked to testing anxiety. This anxiety can cause distress and likely causes a dislike and avoidance of all math-related tasks. The academic study of math anxiety originates as early as the 1950s, when Mary Fides Gough introduced the term mathemaphobia to describe the phobia-like feelings of many towards mathematics.[4] The first math anxiety measurement scale was developed by Richardson and Suinn in 1972.[5] Since this development, several researchers have examined math anxiety in empirical studies.[3] Hembree[6] (1990) conducted a meta-analysis of 151 studies concerning math anxiety. The study determined that math anxiety is related to poor math performance on math achievement tests and to negative attitudes concerning math. Hembree also suggests that math anxiety is directly connected with math avoidance.\n\nAshcraft[3] (2002) suggests that highly anxious math students will avoid situations in which they have to perform mathematical tasks. Unfortunately, math avoidance results in less competency, exposure and math practice, leaving students more anxious and mathematically unprepared to achieve. In college and university, anxious math students take fewer math courses and tend to feel negative towards the subject. In fact, Ashcraft found that the correlation between math anxiety and variables such as self-confidence and motivation in math is strongly negative.\n\nAccording to Schar,[7] because math anxiety can cause math avoidance, an empirical dilemma arises. For instance, when a highly math-anxious student performs disappointingly on a math question, it could be due to math anxiety or the lack of competency in math because of math avoidance. Ashcraft determined that by administering a test that becomes increasingly more mathematically challenging, he noticed that even highly math-anxious individuals do well on the first portion of the test measuring performance. However, on the latter and more difficult portion of the test, there was a stronger negative relationship between accuracy and math anxiety.\n\nAccording to the research found at the University of Chicago by Sian Beilock and her group, math anxiety is not simply about being bad at math. After using brain scans, scholars confirmed that the anticipation or the thought of solving math actually causes math anxiety. The brain scans showed that the area of the brain that is triggered when someone has math anxiety overlaps the same area of the brain where bodily harm is registered.[8] And Trezise and Reeve[9][10] show that students\' math anxiety can fluctuate throughout the duration of a math class.\nPerformanc', 'https://ingoanews.com/wp-content/uploads/2018/04/maths.jpg', 'Do you afraid of Maths', 'Dr. Raghib');
INSERT INTO `blogs` (`Id`, `blogTitle`, `BlogContent`, `ImgURL`, `smallPara`, `Authors`) VALUES
(11, 'Road Map to AI ML Engineer', 'Machine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines \"discover\" their \"own\" algorithms,[1] without needing to be explicitly told what to do by any human-developed algorithms.[2] Recently, generative artificial neural networks have been able to surpass results of many previous approaches.[3][4] Machine-learning approaches have been applied to large language models, computer vision, speech recognition, email filtering, agriculture and medicine, where it is too costly to develop algorithms to perform the needed tasks.[5][6]\n\nThe mathematical foundations of ML are provided by mathematical optimization (mathematical programming) methods. Data mining is a related (parallel) field of study, focusing on exploratory data analysis through unsupervised learning.[8][9]\n\nML is known in its application across business problems under the name predictive analytics. Although not all machine learning is statistically based, computational statistics is an important source of the field\'s methods.\nThe term machine learning was coined in 1959 by Arthur Samuel, an IBM employee and pioneer in the field of computer gaming and artificial intelligence.[10][11] The synonym self-teaching computers was also used in this time period.[12][13]\n\nBy the early 1960s an experimental \"learning machine\" with punched tape memory, called Cybertron, had been developed by Raytheon Company to analyze sonar signals, electrocardiograms, and speech patterns using rudimentary reinforcement learning. It was repetitively \"trained\" by a human operator/teacher to recognize patterns and equipped with a \"goof\" button to cause it to re-evaluate incorrect decisions.[14] A representative book on research into machine learning during the 1960s was Nilsson\'s book on Learning Machines, dealing mostly with machine learning for pattern classification.[15] Interest related to pattern recognition continued into the 1970s, as described by Duda and Hart in 1973.[16] In 1981 a report was given on using teaching strategies so that a neural network learns to recognize 40 characters (26 letters, 10 digits, and 4 special symbols) from a computer terminal.[17]\n\nTom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: \"A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.\"[18] This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms. This follows Alan Turing\'s proposal in his paper \"Computing Machinery and Intelligence\", in which the question \"Can machines think?\" is replaced with the question \"Can machines do what we (as thinking entities) can do?\".[19]\n\nModern-day machine learning has two objectives, one is to classify data based on models which have been developed, the other purpose is to make predictions for future outcomes based on these models. A hypothetical algorithm specific to classifying data may use computer vision of moles coupled with supervised learning in order to train it to classify the cancerous moles. A machine learning algorithm for stock trading may inform the trader of future potential predictions.[', 'https://img.freepik.com/premium-photo/ai-machine-learning-modern-computer-technologies-concept-with-retro-effect-digital-micro-circuit-human-brain-silhouette-connected-motherboard-dark-background-3d-rendering_670147-10747.jpg', 'Become a AI/ML Engineer', 'Prof. Afeefa'),
(12, 'Introduction to Robotics', 'Robotics is an interdisciplinary field that involves the design, construction, operation, and use of robots.[1].\n\nRobotics integrates many fields that deal with specific aspects of robotics. For example, within mechanical engineering, the term robotics refers to the construction of the physical structures of a robots, while in computer science, robotics focuses on the study of robotic software.\n\nThere are also many other aspects of robotic development and releted fields that overleap in its, including electical, control, software, information, electronic, telecomunication, computer, mechatronic, materials and biomedical engineering. The goal of robotics is to design machines that can help and assist humans.\n\nThe field of robotics develops machines that can automate tasks and do various jobs that a human might not be able to do. Robots can be used in many situations for many purposes, but today many are used in dangerous environments (including inspection of radioactive materials, bomb detection and deactivation), manufacturing processes, or where humans cannot survive (e.g., in space, underwater, in high heat, and clean up and containment of hazardous materials and radiation). Robots can take any form, but some are made to resemble humans in appearance. This is claimed to help in the acceptance of robots in certain replicative behaviors which are usually performed by people. Such robots attempt to replicate walking, lifting, speech, cognition, or any other tasks mainly performed by a human. Many of today\'s robots are inspired by nature, contributing to the field of bio-inspired robotics.\n\nCertain robots require user input to operate, while other robots function autonomously. The concept of creating robots that can operate autonomously dates back to classical times, but research into the functionality and potential uses of robots did not grow substantially until the 20th century. Throughout history, it has been frequently assumed by various scholars, inventors, engineers, and technicians that robots will one day be able to mimic human behavior and manage tasks in a human-like fashion. Today, robotics is a rapidly growing field, as technological advances continue; researching, designing, and building new robots serve various practical purposes, whether domestically, commercially, or militarily. Many robots are built to do jobs that are hazardous to people, such as defusing bombs, finding survivors in unstable\n\nruins, and exploring mines and shipwrecks. Robotics is also used in STEM (science, technology, engineering, and mathematics) as a teaching aid.[2]\nThe word robotics was derived from the word robot, which was introduced to the public by Czech writer Karel Čapek in his play R.U.R. (Rossum\'s Universal Robots), which was published in 1920.[3] The word robot comes from the Slavic word robota, which means work/job. The play begins in a factory that makes artificial people called robots, creatures who can be mistaken for humans – very similar to the modern ideas of androids. Karel Čapek himself did not coin the word. He wrote a short letter in reference to an etymology in the Oxford English Dictionary in which he named his brother Josef Čapek as its actual originator.[3]\n\nAccording to the Oxford English Dictionary, the word robotics was first used in print by Isaac Asimov, in his science fiction short story \"Liar!\", published in May 1941 in Astounding Science Fiction. Asimov was unaware that he was coining the term; since the science and technology of electrical devices is electronics, he assumed robotics already referred to the science and technology of robots. In some of Asimov\'s other works, he states that the first use of the word robotics was in his short story Runaround (Astounding Science Fiction, March 1942),[4][5] where he introduced his concept of The Three Laws of Robotics. However, the original publication of \"Liar!\" predates that of \"Runaround\" by ten months, so the former is generally cited as the word\'s origin.\n', 'https://i.pinimg.com/736x/04/79/05/047905be0dc6c1f14e3a8aa41f76133f.jpg', 'Save your time by using Roboti', 'Dr. Shameem');

--
-- Indexes for dumped tables
--

--
-- Indexes for table `blogs`
--
ALTER TABLE `blogs`
  ADD PRIMARY KEY (`Id`);

--
-- AUTO_INCREMENT for dumped tables
--

--
-- AUTO_INCREMENT for table `blogs`
--
ALTER TABLE `blogs`
  MODIFY `Id` int(11) NOT NULL AUTO_INCREMENT, AUTO_INCREMENT=13;
COMMIT;

/*!40101 SET CHARACTER_SET_CLIENT=@OLD_CHARACTER_SET_CLIENT */;
/*!40101 SET CHARACTER_SET_RESULTS=@OLD_CHARACTER_SET_RESULTS */;
/*!40101 SET COLLATION_CONNECTION=@OLD_COLLATION_CONNECTION */;