BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Asia/Kolkata BEGIN:STANDARD DTSTART:19451014T230000 TZOFFSETFROM:+0630 TZOFFSETTO:+0530 TZNAME:IST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20240916T151931Z UID:15CDCB93-1343-4B32-BAE2-BA4805CA3C48 DTSTART;TZID=Asia/Kolkata:20240916T140000 DTEND;TZID=Asia/Kolkata:20240916T150000 DESCRIPTION:The IEEE Student Branch IIT Indore\, in cooperation with the IE EE CIS Chapter\, MP Section and IEEE CIS SB Chapter\, IIT Indore\, is plea sed to invite you to an Online Expert Talk on the Development of Large-sca le Machine Learning Algorithms and Their Applications in the Biomedical Do main. The talk will be delivered by Dr. Mudasir Ahmad Ganaie\, Assistant P rofessor in the Department of Computer Science and Engineering at IIT Ropa r.\n\nDetails of the event are as follows:\nDate: 16th Sept.\, 2024 (Monda y)\nTime: 02:00 PM IST\nVenue: Online (Link will be shared prior to the ev ent to the registered candidates)\n\nRegistration: The registration for th is event is free but mandatory to attend. Please register through the foll owing link: https://forms.gle/FNS5ph5vMWBATTERA by 11:00 AM\, 16th Sept. 2 024.\n\n[]\n\nSpeaker(s): Dr. Mudasir Ahmad Ganaie\, \n\nAgenda: \nIn this talk\, the speaker will present a novel large-scale machine learning algo rithm. Twin support vector machines (TSVMs) have been widely used for bina ry classification problems\, but they face challenges in handling large-sc ale datasets due to the need for computing large matrix inverses and overf itting issues arising from empirical risk minimization. To address these l imitations\, a novel fuzzy least squares TSVM for class imbalance learning (LS-FLSTSVM-CIL) is proposed. The LS-FLSTSVM-CIL addresses these challeng es by eliminating matrix inversions and incorporating structural risk mini mization to avoid overfitting. Additionally\, fuzzy weights are employed t o effectively handle class imbalance\, ensuring a more balanced classifica tion. The model is optimized using a sequential minimization approach\, ma king it computationally feasible for large-scale problems. Moreover\, the speaker will also demonstrate the practical applications of this algorithm in the biomedical domain\, including the diagnosis of Alzheimer’s disea se and breast cancer.\n\nVirtual: https://events.vtools.ieee.org/m/434338 LOCATION:Virtual: https://events.vtools.ieee.org/m/434338 ORGANIZER:phd2101241003@iiti.ac.in SEQUENCE:27 SUMMARY:Development of Large-scale Machine Learning Algorithms and Their Ap plications in the Biomedical Domain - IEEE SB IIT Indore URL;VALUE=URI:https://events.vtools.ieee.org/m/434338 X-ALT-DESC:Description: <br /><p><span style="font-family: georgia\, serif\ ;">The&nbsp\;<strong>IEEE Student Branch IIT Indore\,&nbsp\;</strong>in co operation with</span>&nbsp\;the&nbsp\;<strong>IEEE CIS Chapter\, MP Sectio n</strong>&nbsp\;and&nbsp\;<strong>IEEE CIS SB Chapter\, IIT Indore</stron g>\,&nbsp\;<span style="font-family: georgia\, serif\;">is pleased to invi te you to an&nbsp\;<strong>Online Expert Talk</strong>&nbsp\;on the&nbsp\; <strong>Development of Large-scale Machine Learning Algorithms and Their A pplications in the Biomedical Domain</strong>. The talk will be delivered by&nbsp\;<strong>Dr. Mudasir Ahmad Ganaie</strong>\, Assistant Professor i n the Department of Computer Science and Engineering at IIT Ropar.<br><br> <strong>Details of the event are as follows:</strong><br><strong>Date:</st rong>&nbsp\;16th Sept.\, 2024 (Monday)<br><strong>Time:&nbsp\;</strong>02: 00 PM IST<br><strong>Venue:</strong> Online (Link will be shared prior to the event to the registered candidates)<br><br><strong>Registration:</stro ng>&nbsp\;The registration for this event is free but mandatory to attend. Please register through the following link:&nbsp\;<a href="https://forms. gle/FNS5ph5vMWBATTERA" target="_blank" rel="noopener" data-saferedirecturl ="https://www.google.com/url?q=https://forms.gle/FNS5ph5vMWBATTERA&amp\;so urce=gmail&amp\;ust=1726415648505000&amp\;usg=AOvVaw0a4MwisjnJTUpmP7OfEl4o ">https://forms.gle/<wbr>FNS5ph5vMWBATTERA</a>&nbsp\;<span style="color: # ff0000\;">by 11:00 AM\, 16th Sept. 2024</span>.<br><br><img src="https://e vents.vtools.ieee.org/vtools_ui/media/display/37be77e7-7a90-4b9b-9633-703c 8a1106ae" alt="" width="1000" height="1414"><br></span></p><br /><br />Age nda: <br /><p>In this talk\, the speaker will present a novel large-scale machine learning algorithm. Twin support vector machines (TSVMs) have been widely used for binary classification problems\, but they face challenges in handling large-scale datasets due to the need for computing large matr ix inverses and overfitting issues arising from empirical risk minimizatio n. To address these limitations\, a novel fuzzy least squares TSVM for cla ss imbalance learning (LS-FLSTSVM-CIL) is proposed. The LS-FLSTSVM-CIL add resses these challenges by eliminating matrix inversions and incorporating structural risk minimization to avoid overfitting. Additionally\, fuzzy w eights are employed to effectively handle class imbalance\, ensuring a mor e balanced classification. The model is optimized using a sequential minim ization approach\, making it computationally feasible for large-scale prob lems. Moreover\, the speaker will also demonstrate the practical applicati ons of this algorithm in the biomedical domain\, including the diagnosis o f Alzheimer&rsquo\;s disease and breast cancer.</p> END:VEVENT END:VCALENDAR